thesis about variables

What are Examples of Variables in Research?

Table of contents, introduction.

In writing your thesis, one of the first terms that you encounter is the word variable. Failure to understand the meaning and the usefulness of variables in your study will prevent you from doing excellent research. What are variables, and how do you use variables in your research?

I explain this key research concept below with lots of examples of variables commonly used in a study.

You may find it challenging to understand just what variables are in research, especially those that deal with quantitative data analysis. This initial difficulty about variables becomes much more confusing when you encounter the phrases “dependent variable” and “independent variable” as you go deeper in studying this vital concept of research, as well as statistics.

Understanding what variables mean is crucial in writing your thesis proposal because you will need these in constructing your conceptual framework  and in analyzing the data that you have gathered.

Therefore, it is a must that you should be able to grasp thoroughly the meaning of variables and ways on how to measure them. Yes, the variables should be measurable so that you will use your data for statistical analysis.

I will strengthen your understanding by providing examples of phenomena and their corresponding variables below.

Definition of Variable

Variables are those simplified portions of the complex phenomena that you intend to study. The word variable is derived from the root word “vary,” meaning, changing in amount, volume, number, form, nature, or type. These variables should be measurable, i.e., they can be counted or subjected to a scale.

The next section provides examples of variables related to climate change , academic performance, crime, fish kill, crop growth, and how content goes viral. Note that the variables in these phenomena can be measured, except the last one, where a bit more work is required.

Examples of Variables in Research: 6 Phenomena

The following are examples of phenomena from a global to a local perspective. The corresponding list of variables is given to illustrate how complex phenomena can be broken down into manageable pieces for better understanding and to subject the phenomena to research.

Phenomenon 1: Climate change

Examples of variables related to climate change :

• temperature
• the amount of carbon emission
• the amount of rainfall

Phenomenon 2: Crime and violence in the streets

Examples of variables related to crime and violence :

• number of robberies
• number of attempted murders
• number of prisoners
• number of crime victims
• number of laws enforcers
• number of convictions
• number of carnapping incidents

Phenomenon 3: Poor performance of students in college entrance exams

Examples of variables related to poor academic performance :

• entrance exam score
• number of hours devoted to studying
• student-teacher ratio
• number of students in the class
• educational attainment of teachers
• teaching style
• the distance of school from home
• number of hours devoted by parents in providing tutorial support

Phenomenon 4: Fish kill

Examples of variables related to fish kill :

• dissolved oxygen
• water salinity
• age of fish
• presence or absence of parasites
• presence or absence of heavy metal
• stocking density

Phenomenon 5: Poor crop growth

Examples of variables related to poor crop growth :

• the amount of nitrogen in the soil
• the amount of phosphorous in the soil
• the amount of potassium in the ground
• frequency of weeding
• type of soil

Phenomenon 6:  How Content Goes Viral

• interesting,
• surprising, and
• causing physiological arousal.

Notice in the above variable examples that all the factors listed under the phenomena can be counted or measured using an ordinal, ratio, or interval scale, except for the last one. The factors that influence how content goes viral are essentially subjective.

But researchers devised ways to measure those variables by grouping the respondents’ answers on whether content is positive, interesting, prominent, among others (see the  full description here ).

Thus, the variables in the last phenomenon represent the  nominal scale of measuring variables .

The expected values derived from these variables will be in terms of numbers, amount, category, or type. Quantified variables allow statistical analysis . Variable descriptions, correlations, or differences are then determined.

Difference Between Independent and Dependent Variables

Which of the above examples of variables are the independent and the dependent variables?

Independent Variables

The independent variables are those variables that may influence or affect the other variable, i.e., the dependent variable.

For example, in the second phenomenon, i.e., crime and violence in the streets, the independent variables are the number of law enforcers. If there are more law enforcers, it is expected that it will reduce the following:

• number of robberies,
• number of attempted murders,
• number of prisoners, 
• number of crime victims, and
• the number of carnapping incidents.

The five variables listed under crime and violence in the streets as the theme of a study are all dependent variables.

Dependent Variables

The dependent variable, as previously mentioned, is the variable affected or influenced by the independent variable.

For example, in the first phenomenon on climate change, temperature as the independent variable influences sea level rise, the dependent variable. Increased temperature will cause the expansion of water in the sea. Thus, sea-level rise on a global scale will occur.

I will leave the classification of the other variables to you. Find out whether those are independent or dependent variables. Note, however, that some variables can be both independent or dependent variables, as the context of the study dictates.

Finding the relationship between variables

How will you know that one variable may cause the other to behave in a certain way?

Finding the relationship between variables requires a thorough  review of the literature . Through a review of the relevant and reliable literature, you will find out which variables influence the other variable. You do not just guess relationships between variables. The entire process is the essence of research.

At this point, I believe that the concept of the variable is now clear to you. Share this information with your peers, who may have difficulty in understanding what the variables are in research.

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How to write the conceptual framework in a research proposal, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

128 Comments

Your question is unclear to me Biyaminu. What do you mean? If you want to cite this, see the citation box after the article.

• Pingback: Quantitative Research Design: 4 Common Ways to Gather Your Data Efficiently October 15, 2020

Dear Calvin, when you state your research objectives that’s where you will know if you need to use variables or not.

Great work. I’d just like to know in which situations are variables not used in scientific research please. thank you.

• Pingback: Nonparametric Tests: 8 Important Considerations Before Using Them October 11, 2020

I salute your work, before I was have no enough knowledge about variable I think I was claimed from my lecturers, but the real meaning I was in the mid night. thanks

Thank you very much for your nice NOTE! I have a question: Can you please give me any examples of variables in students’ indiscipline?

A well articulated exposition! Pls, I need a simple guide on the variables of the following topic : IMPACT OF TAX REFORMS ON REVENUE GENERATION IN NIGERIA: A CASE STUDY OF KOGI STATE. THANKS A LOT.

thanks for the explanation a bout variables. keep on posting information a bout reseach on my email.

This was extremely helpful and easy to digest

Dear Hamse, That depends on what variables you are studying. Are you doing a study on cause and effect?

Dear Sophia and Hamse,

As I mentioned earlier, please read the last part of the above article on how to determine the dependent and independent variables.

CHALLENGES FACING DEVELOPMENT OF COOPERATIVE MOVEMENT IN TANA RIVER COUNTY

What is the IV and DV of this Research topic?

You can see in the last part of the above article an explanation about dependent and independent variables.

Dear Maur, what you just want to do is to describe the challenges. No need for a conceptual framework.

Hey, I really appreciate your explanation however I’m having a hard time figuring out the IV and DV on the topic about fish kill, can you help me?

I am requested to write 50 variables in my research as per my topic which is about street vending. I am really clueless.

Hi Regoniel…your articles are much more guiding….pls am writing my thesis on impact of insurgency on Baga Road fish market Maiduguri.

How will my conceptual framework looks like What do I need to talk on

Dear Alhaji, just be clear about what you want to do. Your research question must be clearly stated before you build your conceptual framework.

• Pingback: How to Analyze Frequency Data | SimplyEducate.Me December 4, 2018

Thanks so much ! This article is so much simple to my understanding. A friend of my referred me to this site and I am so greatful. Please Sir, when writing the dependent and independent variables should it be in a table form ?

Dear Grace, Good day. I don’t understand what you mean. But if your school requires that the independent and dependent variables be written in table form, I see no problem with that. It’s just a way for you to clearly show what variables you are analyzing. And you need to justify that.

Can you please give me what are the possible variables in terms of installation of street lights along barangay roads of calauan, laguna: an assessment?

Hello sir, sorry to bother you but what are the guidelines for writing a good report

Guidelines for writing a good research report?

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Operationalize a variable: a step-by-step guide to quantifying your research constructs.

Operationalizing a variable is a fundamental step in transforming abstract research constructs into measurable entities. This process allows researchers to quantify variables, enabling the empirical testing of hypotheses within quantitative research. The guide provided here aims to demystify the operationalization process with a structured approach, equipping scholars with the tools to translate theoretical concepts into practical, quantifiable measures.

Key Takeaways

• Operationalization is crucial for converting theoretical constructs into measurable variables, forming the backbone of empirical research.
• Identifying the right variables involves distinguishing between constructs and variables, and selecting those that align with the research objectives.
• The validity and reliability of measurements are ensured by choosing appropriate measurement instruments and calibrating them for consistency.
• Quantitative analysis of qualitative data requires careful operationalization to maintain the integrity and applicability of research findings.
• Operationalization impacts research outcomes by influencing study validity, generalizability, and contributing to the academic field's advancement.

Understanding the Concept of Operationalization in Research

Defining operationalization.

Operationalization is the cornerstone of quantitative research, transforming abstract concepts into measurable entities. It is the process by which you translate theoretical constructs into variables that can be empirically measured. This crucial step allows you to quantify the phenomena of interest, paving the way for systematic investigation and analysis.

To operationalize a variable effectively, you must first clearly define the construct and then determine the specific ways in which it can be observed and quantified. For instance, if you're studying the concept of 'anxiety,' you might operationalize it by measuring heart rate, self-reported stress levels, or the frequency of anxiety-related behaviors.

