difference between a case study and survey

Case Study vs. Survey: What's the Difference?

Key Differences

Comparison chart, methodology, generalizability, case study and survey definitions, what is the purpose of a case study, what is a case study, can case studies be generalized, can case studies be biased, are case studies credible, how is data collected in a case study, how long does a case study take, what is a survey, are case studies qualitative or quantitative, what fields use case studies, are surveys qualitative or quantitative, how are survey results analyzed, what challenges are associated with surveys, can surveys predict behavior, what makes a good case study, what types of surveys exist, what is a good response rate for a survey, what is the purpose of a survey, can surveys be biased, how are surveys conducted.

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Case Studies vs. Surveys

What's the difference.

Case studies and surveys are both research methods used in various fields to gather information and insights. However, they differ in their approach and purpose. Case studies involve in-depth analysis of a specific individual, group, or situation, aiming to understand the complexities and unique aspects of the subject. They provide detailed qualitative data and allow researchers to explore causal relationships. On the other hand, surveys involve collecting data from a larger sample size through standardized questionnaires or interviews. Surveys are more focused on obtaining quantitative data and generalizing findings to a larger population. While case studies offer rich and detailed information, surveys provide a broader perspective and statistical analysis. Ultimately, the choice between these methods depends on the research objectives and the nature of the research question.

Further Detail

Introduction.

When conducting research, it is essential to choose the most appropriate method to gather data and analyze information. Two commonly used research methods are case studies and surveys. Both methods have their own unique attributes and can provide valuable insights, but they differ in terms of their approach, data collection, and analysis techniques. In this article, we will explore the attributes of case studies and surveys, highlighting their strengths and limitations.

Case Studies

Case studies are an in-depth examination of a particular individual, group, or phenomenon. They involve a comprehensive analysis of a specific case, often using multiple sources of data such as interviews, observations, and documents. Case studies are particularly useful when researchers aim to understand complex social phenomena or explore rare events. They provide a detailed and holistic view of the subject under investigation.

One of the key attributes of case studies is their ability to generate rich and detailed qualitative data. By using various data collection methods, researchers can gather a wide range of information, including personal experiences, attitudes, and behaviors. This depth of data allows for a comprehensive understanding of the case, capturing nuances and complexities that may not be captured by other research methods.

Furthermore, case studies are often conducted in real-world settings, providing a high level of ecological validity. Researchers can observe and analyze the subject within its natural context, which enhances the external validity of the findings. This attribute is particularly valuable when studying complex social phenomena that are influenced by contextual factors.

However, case studies also have limitations. Due to their in-depth nature, case studies are time-consuming and resource-intensive. They require significant effort to collect and analyze data, making them less suitable for large-scale studies. Additionally, the findings of case studies may lack generalizability, as they are often focused on specific cases or contexts. Therefore, caution must be exercised when applying the results of a case study to a broader population.

Surveys, on the other hand, are a research method that involves collecting data from a large number of participants using standardized questionnaires or interviews. Surveys are widely used in social sciences and market research to gather quantitative data and identify patterns or trends within a population. They provide a snapshot of the opinions, attitudes, and behaviors of a specific group.

One of the primary attributes of surveys is their ability to collect data from a large and diverse sample. By reaching a significant number of participants, surveys allow researchers to generalize their findings to a broader population. This attribute makes surveys particularly useful when studying large-scale phenomena or when the goal is to make statistical inferences.

Moreover, surveys offer a structured and standardized approach to data collection. The use of pre-determined questions and response options ensures consistency across participants, making it easier to compare and analyze the data. Surveys also allow for efficient data collection, as they can be administered to a large number of participants simultaneously, reducing the time and resources required.

However, surveys also have limitations. They rely heavily on self-reporting, which may introduce response biases or inaccuracies. Participants may provide socially desirable responses or misunderstand the questions, leading to biased or unreliable data. Additionally, surveys often provide limited depth of information, as they focus on collecting quantitative data rather than exploring the underlying reasons or motivations behind participants' responses.

Comparing Case Studies and Surveys

While case studies and surveys differ in their approach and data collection techniques, they both have their own strengths and limitations. Case studies offer a detailed and holistic understanding of a specific case or phenomenon, capturing rich qualitative data and providing high ecological validity. However, they are time-consuming, resource-intensive, and may lack generalizability.

On the other hand, surveys allow for data collection from a large and diverse sample, enabling generalizability and statistical inferences. They offer a structured and efficient approach to data collection, but may suffer from response biases and provide limited depth of information.

Choosing between case studies and surveys depends on the research objectives, the nature of the phenomenon under investigation, and the available resources. If the goal is to explore complex social phenomena in-depth and within their natural context, a case study may be the most appropriate method. However, if the aim is to gather data from a large population and make statistical inferences, a survey would be more suitable.

