research methods in psychology experiment

Experimental Design: Types, Examples & Methods

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Ch 2: Psychological Research Methods

Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.

The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.

Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.

You can view the transcript for “Understanding driver distraction” here (opens in new window) .

How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.

Introduction to the Scientific Method

Learning objectives.

• Explain the steps of the scientific method
• Describe why the scientific method is important to psychology
• Summarize the processes of informed consent and debriefing
• Explain how research involving humans or animals is regulated

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.

The Scientific Process

The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

Process of Scientific Research

Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.

The basic steps in the scientific method are:

• Observe a natural phenomenon and define a question about it
• Make a hypothesis, or potential solution to the question
• Test the hypothesis
• If the hypothesis is true, find more evidence or find counter-evidence
• If the hypothesis is false, create a new hypothesis or try again
• Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect

In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.

Basic Principles of the Scientific Method

Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.

Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.

Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.

Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.

To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.

Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.

Applying the Scientific Method

To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 5). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Link to Learning

Why the scientific method is important for psychology.

The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.

The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.

Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.

Research Involving Human Participants

Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 6). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.

An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent  form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, the informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.

While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing  upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.

Dig Deeper: Ethics and the Tuskegee Syphilis Study

Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 7). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.

While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?

Learn more about the Tuskegee Syphilis Study on the CDC website .

Research Involving Animal Subjects

This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.

Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.

Introduction to Approaches to Research

• Differentiate between descriptive, correlational, and experimental research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
• Describe the strength and weaknesses of archival research
• Compare longitudinal and cross-sectional approaches to research
• Explain what a correlation coefficient tells us about the relationship between variables
• Describe why correlation does not mean causation
• Describe the experimental process, including ways to control for bias
• Identify and differentiate between independent and dependent variables

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

Watch It: More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark  and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize  the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

Archival research.

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research  is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition  rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

Correlational Research

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Watch this clip from Freakonomics for an example of how correlation does  not  indicate causation.

You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?

Experiments

Causality: conducting experiments and using the data, experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group  gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants  are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample   (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Introduction to Statistical Thinking

Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.

• Define reliability and validity
• Describe the importance of distributional thinking and the role of p-values in statistical inference
• Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
• Describe the basic structure of a psychological research article

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Dig deeper:  everyday connection: how valid is the sat.

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).

Statistical Significance

Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.

Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:

• Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
• Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
• Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
• Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)

Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.

Distributional Thinking

When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.

Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.

• Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
• Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.

Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.

Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.

Finding Significance in Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.

Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.

The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?

Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.

Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.

If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .

So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.

If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.

Drawing Conclusions from Statistics

Generalizability.

One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.

Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample  that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.

In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.

The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.

Cause and Effect

In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?

Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.

In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?

Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)

The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.

We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment  tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.

But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?

We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.

Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.

Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.

Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.

So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:

• This was a 14-year study conducted by researchers at the National Cancer Institute.
• The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
• The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
• About 52,000 people died during the course of the study.
• People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
• The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
• Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
• This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.

This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.

Explore these outside resources to learn more about applied statistics:

• Video about p-values:  P-Value Extravaganza
• Interactive web applets for teaching and learning statistics
• Inter-university Consortium for Political and Social Research  where you can find and analyze data.
• The Consortium for the Advancement of Undergraduate Statistics
• Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
• Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.

How to Read Research

In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.

• Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
• Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
• Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and  materials  as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
• Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
• Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.

Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.

https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/

Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.

In this chapter, you learned to

• define and apply the scientific method to psychology
• describe the strengths and weaknesses of descriptive, experimental, and correlational research
• define the basic elements of a statistical investigation

Putting It Together: Psychological Research

Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.

Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.

You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).

Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.

• Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read   Imagining Ted Bundy  (http://goo.gl/rGqcUv) for more information on this case study.

Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.

• Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.

Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.

• Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
• Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.

Longitudinal Study: researchers   recruit a sample of participants and track them for an extended period of time.

• In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.

Cross-Sectional Study:  researchers gather participants from different groups (commonly different ages) and look for differences between the groups.

• In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.

Correlational Design:  two different variables are measured to determine whether there is a relationship between them.

• Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
• Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.

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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

well-developed set of ideas that propose an explanation for observed phenomena

(plural: hypotheses) tentative and testable statement about the relationship between two or more variables

an experiment must be replicable by another researcher

implies that a theory should enable us to make predictions about future events

able to be disproven by experimental results

implies that all data must be considered when evaluating a hypothesis

committee of administrators, scientists, and community members that reviews proposals for research involving human participants

process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate

purposely misleading experiment participants in order to maintain the integrity of the experiment

when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion

committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals

research studies that do not test specific relationships between variables

research investigating the relationship between two or more variables

research method that uses hypothesis testing to make inferences about how one variable impacts and causes another

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

observational research study focusing on one or a few people

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

subset of individuals selected from the larger population

overall group of individuals that the researchers are interested in

method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships

studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time

compares multiple segments of a population at a single time

reduction in number of research participants as some drop out of the study over time

relationship between two or more variables; when two variables are correlated, one variable changes as the other does

number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r

two variables change in the same direction, both becoming either larger or smaller

two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation

changes in one variable cause the changes in the other variable; can be determined only through an experimental research design

unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables

seeing relationships between two things when in reality no such relationship exists

tendency to ignore evidence that disproves ideas or beliefs

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

subset of a larger population in which every member of the population has an equal chance of being selected

method of experimental group assignment in which all participants have an equal chance of being assigned to either group

consistency and reproducibility of a given result

accuracy of a given result in measuring what it is designed to measure

determines how likely any difference between experimental groups is due to chance

statistical probability that represents the likelihood that experimental results happened by chance

Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!

Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.

To get us started:

• The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
• Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
• While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
• The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Methods in Psychology - 4th American Edition

(40 reviews)

Carrie Cuttler, Washington State University

Rajiv S. Jhangiani, Kwantlen Polytechnic University

Dana C. Leighton, Texas A&M University, Texarkana

Copyright Year: 2019

ISBN 13: 9781999198107

Publisher: Kwantlen Polytechnic University

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Beth Mechlin, Associate Professor of Psychology & Neuroscience, Earlham College on 3/19/24

This is an extremely comprehensive text for an undergraduate psychology course about research methods. It does an excellent job covering the basics of a variety of types of research design. It also includes important topics related to research... read more

Comprehensiveness rating: 5 see less

This is an extremely comprehensive text for an undergraduate psychology course about research methods. It does an excellent job covering the basics of a variety of types of research design. It also includes important topics related to research such as ethics, finding journal articles, and writing reports in APA format.

Content Accuracy rating: 5

I did not notice any errors in this text.

Relevance/Longevity rating: 5

The content is very relevant. It will likely need to be updated over time in order to keep research examples relevant. Additionally, APA formatting guidelines may need to be updated when a new publication manual is released. However, these should be easy updates for the authors to make when the time comes.

Clarity rating: 5

This text is very clear and easy to follow. The explanations are easy for college students to understand. The authors use a lot of examples to help illustrate specific concepts. They also incorporate a variety of relevant outside sources (such as videos) to provide additional examples.

Consistency rating: 5

The text is consistent and flows well from one section to the next. At the end of each large section (similar to a chapter) the authors provide key takeaways and exercises.

Modularity rating: 5

This text is very modular. It is easy to pick and choose which sections you want to use in your course when. Each section can stand alone fairly easily.

Organization/Structure/Flow rating: 5

The text is very well organized. Information flows smoothly from one topic to the next.

Interface rating: 5

The interface is great. The text is easy to navigate and the images display well (I only noticed 1 image in which the formatting was a tad off).

Grammatical Errors rating: 5

I did not notice any grammatical errors.

Cultural Relevance rating: 5

The text is culturally relevant.

This is an excellent text for an undergraduate research methods course in the field of Psychology. I have been using the text for my Research Methods and Statistics course for a few years now. This text focuses on research methods, so I do use another text to cover statistical information. I do highly recommend this text for research methods. It is comprehensive, clear, and easy for students to use.

Reviewed by William Johnson, Lecturer, Old Dominion University on 1/12/24

This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover). read more

This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover).

I have not noticed any inaccurate information (other than directed students to read Malcolm Gladwell). I appreciate that the textbook includes information on research errors that have not been supported by replication efforts, such as embodied cognition.

Many of the basic concepts of research methods are rather timeless, but I appreciate that the text includes newer research as examples while also including "classic" studies that exemplify different methods.

The writing is clear and simple. The keywords are bolded and reveal a definition when clicked, which students often find very helpful. Many of the figures are very helpful in helping students understand various methods (I really like the ones in the single-subject design subchapter).

The book is very consistent in its terminology and writing style, which I see as a positive compared to other open psychology textbooks where each chapter is written by subject matter experts (such as the NOBA intro textbook).

Modularity rating: 4

I teach this textbook almost entirely in order (except for moving chapters 12 & 13 earlier in the semester to aid students in writing Results sections in their final papers). I think that the organization and consistency of the book reduces its modularity, in that earlier chapters are genuinely helpful for later chapters.

Organization/Structure/Flow rating: 4

I preferred the organization of previous editions, which had "Theory in Research" as its own chapter. If I were organizing the textbook, I am not sure that I would have out descriptive or inferential statistics as the final two chapters (I would have likely put Chapter 11: Presenting Your Research as the final chapter). I also would not have put information about replicability and open science in the inferential statistics section.

The text is easy to read and the formatting is attractive. My only minor complaint is that some of the longer subchapters can be a pretty long scroll, but I understand the desire for their only to be one page per subchapter/topic.

I have not noticed any grammatical errors.

Cultural Relevance rating: 3

I do not think the textbook is insensitive, but there is not much thought given to adapting research instruments across cultures. For instance, talking about how different constructs might have different underlying distributions in different cultures would be useful for students. In the survey methods section, a discussion of back translation or emic personality trait measurement/development for example might be a nice addition.

I choose to use this textbook in my methods classes, but I do miss the organization of the previous American editions. Overall, I recommend this textbook to my colleagues.

Reviewed by Brianna Ewert, Psychology Instructor, Salish Kootenai College on 12/30/22

This text includes the majority of content included in our undergraduate Research Methods in Psychology course. The glossary provides concise definitions of key terms. This text includes most of the background knowledge we expect our students to... read more

Comprehensiveness rating: 4 see less

This text includes the majority of content included in our undergraduate Research Methods in Psychology course. The glossary provides concise definitions of key terms. This text includes most of the background knowledge we expect our students to have as well as skill-based sections that will support them in developing their own research projects.

The content I have read is accurate and error-free.

The content is relevant and up-to-date.

The text is clear and concise. I find it pleasantly readable and anticipate undergraduate students will find it readable and understandable as well.

The terminology appears to be consistent throughout the text.

The modular sections stand alone and lend themselves to alignment with the syllabus of a particular course. I anticipate readily selecting relevant modules to assign in my course.

The book is logically organized with clear and section headings and subheadings. Content on a particular topic is easy to locate.

The text is easy to navigate and the format/design are clean and clear. There are not interface issues, distortions or distracting format in the pdf or online versions.

The text is grammatically correct.

Cultural Relevance rating: 4

I have not found culturally insensitive and offensive language or content in the text. For my courses, I would add examples and supplemental materials that are relevant for students at a Tribal College.

This textbook includes supplemental instructor materials, included slides and worksheets. I plan to adopt this text this year in our Research Methods in Psychology course. I expect it to be a benefit to the course and students.

Reviewed by Sara Peters, Associate Professor of Psychology, Newberry College on 11/3/22

This text serves as an excellent resource for introducing survey research methods topics to undergraduate students. It begins with a background of the science of psychology, the scientific method, and research ethics, before moving into the main... read more

This text serves as an excellent resource for introducing survey research methods topics to undergraduate students. It begins with a background of the science of psychology, the scientific method, and research ethics, before moving into the main types of research. This text covers experimental, non-experimental, survey, and quasi-experimental approaches, among others. It extends to factorial and single subject research, and within each topic is a subset (such as observational research, field studies, etc.) depending on the section.

I could find no accuracy issues with the text, and appreciated the discussions of research and cited studies.

There are revised editions of this textbook (this being the 4th), and the examples are up to date and clear. The inclusion of exercises at the end of each chapter offer potential for students to continue working with material in meaningful ways as they move through the book and (and course).

The prose for this text is well aimed at the undergraduate population. This book can easily be utilized for freshman/sophomore level students. It introduces the scientific terminology surrounding research methods and experimental design in a clear way, and the authors provide extensive examples of different studies and applications.

Terminology is consistent throughout the text. Aligns well with other research methods and statistics sources, so the vocabulary is transferrable beyond the text itself.

Navigating this book is a breeze. There are 13 chapters, and each have subsections that can be assigned. Within each chapter subsection, there is a set of learning objectives, and paragraphs are mixed in with tables and figures for students to have different visuals. Different application assignments within each chapter are highlighted with boxes, so students can think more deeply given a set of constructs as they consider different information. The last subsection in each chapter has key summaries and exercises.

The sections and topics in this text are very straightforward. The authors begin with an introduction of psychology as a science, and move into the scientific method, research ethics, and psychological measurement. They then present multiple different research methodologies that are well known and heavily utilized within the social sciences, before concluding with information on how to present your research, and also analyze your data. The text even provides links throughout to other free resources for a reader.

This book can be navigated either online (using a drop-down menu), or as a pdf download, so students can have an electronic copy if needed. All pictures and text display properly on screen, with no distortions. Very easy to use.

There were no grammatical errors, and nothing distracting within the text.

This book includes inclusive material in the discussion of research ethics, as well as when giving examples of the different types of research approaches. While there is always room for improvement in terms of examples, I was satisfied with the breadth of research the authors presented.

This text provides an overview of both research methods, and a nice introduction to statistics for a social science student. It would be a good choice for a survey research methods class, and if looking to change a statistics class into an open resource class, could also serve as a great resource.

Reviewed by Sharlene Fedorowicz, Adjunct Professor, Bridgewater State University on 6/23/21

The comprehensiveness of this book was appropriate for an introductory undergraduate psychology course. Critical topics are covered that are necessary for psychology students to obtain foundational learning concepts for research. Sections within... read more

The comprehensiveness of this book was appropriate for an introductory undergraduate psychology course. Critical topics are covered that are necessary for psychology students to obtain foundational learning concepts for research. Sections within the text and each chapter provide areas for class discussion with students to dive deeper into key concepts for better learning comprehension. The text covered APA format along with examples of research studies to supplement the learning. The text segues appropriately by introducing the science of psychology, followed by scientific method and ethics before getting into the core of scientific research in the field of psychology. Details are provided in quantitative and qualitative research, correlations, surveys, and research design. Overall, the text is fully comprehensive and necessary introductory research concepts.

The text appears to be accurate with no issues related to content.

Relevance/Longevity rating: 4

The text provided relevant research information to support the learning. The content was up-to-date with a variety of different examples related to the different fields of psychology. However, some topics such as in the pseudoscience section were not very relevant and bordered the line of beliefs. Here, more current or relevant solid examples would provide more relevancy in this part of the text. Bringing in more solid or concrete examples that are more current for students may have been more appropriate such as lack of connection between information found on social media versus real science.

The language and flow of the chapters accompanied by the terms, concepts, and examples of applied research allows for clarity of learning content. Terms were introduced at the appropriate time with the support of concepts and current or classic research. The writing style flows nicely and segues easily from concept to concept. The text is easy for students to understand and grasp the details related to psychological research and science.