Consider the following aspects when operationalizing your variables:

• The type of variable (e.g., binary, continuous, categorical)
• The units of measurement (e.g., dollars, frequency, Likert scale)
• The method of data collection (e.g., surveys, observations, physiological measures)

By meticulously defining and measuring your variables, you ensure that your research can be rigorously tested and validated, contributing to the robustness and credibility of your findings.

The Role of Operationalization in Quantitative Research

In quantitative research, operationalization is the cornerstone that bridges the gap between abstract concepts and measurable outcomes. It involves defining your research variables in practical, quantifiable terms, allowing for precise data collection and analysis. Operationalization transforms theoretical constructs into indicators that can be empirically tested , ensuring that your study can be objectively evaluated against your hypotheses.

Operationalization is not just about measurement, but about the meaning behind the numbers. It requires careful consideration to select the most appropriate indicators for your variables. For instance, if you're studying educational achievement, you might operationalize this as GPA, standardized test scores, or graduation rates. Each choice has implications for what aspect of 'achievement' you're measuring:

• GPA reflects consistent performance across a variety of subjects.
• Standardized test scores may indicate aptitude in specific areas.
• Graduation rates can signify the completion of an educational milestone.

By operationalizing variables effectively, you lay the groundwork for a robust quantitative study. This process ensures that your research can be replicated and that your findings contribute meaningfully to the existing body of knowledge.

Differences Between Endogenous and Exogenous Variables

In the realm of research, understanding the distinction between endogenous and exogenous variables is crucial for designing robust experiments and drawing accurate conclusions. Endogenous variables are those that are influenced within the context of the study, often affected by other variables in the system. In contrast, exogenous variables are external factors that are not influenced by the system under study but can affect endogenous variables.

When operationalizing variables, it is essential to identify which are endogenous and which are exogenous to establish clear causal relationships. Exogenous variables are typically manipulated to observe their effect on endogenous variables, thereby testing hypotheses about causal links. For example, in a study on education outcomes, student motivation might be an endogenous variable, while teaching methods could be an exogenous variable manipulated by the researcher.

Consider the following points to differentiate between these two types of variables:

• Endogenous variables are outcomes within the system, subject to influence by other variables.
• Exogenous variables serve as inputs or causes that can be controlled or manipulated.
• The operationalization of endogenous variables requires careful consideration of how they are measured and how they interact with other variables.
• Exogenous variables, while not requiring operationalization, must be selected with an understanding of their potential impact on the system.

Identifying Variables for Operationalization

Distinguishing between variables and constructs.

In the realm of research, it's crucial to differentiate between variables and constructs. A variable is a specific, measurable characteristic that can vary among participants or over time. Constructs, on the other hand, are abstract concepts that are not directly observable and must be operationalized into measurable variables. For example, intelligence is a construct that can be operationalized by measuring IQ scores, which are variables.

Variables can be classified into different types , each with its own method of measurement. Here's a brief overview of these types:

• Continuous: Can take on any value within a range (e.g., height, weight).
• Ordinal: Represent order without specifying the magnitude of difference (e.g., socioeconomic status levels).
• Nominal: Categories without a specific order (e.g., types of fruit).
• Binary: Two categories, often representing presence or absence (e.g., employed/unemployed).
• Count: The number of occurrences (e.g., number of visits to a website).

When you embark on your research journey, ensure that you clearly identify each construct and the corresponding variable that will represent it in your study. This clarity is the foundation for a robust and credible research design.

Criteria for Selecting Variables

When you embark on the journey of operationalizing variables for your research, it is crucial to apply a systematic approach to variable selection. Variables should be chosen based on their relevance to your research questions and hypotheses , ensuring that they directly contribute to the investigation of your theoretical constructs.

Consider the type of variable you are dealing with—whether it is continuous, ordinal, nominal, binary, or count. Each type has its own implications for how data will be collected and analyzed. For instance, continuous variables allow for a wide range of values, while binary variables are restricted to two possible outcomes. Here is a brief overview of variable types and their characteristics:

• Continuous : Can take on any value within a range
• Ordinal : Values have a meaningful order but intervals are not necessarily equal
• Nominal : Categories without a meaningful order
• Binary : Only two possible outcomes
• Count : Integer values that represent the number of occurrences

Additionally, ensure that the levels of the variable encompass all possible values and that these levels are clearly defined. For binary and ordinal variables, this means specifying the two outcomes or the order of values, respectively. For continuous variables, define the range and consider using categories like 'above X' or 'below Y' if there are no natural bounds to the values.

Lastly, the proxy attribute of the variable should be considered. This refers to the induced variations or treatment conditions in your experiment. For example, if you are studying the effect of a buyer's budget on purchasing decisions, the proxy attribute might include different budget levels such as $5, $10, $20, and $40.

Developing Hypotheses and Research Questions

After grasping the fundamentals of your research domain, the next pivotal step is to develop a clear and concise hypothesis. This hypothesis will serve as the foundation for your experimental design and guide the direction of your study. Formulating a hypothesis requires a deep understanding of the variables at play and their potential interrelations . It's essential to ensure that your hypothesis is testable and that you have a structured plan for how to test it.

Once your hypothesis is established, you'll need to craft research questions that are both specific and measurable. These questions should stem directly from your hypothesis and aim to dissect the larger inquiry into more manageable segments. Here's how to find research question: start by identifying key outcomes and potential causes that might affect these outcomes. Then, design an experiment to induce variation in the causes and measure the outcomes. Remember, the clarity of your research questions will significantly impact the effectiveness of your data analysis later on.

To aid in this process, consider the following steps:

• Synthesize the existing literature to identify gaps and opportunities for further investigation.
• Define a clear problem statement that your research will address.
• Establish a purpose statement that guides your inquiry without advocating for a specific outcome.
• Develop a conceptual and theoretical framework to underpin your research.
• Formulate quantitative and qualitative research questions that align with your hypothesis and frameworks.

Effective experimental design involves identifying variables, establishing hypotheses, choosing sample size, and implementing randomization and control groups to ensure reliable and meaningful research results.

Choosing the Right Measurement Instruments

Types of measurement instruments.

When you embark on the journey of operationalizing your variables, selecting the right measurement instruments is crucial. These instruments are the tools that will translate your theoretical constructs into observable and measurable data. Understanding the different types of measurement instruments is essential for ensuring that your data accurately reflects the constructs you are studying.

Measurement instruments can be broadly categorized into five types: continuous, ordinal, nominal, binary, and count. Each type is suited to different kinds of data and research questions. For instance, a continuous variable, like height, can take on any value within a range, while an ordinal variable represents ordered categories, such as a satisfaction scale.

Here is a brief overview of the types of measurement instruments:

• Continuous : Can take on any value within a range; e.g., temperature, weight.
• Ordinal : Represents ordered categories; e.g., Likert scales for surveys.
• Nominal : Categorizes data without a natural order; e.g., types of fruit, gender.
• Binary : Has only two categories; e.g., yes/no questions, presence/absence.
• Count : Represents the number of occurrences; e.g., the number of visits to a website.

Choosing the appropriate instrument involves considering the nature of your variable, the level of detail required, and the context of your research. For example, if you are measuring satisfaction levels, you might use a Likert scale, which is an ordinal type of instrument. On the other hand, if you are counting the number of times a behavior occurs, a count instrument would be more appropriate.

Ensuring Validity and Reliability

To ensure the integrity of your research, it is crucial to select measurement instruments that are both valid and reliable. Validity refers to the degree to which an instrument accurately measures what it is intended to measure. Reliability, on the other hand, denotes the consistency of the instrument across different instances of measurement.

When choosing your instruments, consider the psychometric properties that have been documented in large cohort studies or previous validations. For instance, scales should have demonstrated internal consistency reliability, which can be assessed using statistical measures such as Cronbach's alpha. It is also important to calibrate your instruments to maintain consistency over time and across various contexts.

Here is a simplified checklist to guide you through the process:

• Review literature for previously validated instruments
• Check for cultural and linguistic validation if applicable
• Assess internal consistency reliability (e.g., Cronbach's alpha)
• Perform pilot testing and calibration
• Plan for ongoing assessment of instrument performance

Calibrating Instruments for Consistency

Calibration is a critical step in ensuring that your measurement instruments yield reliable and consistent results. It involves adjusting the instrument to align with a known standard or set of standards. Calibration must be performed periodically to maintain the integrity of data collection over time.

When calibrating instruments, you should follow a systematic approach. Here is a simple list to guide you through the process:

• Identify the standard against which the instrument will be calibrated.
• Compare the instrument's output with the standard.
• Adjust the instrument to minimize any discrepancies.
• Document the calibration process and results for future reference.

It's essential to recognize that different instruments may require unique calibration methods. For instance, a scale used for measuring weight will be calibrated differently than a thermometer used for temperature. Below is an example of how calibration data might be recorded in a table format:

Remember, the goal of calibration is not just to adjust the instrument but to understand its behavior and limitations. This understanding is crucial for interpreting the data accurately and ensuring that your research findings are robust and reliable.