It is worth noting that case studies and surveys are not mutually exclusive. In fact, they can complement each other in a mixed-methods approach. Researchers can use a case study to gain a deep understanding of a specific case and then conduct a survey to validate or generalize the findings to a larger population.

Case studies and surveys are valuable research methods that offer unique attributes and insights. Case studies provide a detailed and holistic understanding of a specific case or phenomenon, capturing rich qualitative data and enhancing external validity. Surveys, on the other hand, allow for data collection from a large and diverse sample, enabling generalizability and statistical inferences. Both methods have their own strengths and limitations, and the choice between them depends on the research objectives and available resources. By understanding the attributes of case studies and surveys, researchers can make informed decisions and conduct rigorous and impactful research.

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Distinguishing Between Case Study & Survey Methods

Key Difference – Case Study vs Survey

When carrying out research, case studies and surveys are two methods used by researchers. Although both are used to collect information, there is a key difference between a case study and a survey. A case study involves researching an individual, group, or specific situation in-depth, usually over a long period of time. On the other hand, a survey involves gathering data from an entire population or a very large sample to understand opinions on a specific topic. The main difference between the two methods is that case studies produce rich, descriptive data, while surveys do not; instead, the data collected from surveys is more statistically significant.

Key Takeaways

• Case studies involve in-depth research of an individual, group, or specific situation, while surveys gather data from an entire population or a large sample.
• Case studies produce rich, descriptive data, while surveys produce data that is more statistically significant.
• Case studies are used in qualitative research, while surveys are mostly used in quantitative research.

What is a Case Study?

A case study refers to an in-depth study in which an individual, group, or a particular situation is studied. This is used in both natural and social sciences. In the natural sciences, a case study can be used to validate a theory or even a hypothesis. In the social sciences, case studies are used extensively to study human behavior and comprehend various social aspects. For example, in psychology, case studies are conducted to comprehend individual behavior. In such cases, the researcher records the entire history of the individual so that it enables him to identify various patterns of behavior. One of the classic examples of a case study is Sigmund Freud’s study of Anna O.

Case studies typically produce rich descriptive data. However, they cannot be used to provide generalizations on an entire population since the sample of a case study is usually limited to a single individual or a few individuals. Various research techniques, such as interviews, direct and participatory observation, and documents can be used for case studies.

What is a Survey?

A survey refers to research where data is gathered from an entire population or a very large sample to understand the opinions on a particular matter. In modern society, surveys are often used in politics and marketing. For example, imagine a situation where an organization wishes to understand the opinions of consumers on their latest product. Naturally, the organization would conduct a survey to comprehend the opinions of the consumer.

One of the most powerful research techniques used for surveys is the questionnaire. For this, the researcher creates a set of questions on the topic for which he will gather information from the participants. Unlike case studies, the data gathered from surveys is not very descriptive. Instead, they are statistically significant.

What is the difference between Case Study and Survey?

Definitions of Case Study and Survey: Case Study: A case study refers to an in-depth study in which an individual, group, or a particular situation is studied. Survey: A survey refers to research where data is gathered from an entire population or a very large sample to understand the opinions on a particular matter. Characteristics of Case Study and Survey: Research Type: Case Study: Case studies are used in qualitative research. Survey: Surveys are mostly used in quantitative research. Data: Case Study: Case studies produce rich in-depth data. Survey: Surveys produce numerical data. Sample: Case Study: For a case study, a relatively small population is chosen. This can vary from a few individuals to groups. Survey: For a survey, a large population can be used as the sample.

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Case Study vs. Survey — What's the Difference?

Difference Between Case Study and Survey

Table of contents, key differences, comparison chart, methodology, sample size, compare with definitions, common curiosities, what is the main advantage of surveys, what is a survey, what is a case study, how long does it take to conduct a survey, is a case study subjective, how do researchers ensure validity in case studies, what are common uses for surveys, when should i use a case study, can case studies be generalized, are case studies qualitative or quantitative, what sample size is typical for surveys, can a case study include quantitative data, what types of questions are used in surveys, can one research include both a case study and a survey, are online surveys reliable, share your discovery.

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2.2 Approaches to Research

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research
• Compare and contrast correlation and causation

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.

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. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

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 this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in 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.

Clinical or 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.

Link to Learning

Watch this CBC video about Krista's and Tatiana's lives to learn more.

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).

Over time, it has become clear that while Krista and Tatiana share some sensory experiences and motor control, they remain two distinct individuals, which provides invaluable insight for researchers interested in the mind and the brain (Egnor, 2017).

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 precious 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.

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 chapter: 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 2.7 ).