The text provides consistency in the outline of each chapter. The beginning section chapter starts objectives as an overview to help students unpack the learning content. Key terms are consistently bolded followed by concept or definition and relevant examples. Research examples are pertinent and provide students with an opportunity to understand application of the contents. Practice exercises are provided with in the chapter and at the and in order for students to integrate learning concepts from within the text.

Sections and subsections are clearly organized and divided appropriately for ease-of-use. The topics are easily discernible and follow the flow of ideal learning routines for students. The sections and subsections are consistently outlined for each concept module. The modularity provides consistency allowing for students to focus on content rather than trying to discern how to pull out the information differently from each chapter or section. In addition, each section and subsection allow for flexibility in learning or expanding concepts within the content area.

The organization of the textbook was easy to follow and each major topic was outlined clearly. However, the chapter on presenting research may be more appropriately placed toward the end of the book rather than in the middle of the chapters related to research and research design. In addition, more information could have been provided upfront around APA format so that students could identify the format of citations within the text as practice for students throughout the book.

The interface of the book lends itself to a nice layout with appropriate examples and links to break up the different sections in the chapters. Examples where appropriate and provided engagement opportunities for the students for each learning module. Images and QR codes or easily viewed and used. Key terms are highlighted in relevant figures, graphs, and tables were appropriately placed. Overall, the interface of the text assisted with the organization and flow of learning material.

No grammatical errors were detected in this book.

The text appears to be culturally sensitive and not offensive. A variety of current and classic research examples are relevant. However, more examples of research from women, minorities, and ethnicities would strengthen the culture of this textbook. Instructors may need to supplement some research in this area to provide additional inclusivity.

Overall, I was impressed by the layout of the textbook and the ease of use. The layout provides a set of expectations for students related to the routine of how the book is laid out and how students will be able to unpack the information. Research examples were relevant, although I see areas where I will supplement information. The book provides opportunities for students to dive deeper into the learning and have rich conversations in the classroom. I plan to start using the psychology textbook for my students starting next year.

Reviewed by Anna Behler, Assistant Professo, North Carolina State University on 6/1/21

The text is very thorough and covers all of the necessary topics for an undergraduate psychology research methods course. There is even coverage of qualitative research, case studies, and the replication crisis which I have not seen in some other... read more

The text is very thorough and covers all of the necessary topics for an undergraduate psychology research methods course. There is even coverage of qualitative research, case studies, and the replication crisis which I have not seen in some other texts.

There were no issues with the accuracy of the text.

The content is very up to date and relevant for a research methods course. The only updates that will likely be necessary in the coming years are updates to examples and modifications to the section on APA formatting.

The clarity of the writing was good, and the chapters were written in a way that was accessible and easy to follow.

I did not note any issues with consistency.

Each chapter is divided into multiple subsections. This makes the chapters even easier to read, as they are broken down into short and easy to navigate sections. These sections make it easy to assign readings as needed depending on which topics are being covered in class.

Organization/Structure/Flow rating: 3

The organization was one of the few areas of weakness, and I felt that the chapters were ordered somewhat oddly. However, this is something that is easily fixed, as chapters (and even subsections) can be assigned in whatever order is needed.

There were no issues of note with the interface, and the PDF of the text was easy to navigate.

The text was well written and there were no grammatical/writing errors of note.

Overall, the book did not contain any notable instances of bias. However, it would probably be appropriate to offer a more thorough discussion of the WEIRD problem in psychology research.

Reviewed by Seth Surgan, Professor, Worcester State University on 5/24/21

Pitched very well for a 200-level Research Methods course. This text provided students with solid basis for class discussion and the further development of their understanding of fundamental concepts. read more

Pitched very well for a 200-level Research Methods course. This text provided students with solid basis for class discussion and the further development of their understanding of fundamental concepts.

No issues with accuracy.

Coverage was on target, relevant, and applicable, with good examples from a variety of subfields within Psychology.

Clearly written -- students often struggle with the dry, technical nature of concepts in Research Methods. Part of the reason I chose this text in the first place was how favorably it compared to other options in terms of clarity.

No problems with inconsistent of shifting language. This is extremely important in Research Methods, where there are many closely related terms. Language was consistent and compatible with other textbook options that were available to my students.

Chapters are broken down into sections that are reasonably sized and conceptually appropriate.

The organization of this textbook fit perfectly with the syllabus I've been using (in one form or another) for 15+ years.

This textbook was easy to navigate and available in a variety of formats.

No problems at all.

Examples show an eye toward inclusivity. I did not detect any insensitive or offensive examples or undertones.

I have used this textbook for a 200-level Research Methods course run over a single summer session. This was my first experience using an OER textbook and I don't plan on going back.

Reviewed by Laura Getz, Assistant Professor, University of San Diego on 4/29/21

The topics covered seemed to be at an appropriate level for beginner undergraduate psychology students; the learning objectives for each subsection and the key takeaways and exercises for each chapter are also very helpful in guiding students’... read more

The topics covered seemed to be at an appropriate level for beginner undergraduate psychology students; the learning objectives for each subsection and the key takeaways and exercises for each chapter are also very helpful in guiding students’ attention to what is most relevant. The glossary is also thorough and a good resource for clear definitions. I would like to see a final chapter on a “big picture” or integrating key ideas of replication, meta-analysis, and open science.

Content Accuracy rating: 4

For the most part, I like the way information is presented. I had a few specific issues with definitions for ordinal variables being quantitative (1st, 2nd, 3rd aren’t really numbers as much as ranked categories), the lack of specificity about different forms of validity (face, content, criterion, and discriminant all just labeled “validity” whereas internal and external validity appear in different sections), and the lack of clear distinction between correlational and quasi-experimental variables (e.g., in some places, country of origin is listed as making a design quasi-experimental, but in other chapters it is defined as correlational).

Some of the specific studies/experiments mentioned do not seem like the best or most relevant for students to learn about the topics, but for the most part, content is up-to-date and can definitely be updated with new studies to illustrate concepts with relative ease.

Besides the few concepts I listed above in “accuracy”, I feel the text was very accessible, provides clear definitions, and many examples to illustrate any potential technical/jargon terms.

I did not notice any issues with inconsistent terms except for terms that do have more than one way of describing the same concept (e.g., 2-sample vs. independent samples t-test)

I assigned the chapters out of order with relative ease, and students did not comment about it being burdensome to navigate.

The order of chapters sometimes did not make sense to me (e.g., Experimental before Non-experimental designs, Quasi-experimental designs separate from other non-experimental designs, waiting until Chapter 11 to talk about writing), but for the most part, the chapter subsections were logical and clear.

Interface rating: 4

I had no issues navigating the online version of the textbook other than taking a while to figure out how to move forward and back within the text itself rather than going back to the table of contents (this might just be a browser issue, but is still worth considering).

No grammatical errors of note.

There was nothing explicitly insensitive or offensive about the text, but there were many places where I felt like more focus on diversity and individual differences could be helpful. For example, ethics and history of psychological testing would definitely be a place to bring in issues of systemic racism and/or sexism and a focus on WEIRD samples (which is mentioned briefly at another point).

I was very satisfied with this free resource overall, and I recommend it for beginning level undergraduate psychology research methods courses.

Reviewed by Laura Stull, Associate Professor, Anderson University on 4/23/21

This book covers essential topics and areas related to conducting introductory psychological research. It covers all critical topics, including the scientific method, research ethics, research designs, and basic descriptive and inferential... read more

This book covers essential topics and areas related to conducting introductory psychological research. It covers all critical topics, including the scientific method, research ethics, research designs, and basic descriptive and inferential statistics. It even goes beyond other texts in terms of offering specific guidance in areas like how to conduct research literature searches and psychological measurement development. The only area that appears slightly lacking is detailed guidance in the mechanics of writing in APA style (though excellent basic information is provided in chapter 11).

All content appears accurate. For example, experimental designs discussed, descriptive and inferential statistical guidance, and critical ethical issues are all accurately addressed, See comment on relevance below regarding some outdated information.

Relevance/Longevity rating: 3

Chapter 11 on APA style does not appear to cover the most current version of the APA style guide (7th edition). While much of the information in Chapter 11 is still current, there are specifics that did change from 6th to 7th edition of the APA manual and so, in order to be current, this information would have to be supplemented with external sources.

The book is extremely well organized, written in language and terms that should be easily understood by undergraduate freshmen, and explains all necessary technical jargon.

The text is consistent throughout in terms of terminology and the organizational framework (which aids in the readability of the text).

The text is divided into intuitive and common units based on basic psychological research methodology. It is clear and easy to follow and is divided in a way that would allow omission of some information if necessary (such as "single subject research") or reorganization of information (such as presenting survey research before experimental research) without disruption to the course as a whole.

As stated previously, the book is organized in a clear and logical fashion. Not only are the chapters presented in a logical order (starting with basic and critical information like overviews of the scientific method and research ethics and progressing to more complex topics like statistical analyses).

No issues with interface were noted. Helpful images/charts/web resources (e.g., Youtube videos) are embedded throughout and are even easy to follow in a print version of the text.

No grammatical issues were noted.

No issues with cultural bias are noted. Examples are included that address topics that are culturally sensitive in nature.

I ordered a print version of the text so that I could also view it as students would who prefer a print version. I am extremely impressed with what is offered. It covers all of the key content that I am currently covering with a (non-open source) textbook in an introduction to research methods course. The only concern I have is that APA style is not completely current and would need to be supplemented with a style guide. However, I consider this a minimal issue given all of the many strengths of the book.

Reviewed by Anika Gearhart, Instructor (TT), Leeward Community College on 4/22/21

Includes the majority of elements you expect from a textbook covering research methods. Some topics that could have been covered in a bit more depth were factorial research designs (no coverage of 3 or more independent variables) and external... read more

Includes the majority of elements you expect from a textbook covering research methods. Some topics that could have been covered in a bit more depth were factorial research designs (no coverage of 3 or more independent variables) and external validity (or the validities in general).

Nothing found that was inaccurate.

Looks like a few updates could be made to chapter 11 to bring it up to date with APA 7. Otherwise, most examples are current.

Very clear, a great fit for those very new to the topic.

The framework is clear and logical, and the learning objectives are very helpful for orienting the reader immediately to the main goals of each section.

Subsections are well-organized and clear. Titles for sections and subsections are clear.

Though I think the flow of this textbook for the most part is excellent, I would make two changes: move chapter 5 down with the other chapters on experimental research and move chapter 11 to the very end. I feel that this would allow for a more logical presentation of content.

The webpage navigation is easy to use and intuitive, the ebook download works as designed, and the page can be embedded directly into a variety of LMS sites or used with a variety of devices.

I found no grammatical errors in this book.

While there were some examples of studies that included participants from several cultures, the book does not touch on ecological validity, an important external validity issue tied to cultural psychology, and there is no mention of the WEIRD culture issue in psychology, which seems somewhat necessary when orienting new psychology students to research methods today.

I currently use and enjoy this textbook in my research methods class. Overall, it has been a great addition to the course, and I am easily able to supplement any areas that I feel aren't covered with enough breadth.

Reviewed by Amy Foley, Instructor/Field & Clinical Placement Coordinator, University of Indianapolis on 3/11/21

This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields. read more

This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields.

Accurate information!

This book is current and lines up well with the music therapy research course I teach as a supplemental text for students to understand research designs.

Clear language for psychology and related fields.

The format of the text is consistent. I appreciate the examples, different colored boxes, questions, and links to external sources such as video clips.

It is easy to navigate this text by chapters and smaller units within each chapter. The only confusion that has come from using this text includes the fact that the larger units have roman numerals and the individual chapters have numbers. I have told students to "read unit six" and they only read the small chapter 6, not the entire unit for example.

Flows well!

I have not experienced any interface issues.

I have not found any grammar errors.

Book appears culturally relevant.

This is a great resource for research methods courses in psychology or related fields. I am glad to have used several chapters of this text within the music therapy research course I teach where students learn about research design and then create their own research proposal.

Reviewed by Veronica Howard, Associate Professor, University of Alaska Anchorage on 1/11/21, updated 1/11/21

VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues. read more

VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues.

Content appears accurate.

By expanding to include more contemporary research perspectives, the authors have created a wonderful dynamic that permits the text to be the foundation for many courses as well as revision and remixing for other authors.

Book easy to read, follow.

Consistency rating: 4

Content overall consistent. Only mild inconsistency in writing style between chapters.

Exceptionally modular. All content neatly divided into units with smaller portions. This would be a great book to use in a course that meets bi-weekly, or adapted into other formats.

Content organized in a clear and logical fashion, and would guide students through a semester-long course on research methods, starting with review content, broad overview of procedures (including limitations), then highlighting less common (though relevant) procedures.

Rich variety of formats for use.

No errors found.

I would appreciate more cultural examples.

Reviewed by Greg Mullin, Associate Professor, Bunker Hill Community College on 12/30/20, updated 1/6/21

I was VERY pleased with the comprehensiveness of the text. I believe it actually has an edge over the publisher-based text that I've been using for years. Each major topic was thoroughly covered with more than enough detail on individual concepts. read more

I was VERY pleased with the comprehensiveness of the text. I believe it actually has an edge over the publisher-based text that I've been using for years. Each major topic was thoroughly covered with more than enough detail on individual concepts.

I did not find any errors within the text. The authors provided an unbiased representation of research methods in psychology.

The content connects to classic, timeless examples in the field, but also mixes in a fair amount of more current, relatable examples. I feel like I'll be able to use this version of the text for many years without its age showing.

The authors present a clear and efficient writing style throughout that is rich with relatable examples. The only area that may be a bit much for undergraduate-level student understanding is the topic of statistics. I personally scale back my discussion of statistics in my Intro to Research Methods course, but for those that prefer a deeper dive, the higher-level elements are there.

I did not notice any shifts with the use of terminology or with the structural framework of the text. The text is very consistent and organized in an easily digestible way.

The authors do a fantastic job breaking complex topics down into manageable chunks both as a whole and within chapters. As I was reading, I could easily see how I could align my current approach of teaching Intro to Research Methods with their modulated presentation of the material.

I effortlessly moved through the text given the structural organization. All topics are presented in a logical fashion that allowed each message to be delivered to the reader with ease.

I read the text through the PDF version and found no issue with the interface. All image and text-based material was presented clearly.

I cannot recall coming across any grammatical errors. The text is very well written.

I did not find the text to be culturally insensitive in any way. The authors use inclusive language and even encourage that style of writing in the chapter on Presenting Your Research. I would have liked to see more cross-cultural research examples and more of an extended effort to include the theme of diversity throughout, but at no point did I find the text to be offensive.

This is a fantastic text and I look forward to adopting it for my Intro to Research Methods course in the Spring. :)

Reviewed by Maureen O'Connell, Adjunct Professor, Bunker Hill Community College on 12/15/20, updated 12/18/20

This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers.  read more

This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers. 

The content is unbiased, accurate, and I did not find any errors in the text. 

The content is current and up-to-date. I found that the text can be added to should material change, the arrangement of the text/content makes it easily accessible to add material, if necessary. 

The text is clear, easy to understand, simplistic writing at times, but I find this text easy for students to comprehend. All text is relevant to the content of behavioral research. 

The text and terminology is consistent. 

The text is organized well and sectioned appropriately. The information is presented in an easy-to-read format, with sections that can be assigned at various points during the semester and the reader can easily locate this. 