Quantifying Variables: From Theory to Practice

Translating theoretical constructs into measurable variables.

Operationalizing a variable is the cornerstone of empirical research, transforming abstract concepts into quantifiable measures. Your ability to effectively operationalize variables is crucial for testing hypotheses and advancing knowledge within your field. Begin by identifying the key constructs of your study and consider how they can be observed in the real world.

For instance, if your research involves the construct of 'anxiety,' you must decide on a method to measure it. Will you use a self-reported questionnaire, physiological indicators, or a combination of both? Each method has implications for the type of data you will collect and how you will interpret it. Below is an example of how you might structure this information:

• Construct: Anxiety
• Measurement Method: Self-reported questionnaire
• Instrument: Beck Anxiety Inventory
• Scale: 0 (no anxiety) to 63 (severe anxiety)

Once you have chosen an appropriate measurement method, ensure that it aligns with your research objectives and provides valid and reliable data. This process may involve adapting existing instruments or developing new ones to suit the specific needs of your study. Remember, the operationalization of your variables sets the stage for the empirical testing of your theoretical framework.

Assigning Units and Scales of Measurement

Once you have translated your theoretical constructs into measurable variables, the next critical step is to assign appropriate units and scales of measurement. Units are the standards used to quantify the value of your variables, ensuring consistency and robustness in your data. For instance, if you are measuring time spent on a task, your unit might be minutes or seconds.

Variables can be categorized into types such as continuous, ordinal, nominal, binary, or count. This classification aids in selecting the right scale of measurement and is crucial for the subsequent statistical analysis. For example, a continuous variable like height would be measured in units such as centimeters or inches, while an ordinal variable like satisfaction level might be measured on a Likert scale ranging from 'Very Dissatisfied' to 'Very Satisfied'.

Here is a simple table illustrating different variable types and their potential units or scales:

Remember, the choice of units and scales will directly impact the validity of your research findings. It is essential to align them with your research objectives and the nature of the data you intend to collect.

Handling Qualitative Data in Quantitative Analysis

When you embark on the journey of operationalizing variables, you may encounter the challenge of incorporating qualitative data into a quantitative framework. Operationalization is the process of translating abstract concepts into measurable variables in research, which is crucial for ensuring the study's validity and reliability. However, qualitative data, with its rich, descriptive nature, does not lend itself easily to numerical representation.

To effectively handle qualitative data, you must first systematically categorize the information. This can be done through coding, where themes, patterns, and categories are identified. Once coded, these qualitative elements can be quantified. For example, the frequency of certain themes can be counted, or the presence of specific categories can be used as binary variables (0 for absence, 1 for presence).

Consider the following table that illustrates a simple coding scheme for qualitative responses:

This table represents a basic way to transform qualitative feedback into quantifiable data, which can then be analyzed using statistical methods. It is essential to ensure that the coding process is consistent and that the interpretation of qualitative data remains faithful to the original context. By doing so, you can enrich your quantitative analysis with the depth that qualitative insights provide, while maintaining the rigor of a quantitative approach.

Designing the Experimental Framework

Creating a structured causal model (scm).

In your research, constructing a Structured Causal Model (SCM) is a pivotal step that translates your theoretical understanding into a practical framework. SCMs articulate the causal relationships between variables through a set of equations or functions, allowing you to make clear and testable hypotheses about the phenomena under study. By defining these relationships explicitly, SCMs facilitate the prediction and manipulation of outcomes in a controlled experimental setting.

When developing an SCM, consider the following steps:

• Identify the key variables and their hypothesized causal connections.
• Choose the appropriate mathematical representation for each relationship (e.g., linear, logistic).
• Determine the directionality of the causal effects.
• Specify any interaction terms or non-linear dynamics that may be present.
• Validate the SCM by ensuring it aligns with existing theoretical and empirical evidence.

Remember, the SCM is not merely a statistical tool; it embodies your hypotheses about the causal structure of your research question. As such, it should be grounded in theory and prior research, while also being amenable to empirical testing. The SCM approach circumvents the need to search for causal structures post hoc, as it requires you to specify the causal framework a priori, thus avoiding common pitfalls such as 'bad controls' and ensuring that exogenous variation is properly accounted for.

Determining the Directionality of Variables

In the process of operationalizing variables, understanding the directionality is crucial. Directed acyclic graphs (DAGs) serve as a fundamental tool in delineating causal relationships between variables. The direction of the arrow in a DAG explicitly indicates the causal flow, which is essential for constructing a valid Structural Causal Model (SCM).

When you classify variables, you must consider their types —continuous, ordinal, nominal, binary, or count. This classification not only aids in understanding the variables' nature but also in selecting the appropriate statistical methods for analysis. Here is a simple representation of variable types and their characteristics:

By integrating the directionality and type of variables into your research design, you ensure that the operationalization is aligned with the underlying theoretical framework. This alignment is pivotal for the subsequent phases of data collection and analysis, ultimately impacting the robustness of your research findings.

Pre-Analysis Planning and Experimental Design

As you embark on the journey of experimental design, it's crucial to have a clear pre-analysis plan. This plan will guide you through the data collection process and ensure that your analysis is aligned with your research objectives. Developing a pre-analysis plan is akin to creating a roadmap for your research , providing direction and structure to the analytical phase of your study.

To mitigate thesis anxiety , a structured approach to experimental design is essential. Begin by identifying your main research questions and hypotheses. Then, delineate the methods you'll use to test these hypotheses, including the statistical models and the criteria for interpreting results. Here's a simplified checklist to help you organize your pre-analysis planning:

• Define the research questions and hypotheses
• Select the statistical methods for analysis
• Establish criteria for interpreting the results
• Plan for potential contingencies and alternative scenarios

Remember, the robustness of your findings hinges on the meticulousness of your experimental design. By adhering to a well-thought-out pre-analysis plan, you not only enhance the credibility of your research but also pave the way for a smoother, more confident research experience.

Data Collection Strategies

Selecting appropriate data collection methods.

When you embark on the journey of research, selecting the right data collection methods is pivotal to the integrity of your study. It's essential to identify the research method as qualitative, quantitative, or mixed, and provide a clear overview of how the study will be conducted. This includes detailing the instruments or methods you will use, the subjects involved, and the setting of your research.

To ensure that your findings are reliable and valid, it is crucial to modify the data collection process , refine variables, and implement controls. This is where understanding how to find literature on existing methods can be invaluable. Literature reviews help you evaluate scientific literature for measures with strong psychometric properties and use cases relevant to your study. Consider the following steps to guide your selection process:

• Review criteria and priorities for construct selection.
• Evaluate relevant scientific literature for established measures.
• Examine measures used in large epidemiologic studies for alignment opportunities.
• Coordinate internally to avoid duplication and ensure comprehensive coverage.

By meticulously selecting data collection methods that align with your research objectives and hypotheses, you lay the groundwork for insightful and impactful research findings.

Sampling Techniques and Population Considerations

When you embark on the journey of research, selecting the appropriate sampling techniques is crucial to the integrity of your study. Sampling enables you to focus on a smaller subset of participants, which is a practical approach to studying larger populations. It's essential to consider the balance between a sample that is both representative of the population and manageable in size.

To ensure that your sample accurately reflects the population, you must be meticulous in your selection process. Various sampling methods are available, each with its own advantages and disadvantages. For instance, random sampling can help eliminate bias, whereas stratified sampling ensures specific subgroups are represented. Below is a list of common sampling techniques and their primary characteristics:

• Random Sampling : Each member of the population has an equal chance of being selected.
• Stratified Sampling : The population is divided into subgroups, and random samples are taken from each.
• Cluster Sampling : The population is divided into clusters, and a random sample of clusters is studied.
• Convenience Sampling : Participants are selected based on their availability and willingness to take part.
• Snowball Sampling : Existing study subjects recruit future subjects from among their acquaintances.

Remember, the choice of sampling method will impact the generalizability of your findings. It's imperative to align your sampling strategy with your research questions and the practical constraints of your study.

Ethical Considerations in Data Collection

When you embark on data collection, ethical considerations must be at the forefront of your planning. Ensuring the privacy and confidentiality of participants is paramount. You must obtain informed consent, which involves clearly communicating the purpose of your research, the procedures involved, and any potential risks or benefits to the participants.

Consider the following points to uphold ethical standards:

• Respect for anonymity and confidentiality
• Voluntary participation with the right to withdraw at any time
• Minimization of any potential harm or discomfort
• Equitable selection of participants

It is also essential to consider the sensitivity of the information you are collecting and the context in which it is gathered. For instance, when dealing with vulnerable populations or sensitive topics, additional safeguards should be in place to protect participant welfare. Lastly, ensure that your data collection methods comply with all relevant laws and institutional guidelines.

Analyzing and Interpreting Quantified Data

Statistical analysis techniques.

Once you have collected your data, it's time to analyze it using appropriate statistical techniques. The choice of analysis method depends on the nature of your data and the research questions you aim to answer. For instance, if you're looking to understand relationships between variables, regression analysis might be the method of choice. Choosing the right statistical method is crucial as it influences the validity of your research findings.