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 2.8 ). 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 chapter 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.

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 2.9 ). 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. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

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).

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure 2.10 ).

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 studying 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 that 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 2.11 ).

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 increase 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.

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Quantitative study designs: Case Studies/ Case Report/ Case Series

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial
• Case Control
• Cross-Sectional Studies
• Study Designs Home

Case Study / Case Report / Case Series

Some famous examples of case studies are John Martin Marlow’s case study on Phineas Gage (the man who had a railway spike through his head) and Sigmund Freud’s case studies, Little Hans and The Rat Man. Case studies are widely used in psychology to provide insight into unusual conditions.

A case study, also known as a case report, is an in depth or intensive study of a single individual or specific group, while a case series is a grouping of similar case studies / case reports together.

A case study / case report can be used in the following instances:

• where there is atypical or abnormal behaviour or development
• an unexplained outcome to treatment
• an emerging disease or condition

The stages of a Case Study / Case Report / Case Series

Which clinical questions does Case Study / Case Report / Case Series best answer?

Emerging conditions, adverse reactions to treatments, atypical / abnormal behaviour, new programs or methods of treatment – all of these can be answered with case studies /case reports / case series. They are generally descriptive studies based on qualitative data e.g. observations, interviews, questionnaires, diaries, personal notes or clinical notes.

What are the advantages and disadvantages to consider when using Case Studies/ Case Reports and Case Series ?

What are the pitfalls to look for.

One pitfall that has occurred in some case studies is where two common conditions/treatments have been linked together with no comprehensive data backing up the conclusion. A hypothetical example could be where high rates of the common cold were associated with suicide when the cohort also suffered from depression.

Critical appraisal tools 

To assist with critically appraising Case studies / Case reports / Case series there are some tools / checklists you can use.

JBI Critical Appraisal Checklist for Case Series

JBI Critical Appraisal Checklist for Case Reports

Real World Examples

Some Psychology case study / case report / case series examples

Capp, G. (2015). Our community, our schools : A case study of program design for school-based mental health services. Children & Schools, 37(4), 241–248. A pilot program to improve school based mental health services was instigated in one elementary school and one middle / high school. The case study followed the program from development through to implementation, documenting each step of the process.

Cowdrey, F. A. & Walz, L. (2015). Exposure therapy for fear of spiders in an adult with learning disabilities: A case report. British Journal of Learning Disabilities, 43(1), 75–82. One person was studied who had completed a pre- intervention and post- intervention questionnaire. From the results of this data the exposure therapy intervention was found to be effective in reducing the phobia. This case report highlighted a therapy that could be used to assist people with learning disabilities who also suffered from phobias.

Li, H. X., He, L., Zhang, C. C., Eisinger, R., Pan, Y. X., Wang, T., . . . Li, D. Y. (2019). Deep brain stimulation in post‐traumatic dystonia: A case series study. CNS Neuroscience & Therapeutics. 1-8. Five patients were included in the case series, all with the same condition. They all received deep brain stimulation but not in the same area of the brain. Baseline and last follow up visit were assessed with the same rating scale.

References and Further Reading  

Greenhalgh, T. (2014). How to read a paper: the basics of evidence-based medicine. (5th ed.). New York: Wiley.

Heale, R. & Twycross, A. (2018). What is a case study? Evidence Based Nursing, 21(1), 7-8.

Himmelfarb Health Sciences Library. (2019). Study design 101: case report. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casereports.cfm

Hoffmann T., Bennett S., Mar C. D. (2017). Evidence-based practice across the health professions. Chatswood, NSW: Elsevier.

Robinson, O. C., & McAdams, D. P. (2015). Four functional roles for case studies in emerging adulthood research. Emerging Adulthood, 3(6), 413-420.

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Case Study Research Method in Psychology

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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Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

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Understanding the difference between survey and experiment: a student's guide.

In the realm of academic research, surveys and experiments are two fundamental methodologies that students often encounter. Understanding the difference between these two approaches is crucial for designing effective studies and interpreting data accurately. This guide will delve into the essentials of survey and experimental research, compare their applications, and provide practical advice for integrating them into academic projects.

Key Takeaways

• Survey research is a method for collecting data from a predefined group of respondents to gain information and insights on various topics of interest.
• Experiments involve manipulating one variable to determine if changes in one variable cause changes in another variable, establishing a cause-and-effect relationship.
• Surveys are typically used when collecting a large amount of data from a large sample size, while experiments are used when looking to control and measure the impact of specific variables.
• Both surveys and experiments have their own set of advantages and limitations, and the choice between them should be based on the research question and objectives.
• Combining surveys and experiments can provide a more comprehensive understanding of the research topic and can lead to more robust and actionable conclusions.