The topics in the text are organized in a logical and clear manner. It flows really well. 

The text is presented well, including charts, diagrams, and images. There did not appear to be any confusion with this text. 

The text contains no grammatical errors.

The text was culturally appropriate and not offensive. Clear examples of potential biases were outlined in this text which I found quite helpful for the reader. 

Overall, I found this to be a great edition. Much of the time I spend researching outside material for students has been included in this text. I enjoyed the format, easier to navigate, helpful to students by providing an updated version of discussions and practice assignments, and visually more appealing. 

Reviewed by Brittany Jeye, Assistant Professor of Psychology, Worcester State University on 6/26/20

All of the main topics in a Research Methods course are covered in this textbook (e.g., scientific method, ethics, measurement, experimental design, hypothesis testing, APA style, etc.). Some of these topics are not covered as in-depth as in other... read more

All of the main topics in a Research Methods course are covered in this textbook (e.g., scientific method, ethics, measurement, experimental design, hypothesis testing, APA style, etc.). Some of these topics are not covered as in-depth as in other Research Method textbooks I have used previously, but this actually may be a positive depending on the students and course level (that is, students may only need a solid overview of certain topics without getting overwhelmed with too many details). It also gives the instructor the ability to add content as needed, which helps with flexibility in course design.

I did not note any errors or inaccurate/biasing statements in the text.

For the most part, everything was up to date. There was a good mix of classic research and newer studies presented and/or used as examples, which kept the chapters interesting, topical and relevant. I only noted the section on APA Style in the chapter “Presenting Your Research” which may need some updating to be in line with the new APA 7th edition. However, there should be only minor edits needed (the chapter itself was great overview and introduction to the main points of APA style) and it looks like they should be relatively easy to implement.

The text was very well-written and was presented at an accessible level for undergraduates new to Research Methods. Terms were well-defined with a helpful glossary at the end of the textbook.

The consistent structure of the textbook is huge positive. Each chapter begins with learning objectives and ends with bulleted key takeaways. There are also good exercises and learning activities for students at the end of each chapter. Instructors may need to add their own activities for chapters that do not go into a lot of depth (there are also instructor resources online, which may have more options available).

This is one of the biggest strengths of this textbook, in my opinion. I appreciate how each chapter is broken down into clearly defined subsections. The chapters and the subsections, in particular, are not lengthy, which is great for students’ learning. These subsections could be reorganized and used in a variety of ways to suit the needs of a particular course (or even as standalone subsections).

The topics were presented in a logical manner. As mentioned above, since the textbook is very modular, I feel that you could easily rearrange the chapters to fit your needs (for example, presenting survey design before experimental research or making the presenting your research section a standalone unit).

I downloaded the textbook as an ebook, which was very easy to use/navigate. There were no problems reading any of the text or figures/tables. I also appreciated that you could open the ebook using a variety of apps (Kindle, iBook, etc.) depending on your preference (and this is good for students who have a variety of technical needs).

There were no grammatical errors noted.

The examples were inclusive of races, ethnicity and background and there were not any examples that were culturally insensitive or offensive in any way. In future iterations of the replicability section, it may be beneficial to touch upon the “weird” phenomena in psychology research (that many studies use participants who are western, educated and from industrialized, rich and democratic countries) as a point to engage students in improving psychological practices.

I will definitely consider switching to this textbook in the future for Research Methods.

Reviewed by Alice Frye, Associate Teaching Professor, University of Massachusetts Lowell on 6/22/20

Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching. read more

Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching.

Correct and to the point. Complex ideas such as internal consistency reliability and discriminant validity are well handled--correct descriptions that are also succinct and articulated simply and with clear examples that are easy for a student reader to grasp.

Seems likely to have good staying power. One area that has changed quickly in the past is the usefulness of various research data bases. So it is possible that portion could become more quickly outdated, but there is no predicting that. The current descriptions are useful.

Very clearly written without being condescending, overly casual or clunky.

Excellent consistency throughout in terms of organization, language usage, level of detail and tone.

Imho this is one of the particular strengths of the text. Chapters are well divided into discrete parts, which seems likely to be a benefit in cohorts of students who are increasingly accustomed to digesting small amounts of information.

Well organized, straightforward structure that is maintained throughout.

No problems with the interface.

The grammar level is another notable strength. Ideas are articulated clearly, and with sophistication, but in a syntactically very straightforward manner.

The text isn't biased or offensive. I wish that to illustrate various points and research designs it had drawn more frequently on research studies that incorporate a specific focus on race and ethnicity.

This is a very good text. As good as any for profit text I have used to teach a research methods course, if not better.

Reviewed by Lauren Mathieu-Frasier, Adjunct Instructor, University of Indianapolis on 1/13/20

As other reviews have mentioned, this textbook provides a comprehensive look at multiple concepts for an introductory course in research methods in psychology. Some of the concepts (i.e., variables, external validity) are briefly described and... read more

As other reviews have mentioned, this textbook provides a comprehensive look at multiple concepts for an introductory course in research methods in psychology. Some of the concepts (i.e., variables, external validity) are briefly described and glossed over that it will take additional information, examples, and reinforcement from instructors in the classroom. Other sections and concepts, like ethics or reporting of research were well-described and thorough.

It appeared that the information was accurate, error-free, and unbiased.

The information is up-to-date. In the section on APA presentation, it looks like the minor adjustments to the APA Publication Manual 7th Edition would need to be included. However, this section gives a good foundation and the instructor can easily implement the changes.

Clarity rating: 4

The text is clearly written written and provides an appropriate context when terminology is used.

There aren't any issues with consistency in the textbook.

The division of smaller sections can be beneficial when reading it and assigning it to classes. The sections are clearly organized based on learning objectives.

The textbook is organized in a logical, clear manner. There may be topics that instructors choose to present in a different manner (non-experimental and survey research prior to experimental). However, this doesn't generally impact the organization and flow of the book.

While reading and utilizing the book, there weren't any navigation issues that could impact the readability of the book. Students could find this textbook easy to use.

Grammatical errors were not noted.

There weren't any issues with cultural sensitivity in the examples of studies used in the textbook.

Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 1/1/20

The text is comprehensive with an effective glossary of terms at the end. It would be beneficial to include additional examples and exercises for students to better understand concepts covered in Chapter II, Overview of the Scientific Method,... read more

The text is comprehensive with an effective glossary of terms at the end. It would be beneficial to include additional examples and exercises for students to better understand concepts covered in Chapter II, Overview of the Scientific Method, Chapter IV, Psychological Measurement, and Chapter XII Descriptive Statistics.

The text is accurate and there are minimal type/grammatical errors throughout. The verbiage is written in an unbiased manner consistently throughout the textbook.

The content is up-to-date, and examples can be easily updated for future versions. As a public health instructor, I would be interested in seeing examples of community-based examples in future versions. The current examples provided are relevant for undergraduate public health students as well as psychology students.

The text is written in a clear manner. The studies used can be easily understood by undergraduate students in other social science fields, such as public health. More examples and exercises using inferential statistics would be helpful for students to better grasp the concepts.

The framework for each chapter and terminology used are consistent. It is helpful that each section within each chapter begins with learning objectives and the chapter ends with key takeaways and exercises.

The text is clearly divided into sections within each chapter. When I first started reviewing this textbook, I thought each section was actually a very short chapter. I would recommend including a listing of all of the objectives covered in each chapter at the beginning to improve the modularity of the text.

Some of the topics do not follow a logical order. For example, it would be more appropriate to discuss ethics before providing the overview of the scientific method. It would be better to discuss statistics used to determine results before describing how to write manuscripts. However, the text is written in a way that that the chapters could be assigned to students in a different order without impacting the students’ comprehension of the concepts.

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader. There are several data tables throughout the text which are left-aligned and there is a large amount of empty white space next it. I would rearrange the text in future versions to make better use of this space.

The text contains minimal grammatical errors.

The examples are culturally relevant. I did not see any examples that may be considered culturally insensitive or offensive in any way.

As an instructor for an undergraduate public health sciences and methods course, I will consider using some of the content in this text to supplement the current textbook in the future.

Reviewed by Mickey White, Assistant Professor, East Tennessee State University on 10/23/19

The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects. read more

The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects.

Content appears to be accurate and up-to-date.

This text is useful and relevant, particularly with regard to expressing and reporting descriptive statistics and results. As APA updates, the text will be easy to edit, as the sections are separated.

Easy to read and engaging.

Chapters were laid out in a consistent manner, which allows readers to know what is coming. The subsections contained a brief overview and terminology was consistent throughout. The glossary added additional information.

Sections and subsections are delineated in a usable format.

The key takeaways were useful, including the exercises at the end of each chapter.

Reading the book online is a little difficult to navigate page-by-page, but e-pub and PDF formats are easy to navigate.

No errors noted.

Would be helpful to have a clearer exploration of cultural factors impacting research, including historical bias in assessment and research outside of research ethics.

Reviewed by Robert Michael, Assistant Professor, University of Louisiana at Lafayette on 10/14/19

Successfully spans the gamut of topics expected in a Research Methods textbook. Some topics are covered in-depth, while others are addressed only at a surface level. Instructors may therefore need to carefully arrange class material for topics in... read more

Successfully spans the gamut of topics expected in a Research Methods textbook. Some topics are covered in-depth, while others are addressed only at a surface level. Instructors may therefore need to carefully arrange class material for topics in which depth of knowledge is an important learning outcome.

The factual content was error-free, according to my reading. I did spot a few grammatical and typographical errors, but they were infrequent and minor.

Great to see nuanced—although limited—discussion of issues with Null Hypothesis Significance Testing, Reproducibility in Psychological Science, and so forth. I expect that these areas are likely to grow in future editions, perhaps supplementing or even replacing more traditional material.

Extremely easy to read with multiple examples throughout to illustrate the principles being covered. Many of these examples are "classics" that students can easily relate to. Plus, who doesn't like XKCD comics?

The textbook is structured sensibly. At times, certain authors' "voices" seemed apparent in the writing, but I suspect this variability is unlikely to be noticed by or even bothersome to the vast majority of readers.

The topics are easily divisible and seem to follow routine expectations. Instructors might find it beneficial and/or necessary to incorporate some of the statistical thinking and learning into various earlier chapters to facilitate student understanding in-the-moment, rather than trying to leave all the statistics to the end.

Sensible and easy-to-follow structure. As per "Modularity", the Statistical sections may benefit from instructors folding in such learning throughout, rather than only at the end.

Beautifully presented, crisp, easy-to-read and navigate. Caveat: I read this online, in a web-browser, on only one device. I haven't tested across multiple platforms.

High quality writing throughout. Only a few minor slip-ups that could be easily fixed.

Includes limited culturally relevant material where appropriate.

Reviewed by Matthew DeCarlo, Assistant Professor, Radford University on 6/26/19

The authors do a great job of simplifying the concepts of research methods and presenting them in a way that is understandable. There is a tradeoff between brevity and depth here. Faculty who adopt this textbook may need to spend more time in... read more

The authors do a great job of simplifying the concepts of research methods and presenting them in a way that is understandable. There is a tradeoff between brevity and depth here. Faculty who adopt this textbook may need to spend more time in class going in depth into concepts, rather than relying on the textbook for all of the information related to key concepts. The text does not cover qualitative methods in detail.

The textbook provides an accurate picture of research methods. The tone is objective and without bias.

The textbook is highly relevant and up to date. Examples are drawn from modern theories and articles.

The writing is a fantastic mix of objective and authoritative while also being approachable.

The book coheres well together. Each chapter and section are uniform.

This book fits very well within a traditional 16 week semester, covering roughly a chapter per week. One could take out specific chapters and assign them individually if research methods is taught in a different way from a standard research textbook.

Content is very well organized. The table of contents is easy to navigate and each chapter is presented in a clear and consistent manner. The use of a two-tier table of contents is particularly helpful.

Standard pressbooks interface, which is great. Uses all of the standard components of Pressbooks well, though the lack of H5P and interactive content is a drawback.

I did not notice any grammar errors.

Cultural Relevance rating: 2

The book does not deal with cultural competence and humility in the research process. Integration of action research and decolonization perspectives would be helpful.

Reviewed by Christopher Garris, Associate Professor, Metropolitan State University of Denver on 5/24/19

Most content areas in this textbook were covered appropriately extensively. Notably, this textbook included some content that is commonly missing in other textbooks (e.g. presenting your research). There were some areas where more elaboration... read more

Most content areas in this textbook were covered appropriately extensively. Notably, this textbook included some content that is commonly missing in other textbooks (e.g. presenting your research). There were some areas where more elaboration and more examples were needed. For example, the section covering measurement validities included all the important concepts, but needed more guidance for student comprehension. Also, the beginning chapters on 'common sense' reasoning and pseudoscience seemed a little too brief.

Overall, this textbook appeared to be free from glaring errors. There were a couple of instances of concern, but were not errors, per se. For example, the cut-off for Cronbach's alpha was stated definitively at .80, while this value likely would be debated among researchers.

This textbook was presented in such a way that seemed protect it from becoming obsolete within the next few years. This is important for continued, consistent use of the book. The authors have revised this book, and those revisions are clearly summarized in the text. Importantly, the APA section of the textbook appears to be up-to-date. Also, the use of QR codes throughout the text is a nice touch that students may appreciate.

Connected to comprehensiveness, there are some important content areas that I felt were lacking in elaboration and examples (e.g. testing the validity of measurement; introduction of experimental design), which inhibits clarity. Overall, however, the topics seemed to be presented in a straightforward, accessible manner. The textbook includes links to informative videos and walk-throughs where appropriate, which seem to be potentially beneficial for student comprehension. The textbook includes tools designed to aid learning, namely "Key Takeaways" and "Exercises" sections at the end of most modules, but not all. "Key Takeaways" seemed valuable, as they were a nice bookend to the learning objectives stated at the beginning of each module. "Exercises" did not appear to be as valuable, especially for the less-motivated student. On their face, these seemed to be more designed for instructors to use as class activities/active learning. Lastly, many modules of the textbook were text-heavy and visually unappealing. While this is superficial, the inclusion of additional graphics, example boxes, or figures in these text-heavy modules might be beneficial.

The textbook appeared to be internally consistent with its approach and use of terminology.

The textbook had a tendency to 'throw out' big concepts very briefly in earlier modules (e.g. sampling, experimental/non-experimental design), and then cover them in more detail in later modules. This would have been less problematic if the text would explicitly inform the student that these concepts would be elaborated upon later. Beyond this issue, the textbook seems to lend itself to being divided up and used on module-by-module basis.

The organization of the chapters did not make intuitive sense to me. The fact that correlation followed experimental research, and that descriptive research was the second-to-last module in the sequence was confusing. That said, textbook is written in such a way that an instructor easily assign the modules in the order that works best for their class.

Overall, the interface worked smoothly and there were few technical issues. Where there were issues (e.g. inconsistent spacing between lines and words), they were negligible.

The text seemed to be free from glaring grammatical problems.

Because this is a methodology textbook, it does not lend itself to too much cultural criticism. That said, the book did not rely on overly controversial examples, but also didn't shy away from important cultural topics (e.g. gender stereotypes, vaccines).

Reviews prior to 2019 are for a previous edition.