Several software packages can aid in this process, such as SPSS, R, or Python libraries like 'pandas' and 'numpy' for data manipulation, and 'pingouin' or 'stats' for statistical testing. Each package has its strengths, and your selection should align with your research needs and proficiency level.

To illustrate, consider the following table summarizing different statistical tests and their typical applications:

After conducting the appropriate analyses, interpreting the results is your next step. This involves understanding the statistical significance, effect sizes, and confidence intervals to draw meaningful conclusions about your research hypotheses.

Understanding the Implications of Data

Once you have quantified your research variables, the next critical step is to understand the implications of the data you've collected. Interpreting the data correctly is crucial for drawing meaningful conclusions that align with your research objectives. It's essential to recognize that data does not exist in a vacuum; it is influenced by the context in which it was gathered. For instance, quantitative data in the form of surveys, polls, and questionnaires can yield precise results, but these must be considered within the broader social and environmental context to avoid misleading interpretations.

The process of data analysis often reveals patterns and relationships that were not initially apparent. However, caution is advised when inferring causality from these findings. The presence of a correlation does not imply causation, and additional analysis is required to establish causal links. Below is a simplified example of how data might be presented and the initial observations that could be drawn:

In this table, a strong positive correlation is observed between Variable A and Variable B, suggesting a potential relationship worth further investigation. Finally, the interpretation of data should always be done with an awareness of its limitations and the potential for different conclusions when analyzing it independently. This understanding is vital for ensuring that your research findings are robust, reliable, and ultimately, valuable to the field of study.

Reporting Findings with Precision

When you report the findings of your research, precision is paramount. Ensure that your data is presented clearly , with all necessary details to support your conclusions. This includes specifying the statistical methods used, such as regression analysis, and the outcomes derived from these methods. For example, when reporting statistical results, it's common to include measures like mean, standard deviation (SD), range, median, and interquartile range (IQR).

Consider the following table as a succinct way to present your data:

In addition to numerical data, provide a narrative that contextualizes your findings within the broader scope of your research. Discuss any potential biases, such as item non-response, and how they were addressed. The use of Cronbach's alpha coefficients to assess the reliability of scales is an example of adding depth to your analysis. By combining quantitative data with qualitative insights, you create a comprehensive picture that enhances the credibility and impact of your research.

Ensuring the Robustness of Operationalized Variables

Cross-validation and replication studies.

In your research endeavors, cross-validation and replication studies are pivotal for affirming the robustness of your operationalized variables. Principles of replicability include clear methodology, transparent data sharing, independent verification, and reproducible analysis. These principles are not just theoretical ideals; they are practical steps that ensure the reliability of scientific findings. Documentation and collaboration are key for reliable research in scientific progress, and they facilitate the critical examination of results by the wider research community.

When you conduct replication studies, you are essentially retesting the operationalized variables in new contexts or with different samples. This can reveal the generalizability of your findings and highlight any contextual factors that may influence the outcomes. For instance, a study's results may vary when different researchers analyze the data independently, underscoring the importance of context in social sciences. Below is a list of considerations to keep in mind when planning for replication studies:

• Ensure that the methodology is thoroughly documented and shared.
• Seek independent verification of the findings by other researchers.
• Test the operationalized variables across different populations and settings.
• Be prepared for results that may differ from the original study, and explore the reasons why.

By adhering to these practices, you contribute to the cumulative knowledge in your field and enhance the credibility of your research.

Dealing with Confounding Variables

In your research, identifying and managing confounding variables is crucial to ensure the integrity of your findings. Confounding variables are external factors that can influence the outcome of your study, potentially leading to erroneous conclusions if not properly controlled. To mitigate their effects, it's essential to first recognize these variables during the design phase of your research.

Once identified, you can employ various strategies to control for confounders. Here are some common methods:

• Randomization : Assign subjects to treatment or control groups randomly to evenly distribute confounders.
• Matching : Pair subjects with similar characteristics to balance out confounding variables.
• Statistical control : Use regression or other statistical techniques to adjust for the influence of confounders.

Remember, the goal is to isolate the relationship between the independent and dependent variables by minimizing the impact of confounders. This process often involves revisiting and refining your experimental design to ensure that your results will be as accurate and reliable as possible.

Continuous Improvement of Measurement Methods

In the pursuit of scientific rigor, you must recognize the necessity for the continuous improvement of measurement methods. Measurements of abstract constructs have been criticized for their theoretical limitations, underscoring the importance of refinement and evolution in operationalization. To enhance the robustness of your research, consider the following steps:

• Regularly review the units and standards used to represent your variables' quantified values.
• Prioritize the inclusion of previously validated concepts and measures, especially those with strong psychometric properties across multiple languages and cultural contexts.
• Conduct follow-on experiments to test the reliability and validity of your measures.
• Engage in cross-validation with other studies to ensure consistency and generalizability.

By committing to these practices, you ensure that your operationalization process remains dynamic and responsive to new insights and methodologies.

The Impact of Operationalization on Research Outcomes

Influence on study validity.

The operationalization of variables is pivotal to the validity of your study. Operationalization ensures that the constructs you are examining are not only defined but also measured in a way that is consistent with your research objectives. This process directly impacts the credibility of your findings and the conclusions you draw.

When you operationalize a variable, you translate abstract concepts into measurable indicators . This translation is crucial because it allows you to collect data that can be analyzed statistically. For instance, if you are studying the concept of 'anxiety,' you might operationalize it by measuring heart rate, self-reported stress levels, or the frequency of anxiety-related behaviors.

Consider the following aspects to ensure that your operationalization strengthens the validity of your study:

• Conceptual clarity : Define your variables clearly to avoid ambiguity.
• Construct validity : Choose measures that accurately capture the theoretical constructs.
• Reliability : Use measurement methods that yield consistent results over time.
• Contextual relevance : Ensure that your operationalization is appropriate for the population and setting of your study.

By meticulously operationalizing your variables, you not only bolster the validity of your research but also enhance the trustworthiness of your findings within the scientific community.

Operationalization and Research Generalizability

The process of operationalization is pivotal in determining the generalizability of your research findings. Generalizability refers to the extent to which the results of a study can be applied to broader contexts beyond the specific conditions of the original research. By carefully operationalizing variables, you ensure that the constructs you measure are not only relevant within your study's framework but also resonate with external scenarios.

When operationalizing variables, consider the universality of the constructs. Are the variables culturally bound, or do they hold significance across different groups? This consideration is crucial for cross-cultural studies or research aiming for wide applicability. To illustrate, here's a list of factors that can influence generalizability:

• Cultural relevance of the operationalized variables
• The representativeness of the sample population
• The settings in which data is collected
• The robustness of the measurement instruments

Ensuring that these factors are addressed in your operationalization strategy can significantly enhance the generalizability of your research. Remember, the more universally applicable your operationalized variables are, the more impactful your research can be in contributing to the global body of knowledge.

Contributions to the Field of Study

Operationalization is not merely a methodological step in research; it is a transformative process that can significantly enhance the impact of your study. By meticulously converting theoretical constructs into measurable variables, you contribute to the field by enabling empirical testing of theories and facilitating the accumulation of knowledge. This process of quantification allows for the precise replication of research , which is essential for the advancement of science.

Your contributions through operationalization can be manifold. They may include the development of new measurement instruments, the refinement of existing scales, or the introduction of innovative ways to quantify complex constructs. Here's how your work can contribute to the field:

• Providing a clear basis for empirical inquiry
• Enhancing the precision of research findings
• Enabling cross-study comparisons and meta-analyses
• Informing policy decisions and practical applications

Each of these points reflects the broader significance of operationalization. It's not just about the numbers; it's about the clarity and applicability of research that can inform future studies, contribute to theory development, and ultimately, impact real-world outcomes.

Challenges and Solutions in Operationalizing Variables

Common pitfalls in operationalization.

Operationalizing variables is a critical step in research, yet it is fraught with challenges that can compromise the integrity of your study. One major pitfall is the misidentification of variables, which can lead to incorrect assumptions about causal relationships. Avoiding the inclusion of 'bad controls' that can confound results is essential. For instance, when dealing with observational data that includes many variables, it's easy to misspecify a model, leading to biased estimates.

Another common issue arises when researchers infer causal structure ex-post , which can be problematic without a correctly specified Directed Acyclic Graph (DAG). This underscores the importance of identifying causal structures ex-ante to ensure that the operationalization aligns with the true nature of the constructs being studied. Here are some key considerations to keep in mind:

• Ensure clarity in distinguishing between variables and constructs.
• Select variables based on clear criteria that align with your research questions.
• Validate the causal structure of your data before operationalization.

By being mindful of these aspects, you can mitigate the risks associated with operationalization and enhance the credibility of your research findings.

Adapting Operationalization in Evolving Research Contexts

As research contexts evolve, so must the methods of operationalization. The dynamic nature of social sciences, for instance, requires that operationalization be flexible enough to account for changes in environment and population. Outcomes that are valid in one context may not necessarily apply to another , necessitating a reevaluation of operational variables.