Fundamentals of Survey Research

Defining survey research and its purpose.

As you delve into the world of research, you'll find that survey research is a fundamental tool for gathering information. Surveys are primary research tools that provide data as part of overall research strategies, critical to getting the answers you need. At its core, survey research involves the collection of information from a sample of individuals through their responses to questions. This method is standardized and systematic , ensuring that the data collected is reliable and can be generalized to a larger population.

When considering survey research, it's important to understand its purpose. Surveys are most effective when you aim to collect brief and straightforward data points from a large, representative sample. They can be used to measure various elements within a population, from customer feedback to academic research. Here are some key reasons for using surveys:

• To gather qualitative and emotional feedback
• To collect comprehensive data efficiently
• To understand customer or public opinion

Remember, the choice of using a survey ultimately depends on the specific needs and constraints of your research project. By defining clear objectives and understanding the strengths of survey methodology, you can ensure that your research yields valuable insights.

Types of Surveys: Cross-Sectional and Longitudinal

When you embark on survey research, you'll encounter two primary types: cross-sectional and longitudinal studies. Cross-sectional surveys are snapshots, capturing data at a single point in time from a selected sample. They are particularly useful for assessing the current state of affairs, such as public opinion or consumer preferences. In contrast, longitudinal surveys are designed to track changes over time, collecting data from the same subjects at multiple intervals. This approach is invaluable for observing trends, patterns, and the long-term effects of interventions.

Choosing between these types hinges on your research objectives. If you aim to understand how variables may correlate at a specific time, a cross-sectional study might suffice. However, if you're interested in how relationships between variables evolve, a longitudinal survey will be more appropriate. Below is a list highlighting the distinct features of each type:

Cross-sectional surveys:

• Provide a quick overview of a situation
• Cost-effective and less time-consuming
• Ideal for descriptive research

Longitudinal surveys:

• Allow for the observation of developments and changes
• Can identify causal relationships
• Require more resources and commitment

Remember, the choice of survey type will significantly influence your study's insights and conclusions. Tools and resources, such as thesis worksheets and action plans , can assist in managing your data and maintaining the integrity of your research design.

Advantages and Limitations of Survey Methodology

When you embark on survey research, you're choosing a path with both significant benefits and notable challenges. Surveys are praised for their ease of implementation and the ability to collect large volumes of data quickly and at low cost. This is particularly true for remote data collection, where geographical constraints are virtually eliminated. The ability to reach a wide audience swiftly is a key advantage of surveys.

However, surveys come with limitations that must be carefully considered. They provide sampled data, not complete data, which means that the results are based on a subset of the population rather than the entire group. This can lead to survey fatigue , reducing response rates and potentially skewing the data. Moreover, the honesty and intention of respondents can impact the accuracy of the results, and unintentional biases in survey design can lead to incorrect conclusions.

Here's a quick overview of the advantages and disadvantages of surveys:

• Easy to implement
• Fast data collection turnaround
• Effective for collecting large volumes of data
• Suitable for remote data collection

Disadvantages:

• Provides sampled data, not complete data
• Potential for survey fatigue
• Responses may not be entirely objective
• Risk of biases affecting accuracy

Designing Effective Surveys

Crafting clear and unbiased questions.

When you're tasked with crafting clear and unbiased questions , it's crucial to focus on the precision and neutrality of your language. The goal is to elicit responses that are reflective of the respondents' true opinions and experiences, not influenced by the wording of the question. To achieve this, you should use language that is neutral, natural, and clear , avoiding any jargon that might confuse respondents or lead to misinterpretation.

Here are some best practices to consider:

• Ensure each question focuses on a single topic to avoid confusion.
• Keep questions brief; longer questions can be more difficult to comprehend and may introduce bias.
• Avoid double-barrelled questions that ask about two things at once, as they can be answered in multiple ways.
• Use closed-ended questions when looking for specific, quantifiable data.

Remember, the validation of your survey questions is as important as their formulation. Testing your survey with a small group before full deployment can help identify issues with question clarity and structure. By adhering to these guidelines, you can minimize bias and maximize the reliability of your survey data.

Choosing the Right Survey Medium

Selecting the appropriate survey medium is crucial for the success of your research. The medium you choose should align with your research objectives, target population, and available resources. For instance, online surveys are cost-effective and can reach a broad audience quickly, making them ideal for large-scale quantitative research. In contrast, face-to-face interviews allow for deeper exploration of responses, suitable for qualitative insights.