Reviewed by Michel Heijnen, Assistant Professor, University of North Carolina Wilmington on 3/27/18

The book covers all areas related to research methods, not only for the field of psychology, but also to other related fields like exercise science. Topics include ethics, developing a research questions, experimental designs, non-experimental... read more

The book covers all areas related to research methods, not only for the field of psychology, but also to other related fields like exercise science. Topics include ethics, developing a research questions, experimental designs, non-experimental designs, and basic statistics, making this book a great resource for undergraduate research methods classes.

Reviewed content is accurate and seems free of any personal bias.

The topic of research methods in general is not expected to change quickly. It is not expected that this text will become obsolete in the near future. Furthermore, for both the field of psychology as well as other related fields, the examples will continue to have an application to explain certain concepts and will not be outdated soon, even with new research emerging every day.

The text is written so an undergraduate student should be able to understand the concepts. The examples provided in the text greatly contribute to the understanding of the topics and the proposed exercises at the end of each chapter will further apply the knowledge.

The layout and writing style are consistent throughout the text.

Layout of the text is clear, with multiple subsection within each chapter. Each chapter can easily be split into multiple subsection to assign to students. No evidence of self-refers was observed, and individual chapters could be assigned to students without needed to read all preceding chapters. For example, Chapter 4 may not be particularly useful to students outside of psychology, but an instructor can easily reorganize the text and skip this chapter while students can still understand following chapters.

Topics are addressed in a logical manner. Overall, an introduction to research is provided first (including ethics to research), which is followed by different types of research, and concludes with types of analysis.

No images or tables are distorted, making the text easy to read.

No grammatical errors observed in text.

Text is not offensive and does not appear to be culturally insensitive.

I believe that this book is a great resource and, as mentioned previously, can be used for a wider audience than just psychology as the basics of research methods can be applied to various fields, including exercise science.

Reviewed by Chris Koch, Professor of Psychology, George Fox University on 3/27/18

All appropriate areas and topics are covered in the text. In that sense, this book is equivalent to other top texts dealing with research methods in psychology. The appeal of this book is the brevity and clarity. Therefore, some may find that,... read more

All appropriate areas and topics are covered in the text. In that sense, this book is equivalent to other top texts dealing with research methods in psychology. The appeal of this book is the brevity and clarity. Therefore, some may find that, although the topics are covered, topics may not be covered as thoroughly they might like. Overall, the coverage is solid for an introductory course in research methods.

In terms of presentation, this book could be more comprehensive. Each chapter does start with a set of learning objectives and ends with "takeaways" and a short set of exercises. However, it lacks detailed chapter outlines, summaries, and glossaries. Furthermore, an index does not accompany the text.

I found the book to be accurate with content being fairly presented. There was no underlying bias throughout the book.

This is an introductory text for research methods. The basics of research methods have been consistent for some time. The examples used in the text fit the concepts well. Therefore, it should not be quickly dated. It is organized in such a way that sections could be easily modified with more current examples as needed.

The text is easy to read. It is succinct yet engaging. Examples are clear and terminology is adequately defined.

New terms and concepts are dealt with chapter by chapter. However, those things which go across chapters are consistently presented.

The material for each chapter is presented in subsections with each subsection being tied to a particular learning objective. It is possible to use the book by subsection instead of by chapter. In fact, I did that during class by discussing the majority of one chapter, discussing another chapter, and then covering what I previously skipped,

In general, the book follows a "traditional" organization, matching the organization of many competing books. As mentioned in regard to modularity, I did not follow the organization of the book exactly as it was laid out. This may not necessarily reflect poorly on the book, however, since I have never followed the order of any research methods book. My three exams covered chapter 1 through 4, chapters 5, 6, part of 8, and chapters 7, the remainder of 8, 9, and 10. Once we collected data I covered chapters 11 through 13.

Interface rating: 3

The text and images are clear and distortion free. The text is available in several formats including epub, pdf, mobi, odt, and wxr. Unfortunately, the electronic format is not taken full advantage of. The text could be more interactive. As it is, it is just text and images. Therefore, the interface could be improved.

The book appeared to be well written and edited.

I did not find anything in the book that was culturally insensitive or offensive. However, more examples of cross-cultural research could be included.

I was, honestly, surprised by how much I liked the text. The material was presented in easy to follow format that is consistent with how I think about research methods. That made the text extremely easy to use. Students also thought the book was highly accessible Each chapter was relatively short but informative and easy to read.

Reviewed by Kevin White, Assistant Professor, East Carolina University on 2/1/18

This book covers all relevant topics for an introduction to research methods course in the social sciences, including measurement, sampling, basic research design, and ethics. The chapters were long enough to be somewhat comprehensive, but short... read more

This book covers all relevant topics for an introduction to research methods course in the social sciences, including measurement, sampling, basic research design, and ethics. The chapters were long enough to be somewhat comprehensive, but short enough to be digestible for students in an introductory-level class. Student reviews of the book have so far been very positive. The only section of the text for which more detail may be helpful is 2.3 (Reviewing the Research Literature), in which more specific instructions related to literature searches may be helpful to students.

I did not notice any issues related to accuracy. Content appeared to be accurate, error-free, and unbiased.

One advantage of this book is that it is relevant to other applied fields outside of psychology (e.g., social work, counseling, etc.). Also, the exercises at the end of chapter sections are helpful.

The clarity of the text provides students with succinct definitions for research-related concepts, without unnecessary discipline-specific jargon. One suggestion for future editions would be to make the distinctions between different types of non-experimental research a bit more clear for students in introductory classes (e.g., "Correlational Research" in Section 7.2).

Formatting and terminology was consistent throughout this text.

A nice feature of this book is that instructors can select individual sections within chapters, or even jump between sections within chapters. For example, Section 1.4 may not fit for a class that is less clinically-oriented in nature.

The flow of the text was appropriate, with ethics close to the beginning of the book (and an entire chapter devoted to it), and descriptive/inferential statistics at the end.

I did not notice any problems related to interface. I had no trouble accessing or reading the text, and the images were clear.

The text contained no discernible grammatical errors.

The book does not appear to be culturally insensitive in any discernible way, and explicitly addresses prejudice in research (e.g., Section 5.2). However, I think that continuing to add more examples that relate to specific marginalized groups would help improve the text (and especially exercises).

Overall, this book is very useful for an introductory research methods course in psychology or social work, and I highly recommend.

Reviewed by Elizabeth Do, Instructor, Virginia Commonwealth University on 2/1/18

Although this textbook does provide good information regarding introductory concepts necessary for the understanding of correlational designs, and is presented in a logical order. It does not, however, cover qualitative methodologies, or research... read more

Although this textbook does provide good information regarding introductory concepts necessary for the understanding of correlational designs, and is presented in a logical order. It does not, however, cover qualitative methodologies, or research ethics as it relates to other countries outside of the US.

There does not seem to be any errors within the text.

Since this textbook covers a topic that is unlikely to change over the years and it's content is up-to-date, it remains relevant to the field.

The textbook is written at an appropriate level for undergraduate students and is useful in that it does explain important terminology.

There does not seem to be any major inconsistencies within the text.

Overall, the text is very well organized - it is separated into chapters that are divided up into modules and within each module, there are clear learning objectives. It is also helpful that the textbook includes useful exercises for students to practice what they've read about from the text.

The topics covered by this textbook are presented in an order that is logical. The writing is clear and the examples are very useful. However, more information could be provided in some of the chapters and it would be useful to include a table of contents that links to the different chapters within the PDF copy, for reader's ease in navigation when looking for specific terms and/or topics.

Overall, the PDF copy of the textbook made it easy to read; however, there did seem to be a few links that were missing. Additionally, it would be helpful to have some of the graphs printed in color to help with ease of following explanations provided by the text. The inclusion of a table of contents would also be useful for greater ease with navigation.

There does not seem to be any grammatical errors in the textbook. Also, the textbook is written in a clear way, and the information flows nicely.

This textbook focuses primarily on examples from the United States. It does not seem to be culturally insensitive or offensive in anyway and I liked that it included content regarding the avoidance of biased language (chapter 11).

This textbook makes the material very accessible, and it is easy to read/follow examples.

Reviewed by Eric Lindsey, Professor, Penn State University Berks Campus on 2/1/18

The content of the Research Methods in Psychology textbook was very thorough and covered what I would consider to be the important concepts and issues pertaining to research methods. I would judge that the textbook has a comparable coverage of... read more

The content of the Research Methods in Psychology textbook was very thorough and covered what I would consider to be the important concepts and issues pertaining to research methods. I would judge that the textbook has a comparable coverage of information to other textbooks I have reviewed, including the current textbook I am using. The range of scholarly sources included in the textbook was good, with an appropriate balance between older and classic research examples and newer more cutting edge research information. Overall, the textbook provides substantive coverage of the science of conducting research in the field of psychology, supported by good examples, and thoughtful questions.

The textbook adopts a coherent and student-friendly format, and offers a precise introduction to psychological research methodology that includes consideration of a broad range of qualitative and quantitative methods to help students identify and evaluate the best approach for their research needs. The textbook offers a detailed review of the way that psychological researchers approach their craft. The author guides the reader through all aspects of the research process including formulating objectives, choosing research methods, securing research participants, as well as advice on how to effectively collect, analyze and interpret data and disseminate those findings to others through a variety of presentation and publication venues. The textbook offers relevant supplemental information in textboxes that is highly relevant to the material in the accompanying text and should prove helpful to learners. Likewise the graphics and figures that are included are highly relevant and clearly linked to the material presented in the text. The information covered by the textbook reflects an accurate summary of current techniques and methods used in research in the field of psychology. The presentation of information addresses the pros and cons of different research strategies in an objective and evenhanded way.

The range of scholarly sources included in the textbook was good, with an appropriate balance between older, classic research evidence and newer, cutting edge research. Overall, the textbook provides substantive coverage of the science on most topics in research methods of psychology, supported by good case studies, and thoughtful questions. The book is generally up to date, with adequate coverage of basic data collection methods and statistical techniques. Likewise the review of APA style guidelines is reflects the current manual and I like the way the author summarizes changes from the older version of the APA manual. The organization of the textbook does appear to lend itself to editing and adding new information with updates in the future.

I found the textbook chapters to be well written, in a straightforward yet conversational manner. It gives the reader an impression of being taught by a knowledgeable yet approachable expert. The writing style gives the learner a feeling of being guided through the lessons and supported in a very conversational approach. The experience of reading the textbook is less like being taught and more like a colleague sharing information. Furthermore, the style keeps the reader engaged but doesn't detract from its educational purpose. I also appreciate that the writing is appropriately concise. No explanations are so wordy as to overwhelm or lull the reader to sleep, but at the same time the information is not so vague that the reader can't understand the point at all.

The book’s main aim is to enable students to develop their own skills as researchers, so they can generate and advance common knowledge on a variety of psychological topics. The book achieves this objective by introducing its readers, step-by-step, to psychological research design, while maintaining an excellent balance between substance and attention grabbing examples that is uncommon in other research methods textbooks. Its accessible language and easy-to-follow structure and examples lend themselves to encouraging readers to move away from the mere memorization of facts, formulas and techniques towards a more critical evaluation of their own ideas and work – both inside and outside the classroom. The content of the chapters have a very good flow that help the reader to connect information in a progressive manner as they proceed through the textbook.

Each chapter goes into adequate depth in reviewing both past and current research related to the topic that it covers for an undergraduate textbook on research methods in psychology. The information within each chapter flows well from point-to-point, so that the reader comes away feeling like there is a progression in the information presented. The only limitation that I see is that I felt the author could do a little more to let the reader know how information is connected from chapter to chapter. Rather than just drawing the reader’s attention to things that were mentioned in previous chapters, it would be nice to have brief comments about how issues in one chapter relate to topics covered in previous chapters.

In my opinion the chapters are arranged in easily digestible units that are manageable in 30-40 minute reading sessions. In fact, the author designed the chapters of the textbook in a way to make it easy to chunk information, and start and stop to easily pick up where one leaves off from one reading session to another. I also found the flow of information to be appropriate, with chapters containing just the right amount of detail for use in my introductory course in research methods in psychology.

The book is organized into thirteen chapters. The order of the chapters offers a logical progression from a broad overview of information about the principles and theory behind research in psychology, to more specific issues concerning the techniques and mechanics of conducting research. Each chapter ends with a summary of key takeaways from the chapter and exercises that do more than ask for content regurgitation. I find the organization of the textbook to be effective, and matches my approach to the course very well. I would not make any changes to the overall format with the exception of moving chapter 11 on presenting research to the end of the textbook, after the chapters on statistical analysis and interpretation.

I found the quality of the appearance of the textbook to be very good. The textbook features appropriate text and section/header font sizes that allow for an adequate zooming level to read large or smalls sections of text, that will give readers flexibility to match their personal preference. There are learning objectives at the start of each chapter to help students know what to expect. Key terms are highlighted in a separate color that are easily distinguishable in the body of the page. There are very useful visuals in every chapter, including tables, figures, and graphs. Relevant supplemental information is also highlighted in well formatted text boxes that are color coded to indicate what type of information is included. My only criticism is that the photographs included in the text are of low quality, and there are so few in the textbook that I feel it would have been better to just leave them out.

I found no grammatical errors in my review of the textbook. The textbook is generally well written, and the style of writing is at a level that is appropriate for an undergraduate class.

Although the textbook contains no instances of presenting information that is cultural insensitive or offensive, it does not offer an culturally inclusive review of information pertaining to research methods in psychology. I found no inclusion of examples of research conducting with non European American samples included in the summary of studies. Likewise the authors do place much attention on the issue of cultural sensitivity when conducing research. If there is one major weakness of the textbook I would say it is in this area, but based on my experience it is not an uncommon characteristic of textbooks on research methods in psychology.

Reviewed by zehra peynircioglu, Professor, American University on 2/1/18

Short and sweet in most areas. Covers the basic concepts, not very comprehensively but definitely adequately so for a general beginning-level research methods course. For instance, I would liked to have seen a "separate" chapter on correlational... read more

Comprehensiveness rating: 3 see less

Short and sweet in most areas. Covers the basic concepts, not very comprehensively but definitely adequately so for a general beginning-level research methods course. For instance, I would liked to have seen a "separate" chapter on correlational research (there is one on single subject research and one on survey research), a discussion of the importance of providing a theoretical rationale for "getting an idea" (most students are fine with finding interesting and feasible project ideas but cannot give a theoretical rationale) before or after Chapter 4 on Theory, or a chapter on neuroscientific methods, which are becoming more and more popular. Nevertheless, it touches on most traditional areas that are in other books.

I did not find any errors or biases

This is one area where there is not much danger of going obsolete any time soon. The examples might need to be updated periodically (my students tend to not like dated materials, however relevant), but that should be easy.

Very clear and accessible prose. Despite the brevity, the concepts are put forth quite clearly. I like the "not much fluff" mentality. There is also adequate explanations of jargon and technical terminology.

I could not find any inconsistencies. The style and exposition frameworks are also quite consistent.

Yes, the modularity is fine. The chapters follow a logical pattern, so there should not be too much of a need for jumping around. And even if jumping around is needed depending on teaching style, the sections are solid in terms of being able to stand alone (or as an accompaniment to lectures).

Yes, the contents is ordered logically and the high modularity helps with any reorganization that an instructor may favor. In my case, for instance, Ch. 1 is fine, but I would skip it because it's mostly a repetition of what most introductory psychology books also say. I would also discuss non-experimental methods before going into experimental design. But such changes are easy to do, and if someone followed the book's own organization, there would also be a logical flow.