In the face of such variability, you can employ a structured approach to adapt your operationalization. Consider the following steps:

• Review the theoretical underpinnings of your constructs.
• Reassess the variables and their definitions in light of the new context.
• Modify measurement instruments to better capture the nuances of the changed environment.
• Conduct pilot studies to test the revised operationalization.

Furthermore, the integration of automation in research allows for a more nuanced operationalization process. You can select variables, define their operationalization, and customize statistical analyses to fit the evolving research landscape. This adaptability is crucial in ensuring that your research remains relevant and accurate over time.

Case Studies and Best Practices

In the realm of research, the operationalization of variables is a critical step that transforms abstract concepts into measurable entities. Case studies often illustrate the practical application of these principles, providing you with a blueprint for success. For instance, the ThinkIB guide on DP Psychology emphasizes the importance of clearly stating the independent and dependent variables when formulating a hypothesis. This clarity is paramount for the integrity of your research design.

Best practices suggest a structured approach to operationalization. Begin by identifying your variables and ensuring they align with your research objectives. Next, select appropriate measurement instruments that offer both validity and reliability. Finally, design your study to account for potential confounding variables and employ statistical techniques that will yield precise findings. Below is a list of steps that encapsulate these best practices:

• Clearly define your variables.
• Choose measurement instruments with care.
• Design a study that minimizes bias.
• Analyze data with appropriate statistical methods.
• Report findings with accuracy and detail.

By adhering to these steps and learning from the experiences of others, you can enhance the robustness of your research and contribute meaningful insights to your field of study.

Operationalizing variables is a critical step in research and data analysis, but it comes with its own set of challenges. From ensuring reliability and validity to dealing with the complexities of real-world data, researchers and analysts often need to find innovative solutions. If you're grappling with these issues, don't worry! Our website offers a wealth of resources and expert guidance to help you navigate the intricacies of operationalizing variables. Visit us now to explore our articles, tools, and support services designed to streamline your research process.

In conclusion, operationalizing variables is a critical step in the research process that transforms abstract concepts into measurable entities. This guide has delineated a systematic approach to quantifying research constructs, ensuring that they are empirically testable and scientifically valid. By carefully defining variables, selecting appropriate measurement scales, and establishing reliable and valid indicators, researchers can enhance the rigor of their studies and contribute to the advancement of knowledge in their respective fields. It is our hope that this step-by-step guide has demystified the operationalization process and provided researchers with the tools necessary to embark on their empirical inquiries with confidence and precision.

Frequently Asked Questions

What is operationalization in research.

Operationalization is the process of defining a research construct in measurable terms, specifying the exact operations involved in measuring it, and determining the method of data collection.

How do I differentiate between endogenous and exogenous variables?

Endogenous variables are the outcomes within a study that are influenced by other variables, while exogenous variables are external factors that influence the endogenous variables but are not influenced by them within the study's scope.

What criteria should I consider when selecting variables for operationalization?

Criteria include relevance to the research question, measurability, the potential for valid and reliable data collection, and the ability to be manipulated or observed within the study's design.

Why is ensuring validity and reliability important in measurement?

Validity ensures that the instrument measures what it's supposed to measure, while reliability ensures that the measurement results are consistent and repeatable over time.

How do I handle qualitative data in quantitative analysis?

Qualitative data can be quantified through coding, categorization, and the use of scales or indices to convert non-numerical data into a format that can be statistically analyzed.

What is a Structured Causal Model (SCM) in experimental design?

An SCM is a conceptual model that outlines the causal relationships between variables, helping researchers to understand and predict the effects of manipulating one or more variables.

What are some common pitfalls in operationalizing variables?

Common pitfalls include poorly defined constructs, using unreliable or invalid measurement instruments, and failing to account for confounding variables that may affect the results.

How does operationalization impact research outcomes?

Proper operationalization leads to more accurate and meaningful data, which in turn affects the validity and generalizability of the research findings, contributing to the field of study.

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• Cookies & Privacy
• GETTING STARTED
• Introduction
• FUNDAMENTALS
• Acknowledgements
• Research questions & hypotheses
• Concepts, constructs & variables
• Research limitations
• Getting started
• Sampling Strategy
• Research Quality
• Research Ethics
• Data Analysis

Types of variables

Understanding the types of variables you are investigating in your dissertation is necessary for all types of quantitative research design , whether you using an experimental , quasi-experimental , relationship-based or descriptive research design. When you carry out your dissertation, you may need to measure , manipulate and/or control the variables you are investigating. In the section on Research Designs , you can learn more about the various types of quantitative research design. In this article, we present and illustrate the different types of variables you may come across in your dissertation. First, we discuss the main groups of variables: categorical variables and continuous variables . Second, we explain what dependent and independent variables are. This will provide you with one of the foundations required to tackle a dissertation based on a quantitative research design.

Categorical and continuous variables

Dependent and independent variables, ambiguities in classifying variables.

There are two groups of variables that you need to know about: categorical variables and continuous variables . We use the word groups of variables because both categorical and continuous variables include additional types of variable. However, there can also be some ambiguities when deciding whether a variable is categorical or continuous. We discuss the two groups of variable, as well as these potential ambiguities, in the sections that follow:

Categorical variables

Categorical variables are also known as qualitative (or discrete ) variables . These categorical variables can be further classified as being nominal , dichotomous or ordinal variables. Each of these types of categorical variable (i.e., nominal , dichotomous and ordinal ) has what are known as categories or levels . These categories or levels are the descriptions that you give a variable that help to explain how variables should be measured, manipulated and/or controlled. Take the following example:

Career choices of university students You are interested in the career choices of university students . You could ask university students a number of closed questions related to their career choices. For example: What is your planned occupation? What is the most important factor influencing your career choice?

The first question highlights the use of categories and the second question levels . For example:

Question 1 : What is your planned occupation? Variables with categories

Architect Attorney Biochemist Engineer Dentist Doctor Entrepreneur Social Worker Teacher ETC...

Career prospects Nature of the work Physical working conditions Salary and benefits ETC...

What is important to note about the categories in question 1 and the levels in question 2 is that these will be created by you. Ideally, you will have included these categories or levels based on some primary or secondary research. Ultimately, you choose which categories or levels to include and how many categories or levels there should be.

Each of these types of categorical variable (i.e., nominal , dichotomous and ordinal ) are described below with associated examples:

Nominal variables

The following are examples of nominal variables. These nominal variables could address questions like:

These examples highlight two core characteristics of nominal variables:

Nominal variables have two or more categories.

Nominal variables do not have an intrinsic order.

When we talk about nominal variables not having an intrinsic order , we mean that they can only have categories (e.g., black, blond, brown and red hair); not levels (e.g., a Likert scale from 1 to 5).

Dichotomous variables

The following are examples of dichotomous variables. These dichotomous variables could address questions like:

Dichotomous variables are nominal variables that have just two categories. They have a number of characteristics:

Dichotomous variables are designed to give you an either/or response

For example, you are either male or female. You either like watching television (i.e., you answer YES ) or you don't (i.e., you answer NO ).

Dichotomous variables can either be fixed or designed

For example, some variables (e.g., your sex ) can only be dichotomous (i.e., you can only be male or female ). They are therefore fixed . In other cases, dichotomous variables are designed by the researcher. For example, take the question: Do you like watching television? We have determined that the respondent can only select YES (i.e., I like watching television) or NO (i.e., I don't like watching television). However, another researcher could provide the respondent with more than two categories to this question (e.g., most of the time, sometimes , hardly ever ). Where more than two categories are used, these variables become known as nominal variables rather than dichotomous ones.

Ordinal variables

Just like nominal variables, ordinal variables have two or more categories. However, unlike nominal variables, ordinal variables can also be ordered or ranked (i.e., they have levels ). For example, take the following example of an ordinal variable:

So if you asked someone if they liked the policies of the Democratic Party and you presented them with the following three categories: Not very much , They are OK , or Yes, a lot ; you have an ordinal variable. Why? Because you have 3 categories ? namely Not very much , They are OK , and Yes, a lot ? and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the three categories , we cannot place a value to them. For example, we cannot say that the response, They are OK , is twice as positive as the response, Not very much .

Other examples of ordinal variables are:

When it comes to Likert scales, as highlighted in the previous example, there can be some disagreement over whether these should be considered ordinal variables or continuous variables [see the section: Ambiguities in classifying variables ].

Continuous variables

Continuous variables, which are also known as quantitative variables, can be further classified a being either interval or ratio variables. Each of these types of continuous variable (i.e., interval and ratio ) has numerical properties. These numerical properties are the values by which continuous variables can be measured, manipulated and/or controlled. We illustrate the two types of continuous variable (i.e., interval and ratio ) and some associated values in the sections that follow:

Interval variables

Interval variables have a numerical value and can be measured along a continuum . Some examples of interval variables are:

However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable. This is because temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no temperature.