When considering your options, reflect on the accessibility of the medium to your intended participants. A survey that is not easily accessible can lead to low response rates and potential biases in your data. Here are some common survey mediums and their attributes:

• Online : Wide reach, cost-effective, quick turnaround
• Telephone : Personal touch, higher response rates
• Mail : Tangible, can reach non-internet users
• In-person : Detailed responses, high engagement

Remember, the medium you select can also impact the quality of the data collected. It's essential to weigh the advantages and disadvantages of each option. For example, while online surveys offer tools for fast data collection, they may also lead to survey fatigue. On the other hand, in-person interviews can provide rich qualitative data but may be more time-consuming and costly. Ultimately, your choice should be informed by the specific needs and constraints of your research project.

Ensuring Ethical Standards in Survey Research

As you embark on survey research, it's imperative to uphold the highest ethical standards. Ethical considerations are not just a formality; they are central to the integrity and validity of your research. When designing your survey, you must ensure voluntary participation and obtain informed consent , guaranteeing that respondents are fully aware of the survey's purpose and their rights. Anonymity and confidentiality are also crucial to protect the identity and privacy of participants, especially when sensitive data is involved.

To adhere to these ethical principles, consider the following steps:

• Clearly communicate the social and clinical value of your research to participants.
• Assess and ensure the scientific validity of your survey.
• Employ fair subject selection to avoid biases.
• Evaluate the risk-benefit ratio to minimize potential harm.
• Maintain independence in data analysis and reporting.

Remember, ethical research is not only about following guidelines but also about respecting the dignity and rights of your participants. Tools and resources are available to assist you in maintaining research integrity , such as worksheets and templates that emphasize transparent reporting of results. Always be vigilant of the ethical questions that may arise and be prepared to address them responsibly.

Principles of Experimental Research

Understanding controlled experiments.

In the realm of experimental research, a controlled experiment is a cornerstone methodology that allows you to explore cause-and-effect relationships. By manipulating one or more independent variables , researchers can observe the impact on dependent variables, while controlling for extraneous factors. This rigorous approach ensures that the outcomes observed are indeed due to the manipulation of the independent variable and not some other unseen variable.

To conduct a controlled experiment effectively, you must follow a structured process:

• Identify the independent and dependent variables.
• Establish a control group that does not receive the experimental treatment.
• Randomly assign participants to groups to prevent selection bias.
• Apply the treatment to the experimental group(s) while keeping all other conditions constant.
• Collect and analyze the data to determine the effect of the independent variable.

Remember, the goal is to achieve reliable and valid results that contribute to the body of knowledge in your field. As you embark on this journey, resources like the ' Experimental Research Roadmap ' can provide guidance, ensuring that your study adheres to the highest standards of academic rigor.

Randomization and Its Role in Reducing Bias

In your journey to understand experimental research, you'll find that randomization is a cornerstone of robust study design. Randomization serves as a powerful tool to balance treatment groups , ensuring that each participant has an equal chance of being assigned to any given condition. This process helps to mitigate the influence of confounding variables—those pesky factors that could otherwise skew your results.

By randomizing participants, you effectively remove the effect of extraneous variables , such as age or injury history, and minimize bias associated with treatment assignment. The benefits of this technique are manifold; it balances the groups with respect to baseline variability and both known and unknown confounding factors, thus eliminating selection bias. Moreover, randomization enhances the quality of evidence-based studies by minimizing the selection bias that could affect outcomes.

Consider the following points when implementing randomization in your experiment:

• It ensures each participant has an equal chance of assignment to any group.
• It minimizes the impact of confounding variables.
• It increases the reliability of your results.
• It is a key factor in the ability to generalize findings to a larger population.

Interpreting Results from Experimental Studies

Once you've conducted your experiment and gathered the data, the next critical step is to interpret the results. Interpreting the findings involves comparing them to your initial hypotheses and understanding what they mean in the context of your research. It's essential to reiterate the research problem and assess whether the data support or refute your predictions.

When analyzing the results, look for trends, compare groups, and examine relationships among variables. Unexpected or statistically insignificant findings should not be disregarded; instead, they can provide valuable insights. For instance, if you encounter unexpected data , it's crucial to report these events and explain how they were handled during the analysis, ensuring the validity of your study is maintained.

Discussing the implications of your results is where you highlight the key findings and their significance. Here, you can articulate how your results fill gaps in understanding the research problem. However, be mindful of any limitations or unavoidable bias in your study and discuss how these did not inhibit effective interpretation of the results. Below is a structured approach to interpreting experimental data:

• Reiterate the research problem and compare findings with the research questions.
• Describe trends, group comparisons, and variable relationships.
• Highlight unexpected findings and their handling.
• Discuss the implications and significance of the results.
• Acknowledge limitations and biases, and their impact on interpretation.

Comparing Surveys and Experiments

When to use surveys vs. experiments.