As far as I could see, the text is free of significant interface issues, at least in the pdf version

I could not find any errors.

As far as I could see, the book was culturally relevant.

I loved the short and sweet learning objectives, key takeaway sections, and the exercises. They are not overwhelming and can be used in class discussions, too.

Reviewed by George Woodbury, Graduate Student, Miami University, Ohio on 6/20/17

This text covers the typical areas for an undergraduate psychology course in research design. There is no table of contents included with the downloadable version, although there is a table of contents on the website (which excludes sub-sections... read more

This text covers the typical areas for an undergraduate psychology course in research design. There is no table of contents included with the downloadable version, although there is a table of contents on the website (which excludes sub-sections of chapters). The sections on statistics are not extensive enough to be useful in and of themselves, but they are useful for transitions to a follow-up statistics course. There does not seem to be a glossary of terms, which made it difficult at times for my read through and I assume later for students who decide to print the text. The text is comprehensive without being wordy or tedious.

Relatively minor errors; There does not seem to be explicit cultural or methodological bias in the text.

The content is up-to-date, and examples from the psychology literature are generally within the last 25 years. Barring extensive restructuring in the fundamentals of methodology and design in psychology, any updates will be very easy to implement.

Text will be very clear and easy to read for students fluent in English. There is little jargon/technical terminology used, and the vocabulary that is provided in the text is contemporary

There do not seem to be obvious shifts in the terminology or the framework. The text is internally consistent in that regard.

The text is well divided into chapter and subsections. Each chapter is relatively self-contained, so there are little issues with referring to past material that may have been skipped. The learning objectives at the beginning of the chapter are very useful. Blocks of text are well divided with headings.

As mentioned above, the topics of the text follow the well-established trajectory of undergraduate psychology courses. This makes it very logical and clear.

The lack of a good table of contents made it difficult to navigate the text for my read through. There were links to an outside photo-hosting website (flickr) for some of the stock photos, which contained the photos of the original creators of the photos. This may be distracting or confusing to readers. However, the hyperlinks in general helped with navigation with the PDF.

No more grammatical errors than a standard, edited textbook.

Very few examples explicitly include other races, ethnicities, or backgrounds, however the examples seem to intentionally avoid cultural bias. Overall, the writing seems to be appropriately focused on avoiding culturally insensitive or offensive content.

After having examined several textbooks on research design and methodology related to psychology, this book stands out as superior.

Reviewed by Angela Curl, Assistant Professor, Miami University (Ohio) on 6/20/17

"Research Methods in Psychology" covers most research method topics comprehensively. The author does an excellent job explaining main concepts. The chapter on causation is very detailed and well-written as well as the chapter on research ethics.... read more

"Research Methods in Psychology" covers most research method topics comprehensively. The author does an excellent job explaining main concepts. The chapter on causation is very detailed and well-written as well as the chapter on research ethics. However, the explanations of data analysis seem to address upper level students rather than beginners. For example, in the “Describing Statistical Relationships” chapter, the author does not give detailed enough explanations for key terms. A reader who is not versed in research terminology, in my opinion, would struggle to understand the process. While most topics are covered, there are some large gaps. For example, this textbook has very little content related to qualitative research methods (five pages).

The content appears to be accurate and unbias.

The majority of the content will not become obsolete within a short time period-- many of the information can be used for the coming years, as the information provided is, overall, general in nature. The notably exceptions are the content on APA Code of Ethics and the APA Publication Manual, which both rely heavily on outdated versions, which limits the usefulness of these sections. In addition, it would be helpful to incorporate research studies that have been published after 2011.

The majority of the text is clear, with content that is easy for undergraduate students to read and understand. The key points included in the chapters are helpful, but some chapters seem to be missing key points (i.e., the key points do not accurately represent the overall chapter).

The text seems to be internally consistent in its terminology and organization.

Each chapter is broken into subsections that can be used alone. For example, section 5.2 covers reliability and validity of measurement. This could be extremely helpful for educators to select specific content for assigned readings.

The topics are presented in a logical matter for the most part. However, the PDF version of the book does not include a table of contents, and none of the formats has a glossary or index. This can make it difficult to quickly navigate to specific topics or terms, especially when explanations do not appear where expected. For example, the definitions of independent and dependent variables is provided under the heading “Correlation Does Not Imply Causation” (p. 22).

The text is consistent but needs more visual representations throughout the book, rather than heavily in some chapters and none at all in other chapters. Similarly, the text within the chapters is not easily readable due to the large sections of text with little to no graphics or breaks.

The interface of the text is adequate. However, the formatting of the PDF is sometimes weak. For example, the textbook has a number of pages with large blank spaces and other pages are taken up with large photos or graphics. The number of pages (and cost of printing) could have been reduced, or more graphics added to maximize utility.

I found no grammatical errors.

Text appears to be culturally sensitive. I appreciated the inclusion of the content about avoiding biased language (chapter 11).

Instructors who adopt this book would likely benefit from either selecting certain chapters/modules and/or integrating multiple texts together to address the shortcomings of this text. Further, the sole focus on psychology limits the use of this textbook for introductory research methods for other disciplines (e.g., social work, sociology).

Reviewed by Pramit Nadpara, Assistant Professor, Virginia Commonwealth University on 4/11/17

The text book provides good information in certain areas, while not comprehensive information in other areas. The text provides practical information, especially the section on survey development was good. Additional information on sampling... read more

The text book provides good information in certain areas, while not comprehensive information in other areas. The text provides practical information, especially the section on survey development was good. Additional information on sampling strategies would have been beneficial for the readers.

There are no errors.

Research method is a common topic and the fundamentals of it will not change over the years. Therefore, the book is relevant and will not become obsolete.

Clarity rating: 3

The text in the book is clear. Certain aspects of the text could have been presented more clearly. For example, the section on main effects and interactions are some concepts that students may have difficulty understanding. Those areas could be explained more clearly with an example.

Consistency rating: 3

Graphs in the book lacks titles and variable names. Also, the format of chapter title page needs to be consistent.

At times there were related topics spread across several chapters. This could be corrected for a better read by the audience..

The book text is very clear, and the flow from one topic to the next was adequate. However, having a outline would help the reader.

The PDF copy of the book was a easy read. There were few links that were missing though.

There were no grammatical errors.

The text is not offensive and examples in it are mostly based on historical US based experiments.

I would start of by saying that I am a supporter of the Open Textbook concept. In this day and age, there are a variety of Research Methods book/text available on the market. While this book covers research methods basics, it cannot be recommended in its current form as an acceptable alternative to the standard text. Modifications to the text as recommended by myself and other reviewers might improve the quality of this book in the future.

Reviewed by Meghan Babcock, Instructor, University of Texas at Arlington on 4/11/17

This text includes all important areas that are featured in other Research Methods textbooks and are presented in a logical order. The text includes great examples and provides the references which can be assigned as supplemental readings. In... read more

This text includes all important areas that are featured in other Research Methods textbooks and are presented in a logical order. The text includes great examples and provides the references which can be assigned as supplemental readings. In addition, the chapters end with exercises that can be completed in class or as part of a laboratory assignment. This text would be a great addition to a Research Methods course or an Introductory Statistics course for Psychology majors.

The content is accurate. I did not find any errors and the material is unbiased.

Yes - the content is up to date and would be easy to update if/when necessary.

The text is written at an appropriate level for undergraduate students and explains important terminology. The research studies that the author references are ones that undergraduate psychology majors should be familiar with. The only section that was questionable to me was that on multiple regression in section 8.3 (Complex Correlational Designs). I am unaware of other introductory Research Methods textbooks that cover this analysis, especially without describing simple regression first.

The text is consistent in terms of terminology. The framework is also consistent - the chapters begin with Learning Objectives and ends with Key Takeaways and Exercises.

The text is divisible into smaller reading sections - possibly too many. The sections are brief, and in some instances too brief (e.g., the section on qualitative research). I think that the section headers are helpful for instructors who plan on using this text in conjunction with another text in their course.

The topics were presented in a logical fashion and are similar to other published Research Methods texts. The writing is very clear and great examples are provided. I think that some of the sections are rather brief and more information and examples could be provided.

I did not see any interface issues. All of the links worked properly and the tables and figures were accurate and free of errors. I particularly liked the figures in section 5.2 on reliability of measurement.

There are three comments that I have about the interface, however. First, I was expecting the keywords in blue font to be linked to a glossary, but they were not. I would have appreciated this feature. Second, I read this text as a PDF on an iPad and this version lacking was the Table of Contents (TOC) feature. Although I was able to view the TOC in different versions, I would have appreciated it in the PDF version. Also, it would be nice if the TOC was clickable (i.e., you could click on a section and it automatically directed you to that section). Third, I think the reader of this text would benefit from a glossary at the end of each chapter and/or an index at the end of the text. The "Key Takeaways" sections at the end of each chapter were helpful, but I think that a glossary would be a nice addition as well.

I did not notice any grammatical errors of any kind. The text was easy to read and I think that undergraduate students would agree.

The text was not insensitive or offensive to any races, ethnicities, or backgrounds. I appreciated the section on avoiding biased language when writing manuscripts (e.g., using 'children with learning disabilities' instead of 'special children' or using 'African American' instead of 'minority').

I think that this text would be a nice addition to a Research Methods & Statistics course in psychology. There are some sections that I found particularly helpful: (1) 2.2 and 2.3 - the author gives detailed information about generating research questions and reviewing the literature; (2) 9.2 - this section focuses on constructing survey questionnaires; (3) 11.2 and 11.3 - the author talks about writing a research report and about presenting at conferences. These sections will be great additions to an undergraduate Research Methods course. The brief introduction to APA style was also helpful, but should be supplemented with the most recent APA style manual.

Reviewed by Shannon Layman, Lecturer, University of Texas at Arlington on 4/11/17

The sections in this textbook are overall more brief than in previous Methods texts that I have used. Sometimes this brevity is helpful in terms of getting to the point of the text and moving on. In other cases, some topics could use a bit more... read more

The sections in this textbook are overall more brief than in previous Methods texts that I have used. Sometimes this brevity is helpful in terms of getting to the point of the text and moving on. In other cases, some topics could use a bit more detail to establish a better foundation of the content before moving on to examples and/or the next topic.

I did not find any incorrect information or gross language issues.

Basic statistical and/or methodological texts tend to stay current and up-to-date because the topics in this field have not changed over the decades. Any updated methodologies would be found in a more advanced methods text.

The text is very clear and the ideas are easy to follow/ presented in a logical manner. The most helpful thing about this textbook is that the author arrives at the point of the topic very quickly. Another helpful point about this textbook is the relevancy of the examples used. The examples appear to be accessible to a wide audience and do not require specialization or previous knowledge of other fields of psychology.

I feel this text is very consistent throughout. The ideas build on each other and no terms are discussed in later chapters without being established in previous chapters.

Each chapter had multiple subsections which would allow for smaller reading sections throughout the course. The amount of content in each section and chapter appeared to be less than what I have encountered in other Methods texts.

The organization of the topics in this textbook follows the same or similar organization that I see in other textbooks. As I mentioned previously, the ideas build very well throughout the text.

I did not find any issues with navigation or distortion of the figures in the text.

There were not any obvious and/or egregious grammatical errors that I encountered in this text.

This topic is not really an issue with a Methods textbook as the topics are more so conceptual as opposed to topical. That being said, I did not see an issue with any examples used.

I have no other comments than what I addressed previously.

Reviewed by Sarah Allred, Associate Professor, Rutgers University, Camden on 2/8/17

Mixed. For some topics, there is more (and more practical) information than in most textbooks. I appreciated the very practical advice to students about how to plot data (in statistics chapters). Similarly, there is practical advice about how... read more

Mixed. For some topics, there is more (and more practical) information than in most textbooks. I appreciated the very practical advice to students about how to plot data (in statistics chapters). Similarly, there is practical advice about how to comply with ethical guidelines. The section on item development in surveys was very good.

On the other hand, there is far too little information about some subjects. For example, independent and dependent variables are introduced in passing in an early chapter and then referred to only much later in the text. In my experience, students have a surprisingly difficult time grasping this concept. Another important example is sampling; I would have preferred much more information on types of samples and sampling techniques, and the problems that arise from poor sampling. A third example is the introduction to basic experimental design. Variables, measurement, validity, and reliability are all introduced in one chapter.

I did not see an index or glossary.

I found no errors.

The fundamentals of research methods do not change much. Given the current replication crisis in psychology, it might be helpful to have something about replicability.

Mixed. The text itself is spare and clear. The style of the book is to explain a concept in very few words. There are some excellent aspects of this, but on the other hand, there are some concepts that students have a very difficult time undersatnding if they are not embedded in concrete examples. For example, the section on main effects and interactions shows bar graphs of interactions, but this is presented without variable names or axis titles, and separate from any specific experiment.

Sometimes the chapter stucture is laid out on the title page, and other times it is not. Some graphs lack titles and variable names.

The chapters can be stand alone, but sometimes I found conceptually similar pieces spread across several chapters, and conceptually different pieces in the same chapters.

The individual sentences and paragraphs are always very clear. However, I felt that more tables/outlines of major concepts would have been helpful. For example, perhaps a flow chart of different kinds of experimental designs would be useful. (See section on comprehensiveness for more about organization).

The flow from one topic to the next was adequate.

I read the pdf. Perhaps the interface is more pleasant on other devices, but I found the different formats and fonts in image/captions/main text/figure labels distracting. Many if the instances of apparently hyperlinked (blue) text to do not link to anything.

I found no grammatical errors, and prose is standard academic English.

Like most psychology textbooks available in the US, examples are focused on important experiments in US history.

I really wanted to be happy with this text. I am a supporter of the Open Textbook concept, and I wanted to find this book an acceptable alternative to the variety of Research Methods texts I’ve used. Unfortunately, I cannot recommend this book as superior in quality.

Reviewed by Joel Malin, Assistant Professor, Miami University on 8/21/16

This textbook covers all or nearly all of what I believe are important topics to provide an introduction to research methods in psychology. One minor issue is that the pdf version, which I reviewed, does not include an index or a glossary. As... read more

This textbook covers all or nearly all of what I believe are important topics to provide an introduction to research methods in psychology. One minor issue is that the pdf version, which I reviewed, does not include an index or a glossary. As such, it may be difficult for readers to zero in on material that they need, and/or to get a full sense of what will be covered and in what order.

I did not notice errors.

The book provides a solid overview of key issues related to introductory research methods, many of which are nearly timeless.

The writing is clear and accessible. It was easy and pleasing to read.

Terms are clearly defined and build upon each other as the book progresses.

I believe the text is organized in such a way that it could be easily divided into smaller sections.

The order in which material is presented seems to be well thought out and sensible.

I did not notice any issues with the interface. I reviewed the pdf version and thought the images were very helpful.

The book is written in a culturally relevant manner.

Reviewed by Abbey Dvorak, Assistant Professor, University of Kansas on 8/21/16

The text includes basic, essential information needed for students in an introductory research methods course. In addition, the text includes three chapters (i.e., research ethics, theory, and APA style) that are typically absent from or... read more

The text includes basic, essential information needed for students in an introductory research methods course. In addition, the text includes three chapters (i.e., research ethics, theory, and APA style) that are typically absent from or inadequately covered in similar texts. However, I did have some areas of concern regarding the coverage of qualitative and mixed methods approaches, and nonparametric tests. Although the author advocates for the research question to guide the choice of approach and design, minimal attention is given to the various qualitative designs (e.g., phenomenology, narrative, participatory action, etc.) beyond grounded theory and case studies, with no mention of the different types of mixed methods designs (e.g., concurrent, explanatory, exploratory) that are prevalent today. In addition, common nonparametric tests (e.g., Wilcoxon, Mann-Whitney, etc.) and parametric tests for categorical data (e.g., chi-square, Fisher’s exact, etc.) are not mentioned.