Ratio variables

Ratio variables are interval variables that meet an additional condition: a measurement value of 0 (zero) must mean that there is none of that variable. Some examples of ratio variables are:

Sometimes, the measurement scale for data is ordinal , but the variable is treated as though it were continuous . This is more often the case when using Likert scales. When a Likert scale has five values (e.g., strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree), it is treated as an ordinal variable. However, when a Likert scale has seven or more values (e.g., strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree), the variable is sometimes treated as a continuous variable. Nonetheless, this is a matter of dispute. Some researchers would argue that a Likert scale should never be treated as a continuous variable, even with seven levels/values.

Since you are responsible for setting the measurement scale for a variable, you will need to think carefully about how you characterise a variable. For example, social scientists may be more likely to consider the variable gender to be a nominal variable. This is because they view gender as having a number of categories, including male, female, bisexual and transsexual. By contrast, other researchers may simply view gender as a dichotomous variable, having just two categories: male and female. In such cases, it may be better to refer to the variable gender as sex .

A variable is not only something that you measure , but also something that you can manipulate and control for. An independent variable (sometimes called an experimental or predictor variable) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable). The dependent variable is simply that; a variable that is dependent on an independent variable(s). We discuss these concepts in the example below:

For example: Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons:

Some students spend more time revising for their test; and

Some students are naturally more intelligent than others.

Therefore, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. As such, the dependent and independent variables for the study are:

The dependent variable is simply that; a variable that is dependent on an independent variable(s). In our case, the test mark (i.e. the dependent variable) that a student achieves is dependent on revision time and intelligence (i.e., the independent variables). Whilst revision time and intelligence (i.e., independent variables) may (or may not) cause a change in the test mark (i.e., the dependent variable), the reverse is implausible. In other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent. This would not make any sense.

Therefore, the aim of the tutor's investigation is to examine whether these independent variables (i.e., revision time and IQ) result in a change in the dependent variable (i.e., the students' test scores). However, it is also worth noting that whilst this is the main aim of the experiment, the tutor may also be interested to know if the independent variables (i.e., revision time and IQ) are also connected in some way.

You can find out more about the different uses of variables, especially in quantitative research designs (i.e., descriptive , experimental , quasi-experimental and relationship-based research designs), in the section on Research Designs .

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Types of variables and commonly used statistical designs.

Jacob Shreffler ; Martin R. Huecker .

Affiliations

Last Update: March 6, 2023 .

• Definition/Introduction

Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study. [1]  Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis. [1]  Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of the types of variables and commonly used designs to facilitate this understanding. [2]

• Issues of Concern

Individuals who attempt to conduct research and choose an inappropriate design could select a faulty test and make flawed conclusions. This decision could lead to work being rejected for publication or (worse) lead to erroneous clinical decision-making, resulting in unsafe practice. [1]  By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. [3]

To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. [4]  Multiple types of variables determine the appropriate design.

Ordinal data (also sometimes referred to as discrete) provide ranks and thus levels of degree between the measurement. [5]  Likert items can serve as ordinal variables, but the Likert scale, the result of adding all the times, can be treated as a continuous variable. [6]  For example, on a 20-item scale with each item ranging from 1 to 5, the item itself can be an ordinal variable, whereas if you add up all items, it could result in a range from 20 to 100. A general guideline for determining if a variable is ordinal vs. continuous: if the variable has more than ten options, it can be treated as a continuous variable. [7]  The following examples are ordinal variables:

• Likert items
• Cancer stages
• Residency Year

Nominal, Categorical, Dichotomous, Binary

Other types of variables have interchangeable terms. Nominal and categorical variables describe samples in groups based on counts that fall within each category, have no quantitative relationships, and cannot be ranked. [8]  Examples of these variables include:

• Service (i.e., emergency, internal medicine, psychiatry, etc.)
• Mode of Arrival (ambulance, helicopter, car)

A dichotomous or a binary variable is in the same family as nominal/categorical, but this type has only two options. Binary logistic regression, which will be discussed below, has two options for the outcome of interest/analysis. Often used as (yes/no), examples of dichotomous or binary variables would be:

• Alive (yes vs. no)
• Insurance (yes vs. no)
• Readmitted (yes vs. no)

With this overview of the types of variables provided, we will present commonly used statistical designs for different scales of measurement. Importantly, before deciding on a statistical test, individuals should perform exploratory data analysis to ensure there are no issues with the data and consider type I, type II errors, and power analysis. Furthermore, investigators should ensure appropriate statistical assumptions. [9] [10]  For example, parametric tests, including some discussed below (t-tests, analysis of variance (ANOVA), correlation, and regression), require the data to have a normal distribution and that the variances within each group are similar. [6] [11]  After eliminating any issues based on exploratory data analysis and reducing the likelihood of committing type I and type II errors, a statistical test can be chosen. Below is a brief introduction to each of the commonly used statistical designs with examples of each type. An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs. 

Commonly Used Statistical Designs

Independent Samples T-test

An independent samples t-test allows a comparison of two groups of subjects on one (continuous) variable. Examples in biomedical research include comparing results of treatment vs. control group and comparing differences based on gender (male vs. female).

Example: Does adherence to the ketogenic diet (yes/no; two groups) have a differential effect on total sleep time (minutes; continuous)?

Paired T-test

A paired t-test analyzes one sample population, measuring the same variable on two different occasions; this is often useful for intervention and educational research.

Example :  Does participating in a research curriculum (one group with intervention) improve resident performance on a test to measure research competence (continuous)?

One-Way Analysis of Variance (ANOVA)

Analysis of variance (ANOVA), as an extension of the t-test, determines differences amongst more than two groups, or independent variables based on a dependent variable. [11]  ANOVA is preferable to conducting multiple t-tests as it reduces the likelihood of committing a type I error.

Example: Are there differences in length of stay in the hospital (continuous) based on the mode of arrival (car, ambulance, helicopter, three groups)?

Repeated Measures ANOVA

Another procedure commonly used if the data for individuals are recurrent (repeatedly measured) is a repeated-measures ANOVA. [1]  In these studies, multiple measurements of the dependent variable are collected from the study participants. [11]  A within-subjects repeated measures ANOVA determines effects based on the treatment variable alone, whereas mixed ANOVAs allow both between-group effects and within-subjects to be considered.

Within-Subjects Example: How does ketamine effect mean arterial pressure (continuous variable) over time (repeated measurement)?

Mixed Example: Does mean arterial pressure (continuous) differ between males and females (two groups; mixed) on ketamine throughout a surgical procedure (over time; repeated measurement)?  

Nonparametric Tests

Nonparametric tests, such as the Mann-Whitney U test (two groups; nonparametric t-test), Kruskal Wallis test (multiple groups; nonparametric ANOVA), Spearman’s rho (nonparametric correlation coefficient) can be used when data are ordinal or lack normality. [3] [5]  Not requiring normality means that these tests allow skewed data to be analyzed; they require the meeting of fewer assumptions. [11]

Example: Is there a relationship between insurance status (two groups) and cancer stage (ordinal)?  

A Chi-square test determines the effect of relationships between categorical variables, which determines frequencies and proportions into which these variables fall. [11]  Similar to other tests discussed, variants and extensions of the chi-square test (e.g., Fisher’s exact test, McNemar’s test) may be suitable depending on the variables. [8]

Example: Is there a relationship between individuals with methamphetamine in their system (yes vs. no; dichotomous) and gender (male or female; dichotomous)?

Correlation

Correlations (used interchangeably with ‘associations’) signal patterns in data between variables. [1]  A positive association occurs if values in one variable increase as values in another also increase. A negative association occurs if variables in one decrease while others increase. A correlation coefficient, expressed as r,  describes the strength of the relationship: a value of 0 means no relationship, and the relationship strengthens as r approaches 1 (positive relationship) or -1 (negative association). [5]

Example: Is there a relationship between age (continuous) and satisfaction with life survey scores (continuous)?

Linear Regression

Regression allows researchers to determine the degrees of relationships between a dependent variable and independent variables and results in an equation for prediction. [11]  A large number of variables are usable in regression methods.

Example: Which admission to the hospital metrics (multiple continuous) best predict the total length of stay (minutes; continuous)?

Binary Logistic Regression

This type of regression, which aims to predict an outcome, is appropriate when the dependent variable or outcome of interest is binary or dichotomous (yes/no; cured/not cured). [12]

Example: Which panel results (multiple of continuous, ordinal, categorical, dichotomous) best predict whether or not an individual will have a positive blood culture (dichotomous/binary)?

An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs.

(See Types of Variables and Statistical Designs Table 1)

• Clinical Significance

Though numerous other statistical designs and extensions of methods covered in this article exist, the above information provides a starting point for healthcare providers to become acquainted with variables and commonly used designs. Researchers should study types of variables before determining statistical tests to obtain relevant measures and valid study results. [6]  There is a recommendation to consult a statistician to ensure appropriate usage of the statistical design based on the variables and that the assumptions are upheld. [1]  With the variety of statistical software available, investigators must a priori understand the type of statistical tests when designing a study. [13]  All providers must interpret and scrutinize journal publications to make evidence-based clinical decisions, and this becomes enhanced by a limited but sound understanding of variables and commonly used study designs. [14]

• Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be familiar with study design and the variables used in studies to accurately evaluate new data and studies as they are published and apply the latest data to patient care and drive optimal outcomes.