Choosing between a survey and an experiment hinges on the nature of your research question and the type of data you need. Surveys are ideal for gathering a large volume of responses on attitudes, behaviors, or perceptions, allowing you to generalize findings to a broader population. They are particularly useful when you aim to describe characteristics of a large group or when you need to collect data at one point in time or track changes over time.

Experiments, on the other hand, are the gold standard for establishing cause-and-effect relationships. By manipulating one or more variables and controlling external factors, you can infer causality with greater confidence. Experiments are indispensable when testing hypotheses under controlled conditions is necessary to isolate the effects of specific variables.

Here's a quick guide to help you decide:

• Use a survey when you need to understand the prevalence of certain views or behaviors in a population.
• Opt for an experiment when you need to determine if one variable affects another in a controlled setting.
• Consider the resources available, including time, budget, and expertise, as experiments often require more of each.
• Reflect on ethical considerations; surveys may be less intrusive, but informed consent is crucial in both methods.

In summary, surveys are powerful tools for descriptive research, while experiments excel in explanatory research. Your choice should align with your research objectives, the questions you seek to answer, and the level of evidence required.

Impact of Research Design on Data Quality

The integrity of your research findings hinges on the quality of your research design. A robust design ensures that the conclusions drawn are valid and reliable. The quality of research designs can be defined in terms of four key design attributes : internal validity, external validity, construct validity, and statistical validity. These attributes are critical in determining whether the results can be generalized to other settings (external validity), if the study measures what it intends to (construct validity), and if the statistical conclusions are accurate (statistical validity).

When you embark on your master thesis research , choosing the right design is paramount. It involves identifying research gaps and collecting reliable data to contribute to existing knowledge. A poor design can lead to incorrect conclusions, undermining the value of your research. Conversely, a thoughtful and well-executed design bolsters the credibility of your findings.

Here are some considerations to keep in mind when designing your research:

• Ensure clarity and objectivity in your research questions.
• Select a sample size that is representative of the population.
• Employ appropriate randomization techniques to reduce bias.
• Plan for replication to test the study's reliability.

Remember, conducting organizational research via online surveys and experiments offers advantages in data collection, but it also requires careful attention to design to maintain data quality.

Combining Surveys and Experiments for Comprehensive Insights

When you aim to achieve a holistic understanding of your research topic, combining surveys and experiments can be a powerful strategy. Surveys allow you to gather a broad range of data from a large sample, providing a snapshot of attitudes, behaviors, or characteristics. Experiments, on the other hand, enable you to establish cause-and-effect relationships through controlled conditions and manipulation of variables.

By integrating both methods , you can enrich your quantitative findings with the depth of qualitative insights. This mixed-methods approach not only enhances the robustness of your data but also allows you to explore different dimensions of your research question.

Consider the following steps to effectively combine surveys and experiments:

• Begin with a survey to identify patterns and generate hypotheses.
• Use experimental research to test these hypotheses under controlled conditions.
• Re-administer the survey post-experiment to measure changes and gather additional feedback.

This sequential application ensures that each method informs and complements the other, leading to more comprehensive and reliable conclusions . Remember, the key to a successful combination is to maintain clarity and consistency in your research objectives throughout the process.

Applying Survey and Experimental Research in Academic Projects

Selecting appropriate methods for thesis research.

When embarking on your thesis, the choice between survey and experimental research hinges on the nature of your research question. Surveys are ideal for descriptive research , where the goal is to capture the characteristics of a population at a specific point in time. In contrast, experiments are suited for explanatory research that seeks to establish causal relationships through manipulation and control of variables.

To select the method that best aligns with your study, consider the following points:

• Define the purpose of your research: Is it exploratory, descriptive, explanatory, or evaluative?
• Determine the nature of the data required: Do you need quantitative measurements or qualitative insights?
• Assess the feasibility: What resources and time are available to you?

Remember, the methodology you choose will significantly impact the quality of your data and the credibility of your findings. It's essential to weigh the advantages and limitations of each method in the context of your research objectives.

Case Studies: Successful Survey and Experimental Designs

In your academic journey, understanding how to effectively design and implement research is crucial. Case studies of successful survey and experimental designs provide invaluable insights into the practical application of these methodologies. For instance, Sage Publications highlights the complexity of developing research designs for case studies, emphasizing the lack of a comprehensive catalog of research methods tailored to case studies. This underscores the importance of customizing your approach to fit the unique aspects of your research question.

When examining various case studies, you'll notice a common theme: the in-depth, multi-faceted exploration of complex issues within their real-life settings , as noted by BMC Medical Research Methodology. This approach allows for a rich understanding of the phenomena under study. To illustrate, consider the following bulleted list of key elements derived from successful research designs:

• A clear, well-defined research question
• Thoughtful selection of research methods
• Rigorous data collection and management techniques
• Ethical considerations and participant consent
• Detailed analysis and interpretation of data

These elements are echoed across various resources, including websites offering thesis resources, worksheets, and articles on interview research techniques and data management . By studying these case studies, you can glean strategies for excelling in your chosen field of study, translating complex academic procedures into actionable steps .