The text overall is accurate and free of errors. I noticed in the qualitative research sub-section, the author describes qualitative research in general, but does not mention common practices associated with qualitative research, such as transcribing interviews, coding data (e.g., different approaches to coding, different types of codes), and data analysis procedures. The information that is included appears accurate.

The text appears up-to-date and includes basic research information and classic examples that rarely change, which may allow the text to be used for many years. However, the author may want to add information about mixed methods research, a growing research approach, in order for the text to stay relevant across time.

The text includes clear, accessible, straightforward language with minimal jargon. When the author introduces a new term, the term is immediately defined and described. The author also provides interesting examples to clarify and expand understanding of terms and concepts throughout the text.

The text is internally consistent and uses similar language and vocabulary throughout. The author uses real-life examples across chapters in order to provide depth and insight into the information. In addition, the vocabulary, concepts, and organization are consistent with other research methods textbooks.

The modules are short, concise, and manageable for students; the material within each module is logically focused and related to each other. I may move the modules and the sub-topics within them into a slightly different order for my class, and add the information mentioned above, but overall, this is very good.

The author presents topics and structures chapters in a logical and organized manner. The epub and online version do not include page numbers in the text, but the pdf does; this may be confusing when referencing the text or answering student questions. The book ends somewhat abruptly after the chapter on inferential statistics; the text may benefit from a concluding chapter to bring everything together, perhaps with a culminating example that walks the reader through creating the research question, choosing a research approach/design, etc., all the way to writing the research report.

I used and compared the pdf, epub, and online versions of the text. The epub and online versions include a clickable table of contents, but the pdf does not. The table format is inconsistent across the three versions; in the epub version (viewed through ibooks), the table data does not always line up correctly, making it difficult to interpret quickly. In the pdf and online versions, the table format looks different, but the data are lined up. No index made it difficult to quickly find areas of interest in the text; however, I could use the Find/Search functions in all three versions to search and find needed items.

As I read through this text, I did not detect any glaring grammatical errors. Overall, I think the text is written quite well in a style that is accessible to students.

The author uses inclusive, person-first language, and the text does not seem to be offensive or insensitive. As I read, I did notice that topics such as diversity and cultural competency are absent.

I enjoyed reading this text and am very excited to have a free research methods text for my students that I may supplement as needed. I wish there was a test question bank and/or flashcards for my students to help them study, but perhaps that could be added in the future. Overall, this is a great resource!

Reviewed by Karen Pikula, Psychology Instructor PhD, Central Lakes College on 1/7/16

The text covers all the areas and ideas of the subject of research methods in psychology for the learner that is just entering the field. The authors cover all of the content of an introductory research methods textbook and use exemplary examples... read more

The text covers all the areas and ideas of the subject of research methods in psychology for the learner that is just entering the field. The authors cover all of the content of an introductory research methods textbook and use exemplary examples that make those concepts relevent to a beginning researcher. As the authors state, the material is presented in such a manner as to encourage learners to not only be effective consumers of current research but also engage as critical thinkers in the many diverse situations one encounters in everyday life.

The content is accurate, error free, and unbiased. It explains both quantiative and qualitative methods in an unbiased manner. It is a bit slim on qualitative. It would be nice to have a bit more information on, for example, creating interview questions, coding, and qualitative data anaylisis.

The text is up to date, having just been revised. This revision was authored by Rajiv Jhangiani (Capilano University, North Vancouver) and includes the addition of a table of contents and cover page that the original text did not have, changes to Chapter 3 (Research Ethics) to include a contemporary example of an ethical breach and to reflect Canadian ethical guidelines and privacy laws, additional information regarding online data collection in Chapter 9 (Survey Research). Jhangiani has correcte of errors in the text and formulae, as well as changing spelling from US to Canadian conventions. The text is also now available in a inexpensive hard copy which students can purchase online or college bookstores can stock. This makes the text current and updates should be minimal.

The text is very easy to read and also very interesting as the authors supplement content with amazing real life examples.

The text is internally consistent in terms of terminology and framework.

This text is easily and readily divisible into smaller reading sections that can be assigned at different points within a course. I am going to use this text in conjunction with the OER OpenStax Psychology text for my Honors Psychology course. I currently use the OER Openstax Psychology textbook for my Positive Psychology course as well as my General Psychology course,

The topics in the text are presented in logical and clear fashion. The way they are presented allows the text to be used in conjuction with other textbooks as a secondary resource.

The text is free of significant interface issues. It is written in a manner that follows the natural process of doing research.

The text contained no noted grammatical errors.

The text is not culturally insensitive or offensive and actually has been revised to accomodate Canadian ethical guidelines as well as those of the APA.

I have to say that I am excited to have found this revised edition. My students will be so happy that there is also a reasonable priced hard coopy for them to purchase. They love the OpenStax Psychology text with the hard copy available from our bookstore. I do wish there were PowerPoints available for the text as well as a test bank. That is always a bonus!

Reviewed by Alyssa Gibbons, Instructor, Colorado State University on 1/7/16

This text covers everything I would consider essential for a first course in research methods, including some areas that are not consistently found in introductory texts (e.g., qualitative research, criticisms of null hypothesis significance... read more

This text covers everything I would consider essential for a first course in research methods, including some areas that are not consistently found in introductory texts (e.g., qualitative research, criticisms of null hypothesis significance testing). The chapters on ethics (Ch. 3) and theory (Ch. 4) are more comprehensive than most I have seen at this level, but not to the extent of information overload; rather, they anticipate and address many questions that undergraduates often have about these issues.

There is no index or table of contents provided in the PDF, and the table of contents on the website is very broad, but the material is well organized and it would not be hard for an instructor to create such a table. Chapter 2.1 is intended to be an introduction to several key terms and ideas (e.g., variable, correlation) that could serve as a sort of glossary.

I found the text to be highly accurate throughout; terms are defined precisely and correctly.

Where there are controversies or differences of opinion in the field, the author presents both sides of the argument in a respectful and unbiased manner. He explicitly discourages students from dismissing any one approach as inherently flawed, discussing not only the advantages and disadvantages of all methods (including nonexperimental ones) but also ways researchers address the disadvantages.

In several places, the textbook explicitly addresses the history and development of various methods (e.g., qualitative research, null hypothesis significance testing) and the ways in which researchers' views have changed. This allows the author to present current thinking and debate in these areas yet still expose students to older ideas they are likely to encounter as they read the research literature. I think this approach sets students up well to encounter future methodological advances; as a field, we refine our methods over time. I think the author could easily integrate new developments in future editions, or instructors could introduce such developments as supplementary material without creating confusion by contradicting the test.

The examples are generally drawn from classic psychological studies that have held up well over time; I think they will appeal to students for some time to come and not appear dated.

The only area in which I did not feel the content was entirely up to date was in the area of psychological measurement; Chapter 5.2 is based on the traditional view and not the more comprehensive modern or holistic view as presented in the 1999 AERA/APA Standards for Educational and Psychological Measurement. However, a comprehensive treatment of measurement validity is probably not necessary for most undergraduates at this stage, and they will certainly encounter the older framework in the research literature.

The textbook does an excellent job of presenting concepts in simple, accessible language without introducing error by oversimplification. The author consistently anticipates common points of confusion, clarifies terms, and even suggests ways for students to remember key distinctions. Terms are clearly and concretely defined when they are introduced. In contrast to many texts I have used, the terms that are highlighted in the text are actually the terms I would want my students to remember and study; the author refrains from using psychological jargon that is not central to the concepts he is discussing.

I noticed no major inconsistencies or gaps.

The division of sections within each chapter is useful; although I liked the overall organization of the text, there were points at which I would likely assign sections in a slightly different order and I felt I could do this easily without loss of continuity. The one place I would have liked more modularity was in the discussion of inferential statistics: t-tests, ANOVA, and Pearson's r are all covered within Chapter 13.2. On the one hand, this enables students to see the relationships and similarities among these tests, but on the other, this is a lot for students to take in at once.

I found the overall organization of the book to be quite logical, mirroring the sequence of steps a researcher would use to develop a research question, design a study, etc. As discussed above, the modularity of the book makes it easy to reorder sections to suit the structure of a particular class (for example, I might have students read the section on APA writing earlier in the semester as they begin drafting their own research proposals). I like the inclusion of ethics very early on in the text, establishing the importance of this topic for all research design choices.

One organizational feature I particularly appreciated was the consistent integration of conceptual and practical ideas; for example, in the discussion of psychological measurement, reliability and validity are discussed alongside the importance of giving clear instructions and making sure participants cannot be identified by their writing implements. This gives students an accurate and honest picture of the research process - some of the choices we make are driven by scientific ideals and some are driven by practical lessons learned. Students often have questions related to these mundane aspects of conducting research and it is helpful to have them so clearly addressed.

Although I didn't encounter any problems per se with the interface, I do think it could be made more user-friendly. For example, references to figures and tables are highlighted in blue, appearing to be hyperlinks, but they were not. Having such links, as well as a linked, easily-navigable and detailed table of contents, would also be helpful (and useful to students who use assistive technology).

I noticed no grammatical errors.

Where necessary, the author uses inclusive language and there is nothing that seems clearly offensive. The examples generally reflect American psychology research, but the focus is on the methods used and not the participants or cultural context. The text could be more intentionally or proactively inclusive, but it is not insensitive or exclusive.

I am generally hard to please when it comes to textbooks, but I found very little to quibble with in this one. It is a very well-written and accessible introduction to research methods that meets students where they are, addressing their common questions, misconceptions, and concerns. Although it's not flashy, the figures, graphics, and extra resources provided are clear, helpful, and relevant.

Reviewed by Moin Syed, Assistant Professor, University of Minnesota on 6/10/15

The text is thorough in terms of covering introductory concepts that are central to experimental and correlational/association designs. I find the general exclusion of qualitative and mixed methods designs hard to defend (despite some researchers’... read more

The text is thorough in terms of covering introductory concepts that are central to experimental and correlational/association designs. I find the general exclusion of qualitative and mixed methods designs hard to defend (despite some researchers’ distaste for the methods). While these approaches were less commonly used in the recent past, they are prevalent in the early years of psychology and are ascending once again. It strikes me as odd to just ignore two whole families of methods that are used within the practice of psychology—definitely not a sustainable approach.

I do very much appreciate the emphasis on those who will both practice and consume psychology, given the wide variety of undergraduate career paths.

One glaring omission is a Table of Contents within the PDF. It would be nice to make this a linked PDF, so that clicking on the entry in a TOC (or cross-references) would jump the reader to the relevant section.

I did not see an errors. The chapter on theory is not as clear as it could be. The section “what is theory” is not very clear, and these are difficulte concepts (difference between theory, hypothesis, etc.). A bit more time spent here could have been good. Also, the discussion of functional, mechanistic, and typological theories leaves out the fourth of Pepper’s metaphors: contextualism. I’m not sure that was intentional and accidental, but it is noticeable!

This is a research methods text focused on experimental and association designs. The basics of these designs do not change a whole lot over time, so there is little likelihood that the main content will become obsolete anytime soon. Some of the examples used are a bit dated, but then again most of them are considered “classics” in the field, which I think are important to retain (and there is at least one “new classic” included in the ethics section, namely the fraudulent research linking autism to the MMR vaccine).

The text is extremely clear and accessible. In fact, it may even be *too* simple for undergraduate use. Then again, students often struggle with methods, so simplicity is good, and the simplicity can also make the book marketable to high school courses (although I doubt many high schools have methods courses).

Yes, quite consistent throughout. Carrying through the same examples into different chapters is a major strength of the text.

I don’ anticipate any problems here.

The book flows well, with brief sections. I do wonder if maybe the sections are too brief? Perhaps too many check-ins? The “key take-aways” usually come after only a few pages. As mentioned above, the book is written at a very basic level, so this brevity is consistent with that approach. It is not a problem, per se, but those considering adopting the text should be aware of this aspect.

No problems here.

I did not detect any grammatical errors. The text flows very well.

The book is fairly typical of American research methods books in that it only focuses on the U.S. context and draws its examples from “mainstream” psychology (e.g., little inclusion of ethnic minority or cross-cultural psychology). However, the text is certainly not insensitive or offensive in any way.

Nice book, thanks for writing it!

Reviewed by Rajiv Jhangiani, Instructor, Capilano University on 10/9/13

The text is well organized and written, integrates excellent pedagogical features, and covers all of the traditional areas of the topic admirably. The final two chapters provide a good bridge between the research methods course and the follow-up... read more

The text is well organized and written, integrates excellent pedagogical features, and covers all of the traditional areas of the topic admirably. The final two chapters provide a good bridge between the research methods course and the follow-up course on behavioural statistics. The text integrates real psychological measures, harnesses students' existing knowledge from introductory psychology, includes well-chosen examples from real life and research, and even includes a very practical chapter on the use of APA style for writing and referencing. On the other hand, it does not include a table of contents or an index, both of which are highly desirable. The one chapter that requires significant revision is Chapter 3 (Research Ethics), which is based on the US codes of ethics (e.g., Federal policy & APA code) and does not include any mention of the Canadian Tri-Council Policy Statement.

The very few errors I found include the following: 1. The text should read "The fact that his F score…" instead of "The fact that his t score…" on page 364 2. Some formulae are missing the line that separates the numerator from the denominator. See pages 306, 311, 315, and 361 3. Table 12.3 on page 310 lists the variance as 288 when it is 28.8

The text is up-to-date and will not soon lose relevance. The only things I would add are a brief discussion of the contemporary case of Diederik Stapel's research fraud in the chapter on Research Ethics, as well as some research concerning the external validity of web-based studies (e.g., Gosling et al.'s 2004 article in American Psychologist).

Overall, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Some constructive criticism: 1. When discussing z scores on page 311 it might have been helpful to point out that the mean and SD for a set of calculated z scores are 0 and 1 respectively. Good students will come to this realization themselves, but it is not a bad thing to point it out nonetheless. 2. The introduction of the concept of multiple regression might be difficult for some students to grasp. 3. The only place where I felt short of an explanation was in the use of a research example to demonstrate the use of a line graph on page 318. In this case the explanation in question does not pertain to the line graph itself but the result of the study used, which is so fascinating that students will wish for the researchers' explanation for it.

The text is internally consistent.

The text is organized very well into chapters, modules within each chapter, and learning objectives within each module. Each module also includes useful exercises that help consolidate learning.

As mentioned earlier, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Only rarely did I feel that the author could have assisted the student by demonstrating the set-by-step calculation of a statistic (e.g., on page 322 for the calculation of Pearson's r)

The images, graphs, and charts are clear. The only serious issues that hamper navigation are the lack of a table of contents and an index. Many of the graphs will need to be printed in colour (or otherwise modified) for the students to follow the explanations provided in the text.

The text is written rather well and is free from grammatical errors. Of course, spellings are in the US convention.

The text is not culturally insensitive or offensive. Of course, it is not a Canadian edition and so many of the examples (all of which are easy to comprehend) come from a US context.