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Types of Variables and Statistical Designs Table 1 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Types of Variables and Commonly Used Statistical Designs. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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• The future of Cochrane Neonatal. [Early Hum Dev. 2020] The future of Cochrane Neonatal. Soll RF, Ovelman C, McGuire W. Early Hum Dev. 2020 Nov; 150:105191. Epub 2020 Sep 12.
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• Review How to study improvement interventions: a brief overview of possible study types. [Postgrad Med J. 2015] Review How to study improvement interventions: a brief overview of possible study types. Portela MC, Pronovost PJ, Woodcock T, Carter P, Dixon-Woods M. Postgrad Med J. 2015 Jun; 91(1076):343-54.

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts.
• Categorical data represents groupings.

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variable can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables.

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is colour-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases, you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e., the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variable are listed below.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable.

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

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What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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2.2: Concepts, Constructs, and Variables

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• Anol Bhattacherjee
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We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

Conceptual and Theoretical Frameworks for Thesis Studies: What you must know

A theoretical framework is a conceptual model that provides a systematic and structured way of thinking about a research problem or question. It helps to identify key variables and the relationships between them and to guide the selection and interpretation of data. Theoretical frameworks draw on existing theories and research and can be used to develop new hypotheses or test existing ones. They provide a foundation for research design, data collection, and analysis and can help to ensure that research is relevant, rigorous, and coherent. Theoretical frameworks are common in many disciplines, including social sciences, natural sciences, and humanities, and are essential for building knowledge and advancing understanding in a field.

This article explains the importance of frameworks in a thesis study and the differences between conceptual frameworks and theoretical frameworks. It provides guidelines on how to write a thesis framework, definitions of variable types, and examples of framework types.

What is a research framework and why do I need one?

When planning your thesis study, you need to justify your research and explain its design to your readers. This is called the research framework.

When planning your thesis study, you need to justify your research and explain its design to your readers. This is called the research framework. Think of it as the foundation of a building. A good building needs a strong foundation. Similarly, your research needs to be supported by reviewing and explaining the existing knowledge in the field, describing how your research study will fit within or contribute to the existing literature (e.g., it could challenge or test an existing theory or address a knowledge gap), and informing the reader how your study design aligns with your thesis question or hypothesis.

Important components of the framework are a literature review of recent studies associated with your thesis topic as well as theories/models used in your field of research. The literature review acts as a filtering tool to select appropriate thesis questions and guide data collection, analysis, and interpretation of your findings. Think broadly! Apart from reviewing relevant published papers in your field of research, also explore theories that you have come across in your undergraduate courses, other published thesis studies, encyclopedias, and handbooks.

There are two types of research frameworks: theoretical and conceptual .

What is a conceptual framework?

A conceptual framework is a written or visual representation that explains the study variables and their relationships with each other. The starting point is a literature review of existing studies and theories about your topic.

Steps to develop a conceptual framework

• Clarify your study topic by identifying and defining key concepts in your thesis problem statement and thesis question. Essentially, your thesis should address a knowledge gap.
• Perform a literature review to provide a background to interpret and explain the study findings. Also, draw on empirical knowledge that you have gained from personal experience.
• Identify crucial variables from the literature review and your empirical knowledge, classify them as dependent or independent variables, and define them.
• Brainstorm all the possible factors that could affect each dependent variable.
• Propose relationships among the variables and determine any associations that exist between all variables.
• Use a flowchart or tree diagram to present your conceptual framework.

Types of variables

When developing a conceptual framework, you will need to identify the following:

• Independent variables
• Dependent variables
• Moderating variables
• Mediating variables
• Control variables

First, identify the independent (cause) and dependent (effect) variables in your study. Then, identify variables that influence this relationship, such as moderating variables, mediating variables, and control variables. A moderating variable changes the relationship between independent and dependent variables when its value increases or decreases. A mediating variable links independent and dependent variables to better explain the relationship between them. A control variable could potentially impact the cause-and-effect relationship but is kept constant throughout the study so that its effects on the findings/outcomes can be ruled out.

Example of a conceptual framework

You want to investigate the hours spent exercising (cause) on childhood obesity (effect).

Now, you need to consider moderating variables that affect the cause-and-effect relationship. In our example, the amount of junk food eaten would affect the level of obesity.

Next, you need to consider mediating variables. In our example, the maximum heart rate during exercise would affect the child’s weight.

Finally, you need to consider control variables. In this example, because we do not want to investigate the role of age in obesity, we can use this as a control variable. Thus, the study subjects would be children of a specific age (e.g., aged 6–10 years).

What is a theoretical framework?

A theoretical framework provides a general framework for data analysis. It defines the concepts used and explains existing theories and models in your field of research.

A theoretical framework provides a general framework for data analysis. It defines the concepts used and explains existing theories and models in your field of research. It also explains any assumptions that were used to inform your approach and your choice of specific rationales. Theoretical frameworks are often used in the fields of social sciences.

Purpose of a theoretical framework

• Test and challenge existing theories
• Establish orderly connections between observations and facts
• Predict and control situations
• Develop hypotheses

Steps to develop a theoretical framework

• Identify and define key concepts in your thesis problem statement and thesis question.
• Explain and evaluate existing theories by writing a literature review that describes the concepts, models, and theories that support your study.
• Choose the theory that best explains the relationships between the key variables in your study.
• Explain how your research study fills a knowledge gap or fits into existing studies (e.g., testing if an established theory applies to your thesis context).
• Discuss the relevance of any theoretical assumptions and limitations.

A thesis topic can be approached from a variety of angles, depending on the theories used.

• In psychology, a behavioral approach would use different methods and assumptions compared with a cognitive approach when treating anxiety.
• In literature, a book could be analyzed using different literary theories, such as Marxism or poststructuralism.

Structuring a theoretical framework

The structure of a theoretical framework is fluid, and there are no specific rules that need to be followed, as long as it is clearly and logically presented.

The theoretical framework is a natural extension of your literature review. The literature review should identify gaps in the field of your research, and reviewing existing theories will help to determine how these can be addressed. The structure of a theoretical framework is fluid, and there are no specific rules that need to be followed, as long as it is clearly and logically presented. The theoretical framework is sometimes integrated into the literature review chapter of a thesis, but it can also be included as a separate chapter, depending on the complexity of the theories.

Example of a theoretical framework

The sales staff at Company X are unmotivated and struggling to meet their monthly targets. Some members of the management team believe that this could be achieved by implementing a comprehensive product-training program, but others believe that introducing a sales commission structure will help.

Company X is not achieving their monthly sales targets

To increase monthly sales.

Research question:

How can Company X motivate their sales team to achieve its monthly sales targets?

Sub-questions:

• Why do the sales staff feel unmotivated?
• What is the relationship between motivation and monetary rewards?
• Do the sales staff feel that they have sufficient product knowledge?

Theoretical framework:

A literature search will need to be performed to understand the background of the many different theories of motivation in psychology. For example, Maslow’s Hierarchy of Needs (basic human needs—physiological, safety, love/belonging, esteem, and self-actualization—have to be fulfilled before one can live up to their true potential), Vroom’s Theory of Expectancy (people decide upon their actions based on the outcomes they expect), and Locke’s Goal-Setting Theory (goals are a key driver of one’s behavior). These theories would need to be investigated to determine which would be the best approach to increase the motivation of the sales staff in Company X so that the monthly sales targets are met.

A robust conceptual or theoretical framework is crucial when writing a thesis/dissertation. It defines your research gap, identifies your approach, and guides the interpretation of your results.

A thesis is the most important document you will write during your academic studies. For professional thesis editing and thesis proofreading services, check out Enago's Thesis Editing service s for more information.

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What type of framework is used in the Humanities and Social Sciences (HSS) domain? +

Theoretical frameworks are typically used in the HSS domain, while conceptual frameworks are used in the Sciences domain.

What is the difference between mediating versus moderating variables? +

The difference between mediators and moderators can be confusing. A moderating variable is unaffected by the independent variable and can increase or decrease the strength of the relationship between the independent and dependent variables. A mediating variable is affected by the independent variable and can explain the relationship between the independent and dependent variables. T he statistical correlation between the independent and dependent variables is higher when the mediating variable is excluded.

What software should I use to present my conceptual framework? +

The software program Creately provides some useful templates that can help you get started. Other recommended programs are SmartDraw , Inkscape , and diagrams.net .

Variables: Types and Characteristics

Variable is a quantity or a characteristic that has or more mutually exclusive values or properties of objects or people that can be classified, measured or labeled in different ways.

Types of Variables

• Discrete Variable – only a finite or potentially countable set of values.
• Continuous Variable – an infinite set of values between any two levels of the variables.  They are result of measurement.
• Independent Variable – a stimulus variable which is chosen by the researcher to determine its relationship to an observed phenomena.
• Dependent Variable – a response variable which is observed and measured to determine the effect of the independent variable.
• Moderate Variable – a secondary or special type of independent variable chosen by the researcher to ascertain if it alters or modifies.
• Control Variable – a variable controlled by the research in which the effects can be neutralized by removing the variable.
• Intervening Variable – a variable which interferes with the independent and dependent variables, but its effects can either strengthen or weaken the independent and dependent variables.