Translating Research Findings into Actionable Conclusions

Once you've navigated the complexities of your research and arrived at meaningful conclusions, the next critical step is to translate these findings into practical applications. Understanding the implications of your study is essential for making a tangible impact. Begin by synthesizing the key findings without delving into statistical minutiae; provide a narrative that captures what you've learned and how it adds to the existing body of knowledge.

Consider the broader context of your research and how it can inform policy decisions or professional practices. For instance, if your study identifies effective teaching strategies, these can be translated into recommendations for educational curriculum development. It's crucial to understand the problem first to ensure that your conclusions address real-world challenges effectively.

To ensure your research has a lasting influence, follow these steps:

• Reiterate the research problem and align your findings with the initial research questions.
• Discuss any unexpected trends or statistically insignificant findings and their implications.
• Acknowledge limitations and suggest areas for future research to address gaps in the literature.

Remember, the goal is not just to add to the academic conversation but to drive change and foster improvement in the relevant field. By effectively disseminating and translating your research into clinical practice or business insights, you contribute to the advancement of knowledge and the betterment of society.

Delving into the intricacies of survey and experimental research can significantly enhance the quality and impact of your academic projects. By applying these methodologies, you can uncover valuable insights and contribute to the body of knowledge in your field. To learn more about effectively integrating these research techniques into your work, visit our website . We provide comprehensive guides and resources to support your academic endeavors.

In summary, understanding the distinction between surveys and experiments is crucial for students embarking on research projects. Surveys are invaluable for collecting data from large populations, offering insights through a series of questions and enabling the analysis of trends and patterns within a sample. Experiments, on the other hand, allow researchers to establish causal relationships by manipulating variables and observing the outcomes in a controlled setting. Both methods have their unique advantages and limitations, and the choice between them should be guided by the research objectives, the nature of the hypothesis, and the practical constraints of the study. By grasping the differences and applications of each method, students can design more effective studies and contribute meaningful findings to their respective fields.

Frequently Asked Questions

What is the main difference between a survey and an experiment.

A survey is a research method used to collect data from a sample of individuals through their responses to questions. An experiment involves manipulating one variable to determine its effect on another, establishing a cause-and-effect relationship under controlled conditions.

When should I use a survey in my research?

Surveys are most appropriate when you need to collect data from a large group of people to understand trends, attitudes, or behaviors. They are useful for gathering both qualitative and quantitative information.

What are the advantages of experimental research over surveys?

Experimental research allows you to control variables and establish causality, making it possible to determine the effect of one variable on another. This level of control is not possible in survey research, which can only show correlations.

Can I combine surveys and experiments in my research project?

Yes, combining surveys and experiments can provide comprehensive insights. Surveys can gather preliminary data or post-experiment feedback, while experiments can test hypotheses generated from survey results.

How can I ensure my survey questions are unbiased?

To ensure unbiased survey questions, avoid leading or loaded language, ensure questions are clear and straightforward, offer balanced answer choices, and pretest your survey with a small sample to identify potential biases.

What is randomization in experimental research, and why is it important?

Randomization is the process of randomly assigning participants to different treatment groups in an experiment. It is crucial because it helps reduce selection bias and ensures that the groups are comparable, which enhances the validity of the results.

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• Key Differences

Know the Differences & Comparisons

Difference Between Survey and Experiment

While surveys collected data, provided by the informants, experiments test various premises by trial and error method. This article attempts to shed light on the difference between survey and experiment, have a look.

Content: Survey Vs Experiment

Comparison chart, definition of survey.

By the term survey, we mean a method of securing information relating to the variable under study from all or a specified number of respondents of the universe. It may be a sample survey or a census survey. This method relies on the questioning of the informants on a specific subject. Survey follows structured form of data collection, in which a formal questionnaire is prepared, and the questions are asked in a predefined order.

Informants are asked questions concerning their behaviour, attitude, motivation, demographic, lifestyle characteristics, etc. through observation, direct communication with them over telephone/mail or personal interview. Questions are asked verbally to the respondents, i.e. in writing or by way of computer. The answer of the respondents is obtained in the same form.

Definition of Experiment

The term experiment means a systematic and logical scientific procedure in which one or more independent variables under test are manipulated, and any change on one or more dependent variable is measured while controlling for the effect of the extraneous variable. Here extraneous variable is an independent variable which is not associated with the objective of study but may affect the response of test units.