I have covered most of these issues in my earlier comments. The only things left to mention are that the author should have clearly distinguished between mundane and psychological realism, and that, in my opinion, the threats to internal validity could have been grouped together and might have been closer to an exhaustive list. This review originated in the BC Open Textbook Collection and is licensed under CC BY-ND.

Table of Contents

• Chapter 1: The Science of Psychology
• Chapter 2: Overview of the Scientific Method
• Chapter 3: Research Ethics
• Chapter 4: Psychological Measurement
• Chapter 5: Experimental Research
• Chapter 6: Non-experimental Research
• Chapter 7: Survey Research
• Chapter 8: Quasi-Experimental Research
• Chapter 9: Factorial Designs
• Chapter 10: Single-Subject Research
• Chapter 11: Presenting Your Research
• Chapter 12: Descriptive Statistics
• Chapter 13: Inferential Statistics

Ancillary Material

• Kwantlen Polytechnic University

About the Book

This fourth edition (published in 2019) was co-authored by Rajiv S. Jhangiani (Kwantlen Polytechnic University), Carrie Cuttler (Washington State University), and Dana C. Leighton (Texas A&M University—Texarkana) and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Revisions throughout the current edition include changing the chapter and section numbering system to better accommodate adaptions that remove or reorder chapters; continued reversion from the Canadian edition; general grammatical edits; replacement of “he/she” to “they” and “his/her” to “their”; removal or update of dead links; embedded videos that were not embedded; moved key takeaways and exercises from the end of each chapter section to the end of each chapter; a new cover design.

About the Contributors

Dr. Carrie Cuttler received her Ph.D. in Psychology from the University of British Columbia. She has been teaching research methods and statistics for over a decade. She is currently an Assistant Professor in the Department of Psychology at Washington State University, where she primarily studies the acute and chronic effects of cannabis on cognition, mental health, and physical health. Dr. Cuttler was also an OER Research Fellow with the Center for Open Education and she conducts research on open educational resources. She has over 50 publications including the following two published books:  A Student Guide for SPSS (1st and 2nd edition)  and  Research Methods in Psychology: Student Lab Guide.  Finally, she edited another OER entitled  Essentials of Abnormal Psychology. In her spare time, she likes to travel, hike, bike, run, and watch movies with her husband and son. You can find her online at @carriecuttler or carriecuttler.com.

Dr. Rajiv Jhangiani is the Associate Vice Provost, Open Education at Kwantlen Polytechnic University in British Columbia. He is an internationally known advocate for open education whose research and practice focuses on open educational resources, student-centered pedagogies, and the scholarship of teaching and learning. Rajiv is a co-founder of the Open Pedagogy Notebook, an Ambassador for the Center for Open Science, and serves on the BC Open Education Advisory Committee. He formerly served as an Open Education Advisor and Senior Open Education Research & Advocacy Fellow with BCcampus, an OER Research Fellow with the Open Education Group, a Faculty Workshop Facilitator with the Open Textbook Network, and a Faculty Fellow with the BC Open Textbook Project. A co-author of three open textbooks in Psychology, his most recent book is  Open: The Philosophy and Practices that are Revolutionizing Education and Science (2017). You can find him online at @thatpsychprof or thatpsychprof.com.

Dr. Dana C. Leighton is Assistant Professor of Psychology in the College of Arts, Science, and Education at Texas A&M University—Texarkana. He earned his Ph.D. from the University of Arkansas, and has 15 years experience teaching across the psychology curriculum at community colleges, liberal arts colleges, and research universities. Dr. Leighton’s social psychology research lab studies intergroup relations, and routinely includes undergraduate students as researchers. He is also Chair of the university’s Institutional Review Board. Recently he has been researching and writing about the use of open science research practices by undergraduate researchers to increase diversity, justice, and sustainability in psychological science. He has published on his teaching methods in eBooks from the Society for the Teaching of Psychology, presented his methods at regional and national conferences, and received grants to develop new teaching methods. His teaching interests are in undergraduate research, writing skills, and online student engagement. For more about Dr. Leighton see http://www.danaleighton.net and http://danaleighton.edublogs.org

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Chapter 6: Experimental Research

6.1 Experiment Basics 6.2 Experimental Design 6.3 Conducting Experiments

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

5.1 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an  experiment  is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable  cause  a change in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse  these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate  an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as  conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions is often referred to as a  single factor two-level design.  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design.  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to  control  extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 5.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• An extraneous variable is any variable other than the independent and dependent variables. A confound is an extraneous variable that varies systematically with the independent variable.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

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Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an  experiment  is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable  cause  changes in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse  these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Four Big Validities

When we read about psychology experiments with a critical view, one question to ask is “is this study valid?” However, that question is not as straightforward as it seems because in psychology, there are many different kinds of validities. Researchers have focused on four validities to help assess whether an experiment is sound (Judd & Kenny, 1981; Morling, 2014) [1] [2] : internal validity, external validity, construct validity, and statistical validity. We will explore each validity in depth.

Internal Validity

Recall that two variables being statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this implication would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise   and  be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The logic is based on this assumption : If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just  one  difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this difference in belief must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in  internal validity  if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial (Bauman, McGraw, Bartels, & Warren, 2014) [3] . In many psychology experiments, the participants are all undergraduate students and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had undergraduate students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998) [4] . At first, this manipulation might seem silly. When will undergraduate students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity . An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter everyday, often described as mundane realism . Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity and have high mundane realism if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this increase would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of undergraduate students in a laboratory at a selective university who merely judged the appeal of various colours presented on a computer screen; however, this study would have high psychological realism where the same mental process is used in both the laboratory and in the real world.  If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this preference is relevant to grocery shoppers’ cereal-buying decisions because of low external validity but they could be confident that the visual processing of colours has high psychological realism.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments  that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005) [5] . These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes  that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this gender difference was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Construct Validity

In addition to the generalizability of the results of an experiment, another element to scrutinize in a study is the quality of the experiment’s manipulations, or the construct validity . The research question that Darley and Latané started with is “does helping behaviour become diffused?” They hypothesized that participants in a lab would be less likely to help when they believed there were more potential helpers besides themselves. This conversion from research question to experiment design is called operationalization (see Chapter 2 for more information about the operational definition). Darley and Latané operationalized the independent variable of diffusion of responsibility by increasing the number of potential helpers. In evaluating this design, we would say that the construct validity was very high because the experiment’s manipulations very clearly speak to the research question; there was a crisis, a way for the participant to help, and increasing the number of other students involved in the discussion, they provided a way to test diffusion.

What if the number of conditions in Darley and Latané’s study changed? Consider if there were only two conditions: one student involved in the discussion or two. Even though we may see a decrease in helping by adding another person, it may not be a clear demonstration of diffusion of responsibility, just merely the presence of others. We might think it was a form of Bandura’s social inhibition  (discussed in Chapter 4). The construct validity would be lower. However, had there been five conditions, perhaps we would see the decrease continue with more people in the discussion or perhaps it would plateau after a certain number of people. In that situation, we may not necessarily be learning more about diffusion of responsibility or it may become a different phenomenon. By adding more conditions, the construct validity may not get higher. When designing your own experiment, consider how well the research question is operationalized your study.

Statistical Validity

A common critique of experiments is that a study did not have enough participants. The main reason for this criticism is that it is difficult to generalize about a population from a small sample. At the outset, it seems as though this critique is about external validity but there are studies where small sample sizes are not a problem (Chapter 10 will discuss how small samples, even of only 1 person, are still very illuminating for psychology research). Therefore, small sample sizes are actually a critique of statistical validity . The statistical validity speaks to whether the statistics conducted in the study support the conclusions that are made.

Proper statistical analysis should be conducted on the data to determine whether the difference or relationship that was predicted was found. The number of conditions and the number of total participants will determine the overall size of the effect. With this information, a power analysis can be conducted to ascertain whether you are likely to find a real difference. When designing a study, it is best to think about the power analysis so that the appropriate number of participants can be recruited and tested (more on effect sizes in Chapter 12). To design a statistically valid experiment, thinking about the statistical tests at the beginning of the design will help ensure the results can be believed.

Prioritizing Validities

These four big validities—internal, external, construct, and statistical—are useful to keep in mind when both reading about other experiments and designing your own. However, researchers must prioritize and often it is not possible to have high validity in all four areas. In Cialdini’s study on towel usage in hotels, the external validity was high but the statistical validity was more modest. This discrepancy does not invalidate the study but it shows where there may be room for improvement for future follow-up studies (Goldstein, Cialdini, & Griskevicius, 2008) [6] . Morling (2014) points out that most psychology studies have high internal and construct validity but sometimes sacrifice external validity.

Manipulation of the Independent Variable

Again, to  manipulate  an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as  conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check  in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to  control  extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 6.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 6.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 6.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 6.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.
• Judd, C.M. & Kenny, D.A. (1981). Estimating the effects of social interventions . Cambridge, MA: Cambridge University Press. ↵
• Morling, B. (2014, April). Teach your students to be better consumers. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2014/april-14/teach-your-students-to-be-better-consumers.html ↵
• Bauman, C.W., McGraw, A.P., Bartels, D.M., & Warren, C. (2014). Revisiting external validity: Concerns about trolley problems and other sacrificial dilemmas in moral psychology. Social and Personality Psychology Compass, 8/9 , 536-554. ↵
• Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284. ↵
• Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2005/april-05/dont-throw-in-the-towel-use-social-influence-research.html ↵
• Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35 , 472–482. ↵

Research Methods in Psychology Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research methods in psychology.

This course covers foundations of the research process for experimental Psychology: reviewing and evaluating published journal articles, refining new research questions, conducting pilot studies, creating stimuli, sequencing experiments for optimal control and data quality, analyzing data, and communicating scientific methods and results clearly, effectively, and professionally in APA style. Lectures survey time-tested excellent methods, and labs provide opportunities to recreate interesting experiments and innovate, building toward an original research final project.

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How to Conduct a Psychology Experiment

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Conducting your first psychology experiment can be a long, complicated, and sometimes intimidating process. It can be especially confusing if you are not quite sure where to begin or which steps to take.

Like other sciences, psychology utilizes the  scientific method  and bases conclusions upon empirical evidence. When conducting an experiment, it is important to follow the seven basic steps of the scientific method:

• Ask a testable question
• Define your variables
• Conduct background research
• Design your experiment
• Perform the experiment
• Collect and analyze the data
• Draw conclusions
• Share the results with the scientific community

At a Glance

It's important to know the steps of the scientific method if you are conducting an experiment in psychology or other fields. The processes encompasses finding a problem you want to explore, learning what has already been discovered about the topic, determining your variables, and finally designing and performing your experiment. But the process doesn't end there! Once you've collected your data, it's time to analyze the numbers, determine what they mean, and share what you've found.

Find a Research Problem or Question

Picking a research problem can be one of the most challenging steps when you are conducting an experiment. After all, there are so many different topics you might choose to investigate.

Are you stuck for an idea? Consider some of the following:

Investigate a Commonly Held Belief

Folk knowledge is a good source of questions that can serve as the basis for psychological research. For example, many people believe that staying up all night to cram for a big exam can actually hurt test performance.

You could conduct a study to compare the test scores of students who stayed up all night with the scores of students who got a full night's sleep before the exam.

Review Psychology Literature

Published studies are a great source of unanswered research questions. In many cases, the authors will even note the need for further research. Find a published study that you find intriguing, and then come up with some questions that require further exploration.

Think About Everyday Problems

There are many practical applications for psychology research. Explore various problems that you or others face each day, and then consider how you could research potential solutions. For example, you might investigate different memorization strategies to determine which methods are most effective.

Define Your Variables

Variables are anything that might impact the outcome of your study. An operational definition describes exactly what the variables are and how they are measured within the context of your study.

For example, if you were doing a study on the impact of sleep deprivation on driving performance, you would need to operationally define sleep deprivation and driving performance .

An operational definition refers to a precise way that an abstract concept will be measured. For example, you cannot directly observe and measure something like test anxiety . You can, however, use an anxiety scale and assign values based on how many anxiety symptoms a person is experiencing. 

In this example, you might define sleep deprivation as getting less than seven hours of sleep at night. You might define driving performance as how well a participant does on a driving test.

What is the purpose of operationally defining variables? The main purpose is control. By understanding what you are measuring, you can control for it by holding the variable constant between all groups or manipulating it as an independent variable .

Develop a Hypothesis

The next step is to develop a testable hypothesis that predicts how the operationally defined variables are related. In the recent example, the hypothesis might be: "Students who are sleep-deprived will perform worse than students who are not sleep-deprived on a test of driving performance."

Null Hypothesis

In order to determine if the results of the study are significant, it is essential to also have a null hypothesis. The null hypothesis is the prediction that one variable will have no association to the other variable.

In other words, the null hypothesis assumes that there will be no difference in the effects of the two treatments in our experimental and control groups .

The null hypothesis is assumed to be valid unless contradicted by the results. The experimenters can either reject the null hypothesis in favor of the alternative hypothesis or not reject the null hypothesis.

It is important to remember that not rejecting the null hypothesis does not mean that you are accepting the null hypothesis. To say that you are accepting the null hypothesis is to suggest that something is true simply because you did not find any evidence against it. This represents a logical fallacy that should be avoided in scientific research.  

Conduct Background Research

Once you have developed a testable hypothesis, it is important to spend some time doing some background research. What do researchers already know about your topic? What questions remain unanswered?

You can learn about previous research on your topic by exploring books, journal articles, online databases, newspapers, and websites devoted to your subject.

Reading previous research helps you gain a better understanding of what you will encounter when conducting an experiment. Understanding the background of your topic provides a better basis for your own hypothesis.

After conducting a thorough review of the literature, you might choose to alter your own hypothesis. Background research also allows you to explain why you chose to investigate your particular hypothesis and articulate why the topic merits further exploration.

As you research the history of your topic, take careful notes and create a working bibliography of your sources. This information will be valuable when you begin to write up your experiment results.

Select an Experimental Design

After conducting background research and finalizing your hypothesis, your next step is to develop an experimental design. There are three basic types of designs that you might utilize. Each has its own strengths and weaknesses:

Pre-Experimental Design

A single group of participants is studied, and there is no comparison between a treatment group and a control group. Examples of pre-experimental designs include case studies (one group is given a treatment and the results are measured) and pre-test/post-test studies (one group is tested, given a treatment, and then retested).

Quasi-Experimental Design

This type of experimental design does include a control group but does not include randomization. This type of design is often used if it is not feasible or ethical to perform a randomized controlled trial.

True Experimental Design

A true experimental design, also known as a randomized controlled trial, includes both of the elements that pre-experimental designs and quasi-experimental designs lack—control groups and random assignment to groups.

Standardize Your Procedures

In order to arrive at legitimate conclusions, it is essential to compare apples to apples.

Each participant in each group must receive the same treatment under the same conditions.

For example, in our hypothetical study on the effects of sleep deprivation on driving performance, the driving test must be administered to each participant in the same way. The driving course must be the same, the obstacles faced must be the same, and the time given must be the same.

Choose Your Participants

In addition to making sure that the testing conditions are standardized, it is also essential to ensure that your pool of participants is the same.

If the individuals in your control group (those who are not sleep deprived) all happen to be amateur race car drivers while your experimental group (those that are sleep deprived) are all people who just recently earned their driver's licenses, your experiment will lack standardization.

When choosing subjects, there are some different techniques you can use.

Simple Random Sample

In a simple random sample, the participants are randomly selected from a group. A simple random sample can be used to represent the entire population from which the representative sample is drawn.

Drawing a simple random sample can be helpful when you don't know a lot about the characteristics of the population.

Stratified Random Sample

Participants must be randomly selected from different subsets of the population. These subsets might include characteristics such as geographic location, age, sex, race, or socioeconomic status.

Stratified random samples are more complex to carry out. However, you might opt for this method if there are key characteristics about the population that you want to explore in your research.

Conduct Tests and Collect Data

After you have selected participants, the next steps are to conduct your tests and collect the data. Before doing any testing, however, there are a few important concerns that need to be addressed.

Address Ethical Concerns

First, you need to be sure that your testing procedures are ethical . Generally, you will need to gain permission to conduct any type of testing with human participants by submitting the details of your experiment to your school's Institutional Review Board (IRB), sometimes referred to as the Human Subjects Committee.

Obtain Informed Consent

After you have gained approval from your institution's IRB, you will need to present informed consent forms to each participant. This form offers information on the study, the data that will be gathered, and how the results will be used. The form also gives participants the option to withdraw from the study at any point in time.

Once this step has been completed, you can begin administering your testing procedures and collecting the data.

Analyze the Results

After collecting your data, it is time to analyze the results of your experiment. Researchers use statistics to determine if the results of the study support the original hypothesis and if the results are statistically significant.

Statistical significance means that the study's results are unlikely to have occurred simply by chance.

The types of statistical methods you use to analyze your data depend largely on the type of data that you collected. If you are using a random sample of a larger population, you will need to utilize inferential statistics.

These statistical methods make inferences about how the results relate to the population at large.

Because you are making inferences based on a sample, it has to be assumed that there will be a certain margin of error. This refers to the amount of error in your results. A large margin of error means that there will be less confidence in your results, while a small margin of error means that you are more confident that your results are an accurate reflection of what exists in that population.

Share Your Results After Conducting an Experiment

Your final task in conducting an experiment is to communicate your results. By sharing your experiment with the scientific community, you are contributing to the knowledge base on that particular topic.

One of the most common ways to share research results is to publish the study in a peer-reviewed professional journal. Other methods include sharing results at conferences, in book chapters, or academic presentations.

In your case, it is likely that your class instructor will expect a formal write-up of your experiment in the same format required in a professional journal article or lab report :

• Introduction
• Tables and figures

What This Means For You

Designing and conducting a psychology experiment can be quite intimidating, but breaking the process down step-by-step can help. No matter what type of experiment you decide to perform, always check with your instructor and your school's institutional review board for permission before you begin.

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Purna Singh A, Vadakedath S, Kandi V. Clinical research: A review of study designs, hypotheses, errors, sampling types, ethics, and informed consent .  Cureus . 2023;15(1):e33374. doi:10.7759/cureus.33374

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Miller CJ, Smith SN, Pugatch M. Experimental and quasi-experimental designs in implementation research .  Psychiatry Res . 2020;283:112452. doi:10.1016/j.psychres.2019.06.027

Nijhawan LP, Manthan D, Muddukrishna BS, et. al. Informed consent: Issues and challenges . J Adv Pharm Technol Rese . 2013;4(3):134-140. doi:10.4103/2231-4040.116779

Serdar CC, Cihan M, Yücel D, Serdar MA. Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies .  Biochem Med (Zagreb) . 2021;31(1):010502. doi:10.11613/BM.2021.010502

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Experimental Methods In Psychology

March 7, 2021 - paper 2 psychology in context | research methods.

There are three experimental methods in the field of psychology; Laboratory, Field and Natural Experiments. Each of the experimental methods holds different characteristics in relation to; the manipulation of the IV, the control of the EVs and the ability to accurately replicate the study in exactly the same way.

When conducting research, it is important to create an aim and a hypothesis,  click here  to learn more about the formation of aims and hypotheses.

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Experimental Research

23 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.

What Is an Experiment?

As we saw earlier in the book, an  experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in one variable (referred to as an independent variable ) cause a change in another variable (referred to as a dependent variable ). Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher exerts control over, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions are often referred to as a single factor two-level design .  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design .  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s experiment used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres [1] . Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger lesbian women would apply to older gay men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Treatment and Control Conditions

In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This intervention includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bed sheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008) [2] .

Placebo effects are interesting in their own right (see Note “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 5.2 shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 5.2 ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This difference is what is shown by a comparison of the two outer bars in Figure 5.4 .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a wait-list control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This disclosure allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999) [3] . There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002) [4] . The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. Note that the IRB would have carefully considered the use of deception in this case and judged that the benefits of using it outweighed the risks and that there was no other way to answer the research question (about the effectiveness of a placebo procedure) without it. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

• Knecht, S., Dräger, B., Deppe, M., Bobe, L., Lohmann, H., Flöel, A., . . . Henningsen, H. (2000). Handedness and hemispheric language dominance in healthy humans. Brain: A Journal of Neurology, 123 (12), 2512-2518. http://dx.doi.org/10.1093/brain/123.12.2512 ↵
• Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590. ↵
• Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press. ↵
• Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88. ↵

A type of study designed specifically to answer the question of whether there is a causal relationship between two variables.

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

The different levels of the independent variable to which participants are assigned.

Holding extraneous variables constant in order to separate the effect of the independent variable from the effect of the extraneous variables.

Any variable other than the dependent and independent variable.

Changing the level, or condition, of the independent variable systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times.

An experiment design involving a single independent variable with two conditions.

When an experiment has one independent variable that is manipulated to produce more than two conditions.

An extraneous variable that varies systematically with the independent variable, and thus confuses the effect of the independent variable with the effect of the extraneous one.

Any intervention meant to change people’s behavior for the better.

The condition in which participants receive the treatment.

The condition in which participants do not receive the treatment.

An experiment that researches the effectiveness of psychotherapies and medical treatments.

The condition in which participants receive no treatment whatsoever.

A simulated treatment that lacks any active ingredient or element that is hypothesized to make the treatment effective, but is otherwise identical to the treatment.

An effect that is due to the placebo rather than the treatment.

Condition in which the participants receive a placebo rather than the treatment.

Condition in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 6: Experimental Research

Experimental Design

Learning Objectives

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a  between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university  students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called  random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence.  Table 6.2  shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a  treatment  is any intervention meant to change people’s behaviour for the better. This  intervention  includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a  treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a  no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A  placebo  is a simulated treatment that lacks any active ingredient or element that should make it effective, and a  placebo effect  is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008) [1] .

Placebo effects are interesting in their own right (see  Note “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works.  Figure 6.2  shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in  Figure 6.2 ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This  difference  is what is shown by a comparison of the two outer bars in  Figure 6.2 .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This disclosure allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999) [2] . There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002) [3] . The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Within-Subjects Experiments

In a within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.  However, not all experiments can use a within-subjects design nor would it be desirable to.

Carryover Effects and Counterbalancing

The primary disad vantage of within-subjects designs is that they can result in carryover effects. A  carryover effect  is an effect of being tested in one condition on participants’ behaviour in later conditions. One type of carryover effect is a  practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This  type of effect  is called a  context effect . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This  knowledge  could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is  counterbalancing , which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

An efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 is “larger” than 221

Researcher Michael Birnbaum has argued that the lack of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”.  One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [4] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this difference is because participants spontaneously compared 9 with other one-digit numbers (in which case it is relatively large) and compared 221 with other three-digit numbers (in which case it is relatively small) .

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this design is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This difficulty is true for many designs that involve a treatment meant to produce long-term change in participants’ behaviour (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often take exactly this type of mixed methods approach.

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
• Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.
• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g.,  dog ) are recalled better than abstract nouns (e.g.,  truth ).
• Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.
• Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590. ↵
• Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press. ↵
• Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88. ↵
• Birnbaum, M.H. (1999). How to show that 9>221: Collect judgments in a between-subjects design. Psychological Methods, 4(3), 243-249. ↵

An experiment in which each participant is only tested in one condition.

A method of controlling extraneous variables across conditions by using a random process to decide which participants will be tested in the different conditions.

All the conditions of an experiment occur once in the sequence before any of them is repeated.

Any intervention meant to change people’s behaviour for the better.

A condition in a study where participants receive treatment.

A condition in a study that the other condition is compared to. This group does not receive the treatment or intervention that the other conditions do.

A type of experiment to research the effectiveness of psychotherapies and medical treatments.

A type of control condition in which participants receive no treatment.

A simulated treatment that lacks any active ingredient or element that should make it effective.

A positive effect of a treatment that lacks any active ingredient or element to make it effective.

Participants receive a placebo that looks like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness.

Participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it.

Each participant is tested under all conditions.

An effect of being tested in one condition on participants’ behaviour in later conditions.

Participants perform a task better in later conditions because they have had a chance to practice it.

Participants perform a task worse in later conditions because they become tired or bored.

Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions.

Testing different participants in different orders.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental Psychology Studies Humans and Animals

Understanding Experimental Psychology

Our personalities, and to some degree our life experiences, are defined by the way we behave. But what influences the way we behave in the first place? How does our behavior shape our experiences throughout our lives? 

Experimental psychologists are interested in exploring theoretical questions, often by creating a hypothesis and then setting out to prove or disprove it through experimentation. They study a wide range of behavioral topics among humans and animals, including sensation, perception, attention, memory, cognition and emotion.

Experimental Psychology Applied

Experimental psychologists use scientific methods to collect data and perform research. Often, their work builds, one study at a time, to a larger finding or conclusion. Some researchers have devoted their entire career to answering one complex research question. 

These psychologists work in a variety of settings, including universities, research centers, government agencies and private businesses. The focus of their research is as varied as the settings in which they work. Often, personal interest and educational background will influence the research questions they choose to explore. 

In a sense, all psychologists can be considered experimental psychologists since research is the foundation of the discipline, and many psychologists split their professional focus among research, patient care, teaching or program administration. Experimental psychologists, however, often devote their full attention to research — its design, execution, analysis and dissemination. 

Those focusing their careers specifically on experimental psychology contribute work across subfields . For example, they use scientific research to provide insights that improve teaching and learning, create safer workplaces and transportation systems, improve substance abuse treatment programs and promote healthy child development.

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Chapter 6: Experimental Research

Experiment basics, learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an  experiment  is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable  cause  changes in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse  these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Four Big Validities

When we read about psychology experiments with a critical view, one question to ask is “is this study valid?” However, that question is not as straightforward as it seems because in psychology, there are many different kinds of validities. Researchers have focused on four validities to help assess whether an experiment is sound (Judd & Kenny, 1981; Morling, 2014) [1] [2] : internal validity, external validity, construct validity, and statistical validity. We will explore each validity in depth.

Internal Validity

Recall that two variables being statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this implication would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise   and  be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The logic is based on this assumption : If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just  one  difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this difference in belief must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in  internal validity  if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial (Bauman, McGraw, Bartels, & Warren, 2014) [3] . In many psychology experiments, the participants are all undergraduate students and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had undergraduate students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998) [4] . At first, this manipulation might seem silly. When will undergraduate students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity . An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter everyday, often described as mundane realism . Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity and have high mundane realism if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this increase would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of undergraduate students in a laboratory at a selective university who merely judged the appeal of various colours presented on a computer screen; however, this study would have high psychological realism where the same mental process is used in both the laboratory and in the real world.  If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this preference is relevant to grocery shoppers’ cereal-buying decisions because of low external validity but they could be confident that the visual processing of colours has high psychological realism.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments  that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005) [5] . These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes  that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this gender difference was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Construct Validity

In addition to the generalizability of the results of an experiment, another element to scrutinize in a study is the quality of the experiment’s manipulations, or the construct validity . The research question that Darley and Latané started with is “does helping behaviour become diffused?” They hypothesized that participants in a lab would be less likely to help when they believed there were more potential helpers besides themselves. This conversion from research question to experiment design is called operationalization (see Chapter 2 for more information about the operational definition). Darley and Latané operationalized the independent variable of diffusion of responsibility by increasing the number of potential helpers. In evaluating this design, we would say that the construct validity was very high because the experiment’s manipulations very clearly speak to the research question; there was a crisis, a way for the participant to help, and increasing the number of other students involved in the discussion, they provided a way to test diffusion.

What if the number of conditions in Darley and Latané’s study changed? Consider if there were only two conditions: one student involved in the discussion or two. Even though we may see a decrease in helping by adding another person, it may not be a clear demonstration of diffusion of responsibility, just merely the presence of others. We might think it was a form of Bandura’s social inhibition  (discussed in Chapter 4 ). The construct validity would be lower. However, had there been five conditions, perhaps we would see the decrease continue with more people in the discussion or perhaps it would plateau after a certain number of people. In that situation, we may not necessarily be learning more about diffusion of responsibility or it may become a different phenomenon. By adding more conditions, the construct validity may not get higher. When designing your own experiment, consider how well the research question is operationalized your study.

Statistical Validity

A common critique of experiments is that a study did not have enough participants. The main reason for this criticism is that it is difficult to generalize about a population from a small sample. At the outset, it seems as though this critique is about external validity but there are studies where small sample sizes are not a problem ( Chapter 10 will discuss how small samples, even of only 1 person, are still very illuminating for psychology research). Therefore, small sample sizes are actually a critique of statistical validity . The statistical validity speaks to whether the statistics conducted in the study support the conclusions that are made.

Proper statistical analysis should be conducted on the data to determine whether the difference or relationship that was predicted was found. The number of conditions and the number of total participants will determine the overall size of the effect. With this information, a power analysis can be conducted to ascertain whether you are likely to find a real difference. When designing a study, it is best to think about the power analysis so that the appropriate number of participants can be recruited and tested (more on effect sizes in Chapter 12 ). To design a statistically valid experiment, thinking about the statistical tests at the beginning of the design will help ensure the results can be believed.

Prioritizing Validities

These four big validities–internal, external, construct, and statistical–are useful to keep in mind when both reading about other experiments and designing your own. However, researchers must prioritize and often it is not possible to have high validity in all four areas. In Cialdini’s study on towel usage in hotels, the external validity was high but the statistical validity was more modest. This discrepancy does not invalidate the study but it shows where there may be room for improvement for future follow-up studies (Goldstein, Cialdini, & Griskevicius, 2008) [6] . Morling (2014) points out that most psychology studies have high internal and construct validity but sometimes sacrifice external validity.

Manipulation of the Independent Variable

Again, to  manipulate  an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as  conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check  in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to  control  extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 6.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 6.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 6.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 6.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.
• Judd, C.M. & Kenny, D.A. (1981). Estimating the effects of social interventions . Cambridge, MA: Cambridge University Press. ↵
• Morling, B. (2014, April). Teach your students to be better consumers. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2014/april-14/teach-your-students-to-be-better-consumers.html ↵
• Bauman, C.W., McGraw, A.P., Bartels, D.M., & Warren, C. (2014). Revisiting external validity: Concerns about trolley problems and other sacrificial dilemmas in moral psychology. Social and Personality Psychology Compass, 8/9 , 536-554. ↵
• Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284. ↵
• Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2005/april-05/dont-throw-in-the-towel-use-social-influence-research.html ↵
• Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35 , 472–482. ↵
• Research Methods in Psychology. Authored by : Paul C. Price, Rajiv S. Jhangiani, and I-Chant A. Chiang. Provided by : BCCampus. Located at : https://opentextbc.ca/researchmethods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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