In an experiment, the investigator attempts to observe the outcome of the experiment conducted by him intentionally, to test the hypothesis or to discover something or to demonstrate a known fact. An experiment aims at drawing conclusions concerning the factor on the study group and making inferences from sample to larger population of interest.

Key Differences Between Survey and Experiment

The differences between survey and experiment can be drawn clearly on the following grounds:

• A technique of gathering information regarding a variable under study, from the respondents of the population, is called survey. A scientific procedure wherein the factor under study is isolated to test hypothesis is called an experiment.
• Surveys are performed when the research is of descriptive nature, whereas in the case of experiments are conducted in experimental research.
• The survey samples are large as the response rate is low, especially when the survey is conducted through mailed questionnaire. On the other hand, samples required in the case of experiments is relatively small.
• Surveys are considered suitable for social and behavioural science. As against this, experiments are an important characteristic of physical and natural sciences.
• Field research refers to the research conducted outside the laboratory or workplace. Surveys are the best example of field research. On the contrary, Experiment is an example of laboratory research. A laboratory research is nothing but research carried on inside the room equipped with scientific tools and equipment.
• In surveys, the data collection methods employed can either be observation, interview, questionnaire, or case study. As opposed to experiment, the data is obtained through several readings of the experiment.

While survey studies the possible relationship between data and unknown variable, experiments determine the relationship. Further, Correlation analysis is vital in surveys, as in social and business surveys, the interest of the researcher rests in understanding and controlling relationships between variables. Unlike experiments, where casual analysis is significant.

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sanjay kumar yadav says

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September 9, 2017 at 9:30 pm

The article was quite helpful… Thank you.

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Can you develop your Application for Android

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Yeah, we will develop android app soon.

October 31, 2018 at 12:32 am

If I was doing an experiment with Poverty and Education level, which do you think would be more appropriate for me?

Thanks, Chris

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So interested,

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Association between Family Household Income and Cognitive Resilience among Older US Adults: A Cross-Sectional Study

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• Published: 29 May 2024

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• M. Iskandar 1 ,
• J. Martindale 1 , 2 ,
• J. P. W. Bynum 1 , 2 &
• Matthew A. Davis 2 , 3 , 4  

Cognitive resilience has emerged as a mechanism that may help explain individual differences in cognitive function associated with aging and/or pathology. It is unknown whether an association exists between family income level and cognitive resilience. We performed a cross-sectional study to estimate the relationship between family income level and high cognitive resilience using the National Health and Nutrition Examination Survey (NHANES) among older adults (age≥60). Logistic regression was used to estimate the association between income level and high cognitive resilience adjusted for other factors. Accounting for differences in education, occupation, and health status, older adults in the highest income category were twice as likely compared to those with very low income to have high cognitive resilience (OR: 1.90, 95% CI: 1.05,3.43). A doseresponse was apparent between income category and high cognitive resilience. The finding that income, above and beyond that of known factors, affects cognitive function is important for future public health strategies that aim to prevent or delay cognitive impairment.

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Acknowledgments

Dr. Davis affirms that everyone who contributed significantly to the work is listed as a co-author.

Funding Information: All authors were supported by the National Institute on Aging (NIA) at the National Institutes of Health [grant number P30AG066582].

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Authors and affiliations.

Division of Geriatric and Palliative Medicine, University of Michigan Medical School, 400 North Ingalls, Ann Arbor, Michigan, 48109-5482, USA

M. Iskandar, J. Martindale & J. P. W. Bynum

Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan, USA

J. Martindale, J. P. W. Bynum & Matthew A. Davis

Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, Michigan, USA

Matthew A. Davis

Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA

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Author Contributions: Study concept and design: All authors. Acquisition of subjects and/or data: Martindale and Davis. Analysis and interpretation of data: All authors. Preparation of manuscript: All authors.

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Correspondence to Matthew A. Davis .

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Conflict of Interest: All authors were supported by grant P30AG066582 from the National Institute on Aging. Dr. Davis received financial support from Regional Anesthesia & Pain Medicine for consulting statistical review. Mr. Iskandar, Mr. Martindale, and Dr. Bynum have no conflicts to report.

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Sponsor’s Role: The funders had no role in the study design, data collection, management, and analysis, nor any participation in the preparation, review, and approval of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Iskandar, M., Martindale, J., Bynum, J.P.W. et al. Association between Family Household Income and Cognitive Resilience among Older US Adults: A Cross-Sectional Study. J Prev Alzheimers Dis (2024). https://doi.org/10.14283/jpad.2024.97

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Received : 17 January 2024

Accepted : 14 April 2024

Published : 29 May 2024

DOI : https://doi.org/10.14283/jpad.2024.97

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