Characteristics of Variable

1.    Capable of assuming several values representing a certain category. 2.    Values that may arise from counting and or from measurement. 3.    Raw data or figures gathered by a research for statistical purposes. 4.    Predicted values of one variable on the basis of another 5.    Observable characteristic of a person or objects being studied.

Measuring dissertation variables and selecting instruments

https://www.amazon.com/author/dr.susan.carroll.books

When we collect information about people, objects and events, we must turn that information into numbers so that we can measure it. Measuring dissertation variables and selecting instruments are among the most challenging parts of the doctoral process. The following descriptive information is provided with the intention of helping you to do a good job with these tasks.

Data are derived from characteristics about individuals, objects or events. These characteristics are called variables . You attach numbers to your dissertation variables in an effort to measure them and apply statistics to them when you use your instruments.

Categorical variables have different categories and each category takes on a whole number or integer to represent it. The number that is assigned to the category does not have any meaning. A simple example of a categorical variable is Gender [male=0 and female=1]

Variables that are quantitative are classified as either discrete or continuous . They can take on numbers or integers that represent some degree of the variable. For example, the variable of household size for the families can be 1 (one person), 2 and up to double digits for a big family.

There are four scales of measurement used to assign numbers to your variables. 1. Nominal 2. Ordinal 3. Interval 4. Ratio Finally, you have to choose data collection instruments which will assign numbers to your variables. You will be asked about their validity and reliability . Return from measuring dissertation variables and selecting instruments to the dissertation statistics home page.

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Home » Qualitative Variable – Types and Examples

Qualitative Variable – Types and Examples

Table of Contents

Qualitative Variable

Definition:

Qualitative variable, also known as a categorical variable, is a type of variable in statistics that describes an attribute or characteristic of a data point, rather than a numerical value.

Qualitative variables are typically represented by labels or categories, such as “male” or “female,” and are often used in surveys and polls to gather information about a population’s characteristics.

Types Qualitative Variable

There are two main types of qualitative variables:

Nominal Variables

A nominal variable is a Qualitative Variable where the categories are not ordered in any particular way. For example, gender (male or female), race (Asian, Black, Hispanic, etc.), or religion (Christian, Muslim, Hindu, etc.). Nominal variables can be represented using numbers, but the numbers do not have any quantitative meaning. For example, a researcher might assign the number “1” to male and “2” to female, but these numbers do not represent a quantitative difference between the categories.

Ordinal Variables

An ordinal variable is a Qualitative Variable where the categories are ordered in some way. For example, educational level (high school, college, graduate school), income level (low, medium, high), or level of agreement (strongly agree, somewhat agree, neutral, somewhat disagree, strongly disagree). Ordinal variables can be represented using numbers, and the numbers have a quantitative meaning, but the distance between the categories is not necessarily equal. For example, the difference between “high school” and “college” may not be the same as the difference between “college” and “graduate school.”

Examples of Qualitative Variables

Here are some examples of qualitative variables:

• Gender : Male or female
• Marital status: Married, single, divorced, widowed
• Race : Asian, Black, Hispanic, White, etc.
• Religious affiliation: Christian, Muslim, Hindu, Buddhist, etc.
• Political affiliation : Democrat, Republican, Independent, etc.
• Educational level : High school, college, graduate school
• Type of employment : Full-time, part-time, self-employed, unemployed
• Type of housing: Apartment, house, condo, etc.
• Method of transportation : Car, bus, train, bike, etc.
• Language spoken: English, Spanish, French, etc.

Applications of Qualitative Variable

Qualitative variables are used in many applications in different fields, including:

• Market research : Qualitative variables are often used in market research to understand consumer behavior and preferences. For example, a company might use qualitative variables such as age, gender, and income to segment their target market and create customized marketing campaigns.
• Public opinion polling : Qualitative variables are used in public opinion polling to gather information about people’s attitudes, beliefs, and opinions. Pollsters may ask questions about political affiliation, religious affiliation, or social issues to understand public opinion on a particular topic.
• Social sciences research: Qualitative variables are commonly used in social sciences research to study human behavior, culture, and society. Researchers may use qualitative variables to categorize people based on their demographic information or cultural background, and to analyze patterns and trends in behavior or attitudes.
• Healthcare research: Qualitative variables are used in healthcare research to identify risk factors and to understand the impact of treatments on patients. Researchers may use qualitative variables such as age, gender, or medical history to identify populations at risk for certain diseases, and to evaluate the effectiveness of different treatment options.
• Education research: Qualitative variables are used in education research to study the effectiveness of different teaching methods and to identify factors that influence student learning. Researchers may use qualitative variables such as socio-economic status, educational level, or learning style to analyze patterns and trends in student performance.

When to use Qualitative Variable

Qualitative variables should be used in research when the variable being studied is categorical and does not involve numerical values. Here are some situations where qualitative variables are appropriate:

• When studying demographic characteristics: Qualitative variables are useful for studying demographic characteristics such as age, gender, ethnicity, and religion. These variables can be used to segment a population into groups and to compare differences between groups.
• When studying attitudes and beliefs : Qualitative variables can be used to study people’s attitudes and beliefs about various topics, such as politics, social issues, or religion. Researchers can use surveys or interviews to gather data on these variables.
• When studying cultural differences: Qualitative variables are often used in cross-cultural research to study differences between cultures. Researchers may use qualitative variables such as language spoken, nationality, or cultural background to identify groups for comparison.
• When studying consumer behavior : Qualitative variables can be used in market research to study consumer behavior and preferences. Researchers can use qualitative variables such as brand loyalty, product preference, or buying habits to understand consumer behavior.
• When studying patient outcomes: Qualitative variables can be used in healthcare research to study patient outcomes, such as quality of life, satisfaction with treatment, or adherence to medication. Researchers can use qualitative variables to identify factors that influence patient outcomes and to develop interventions to improve patient care.

Purpose of Qualitative Variable

The purpose of a qualitative variable is to categorize data into distinct groups based on non-numerical characteristics or attributes. The use of qualitative variables allows researchers to describe and analyze non-quantifiable phenomena, such as attitudes, beliefs, behaviors, and demographic characteristics, and to identify patterns and trends in the data. The main purposes of qualitative variables are:

• To describe and categorize : Qualitative variables are used to describe and categorize data into meaningful groups based on characteristics or attributes that are not numerical.
• To compare and contrast: Qualitative variables allow researchers to compare and contrast different groups or categories of data, such as different demographic groups or cultural backgrounds.
• To identify patterns and trends: Qualitative variables allow researchers to identify patterns and trends in data that may not be apparent with numerical data. For example, a researcher may use qualitative variables to identify cultural differences in attitudes toward healthcare.
• To develop hypotheses: Qualitative variables can be used to develop hypotheses or research questions for further study. For example, a researcher may use qualitative variables to identify risk factors for a particular disease, which can then be further studied using quantitative methods.
• To inform decision-making: Qualitative variables can provide important information to inform decision-making in fields such as healthcare, education, and business. For example, healthcare providers may use qualitative variables to identify patient preferences and needs, which can inform treatment decisions.

Characteristics of Qualitative Variable

Here are some of the characteristics of qualitative variables:

• Categorical : Qualitative variables are categorical in nature, meaning that they describe characteristics or attributes that are not numerical. They can be nominal, ordinal or binary.
• Non-numeric : Qualitative variables do not involve numerical values, but rather descriptive or categorical data such as colors, shapes, types, or names.
• Limited number of categories: Qualitative variables are often limited to a small number of categories, such as male/female, married/single/divorced, or white/black/Asian.
• Mutually exclusive categories : Categories in a qualitative variable must be mutually exclusive, meaning that each observation can only belong to one category.
• No numerical order : Unlike quantitative variables, qualitative variables do not have a numerical order or ranking. Categories are assigned based on non-numerical criteria.
• Can be used for comparison : Qualitative variables are often used for comparison purposes, such as comparing the frequency of certain behaviors or attitudes across different demographic groups.
• Can be used for classification: Qualitative variables can be used to classify data into distinct groups based on common characteristics or attributes. For example, people can be classified into different racial or ethnic groups based on their ancestry.
• Can be used for hypothesis testing : Qualitative variables can be used to test hypotheses about differences between groups or categories of data. For example, a researcher may hypothesize that men and women have different attitudes toward a particular social issue, and use a qualitative variable to test this hypothesis.

Advantages of Qualitative Variable

There are several advantages of using qualitative variables.

• Rich data: Qualitative variables can provide rich data about complex phenomena such as attitudes, behaviors, and cultural differences. This data can be useful for gaining a deep understanding of a particular issue or topic.
• Flexibility : Qualitative variables are flexible and can be used in a variety of research methods, such as interviews, focus groups, and observations. This allows researchers to choose the method that best suits their research question and participants.
• Participant perspective : Qualitative variables allow researchers to capture the participant’s perspective and experience. By using open-ended questions or prompts, researchers can gain insight into how participants perceive and interpret a particular issue.
• Depth of understanding: Qualitative variables allow for a depth of understanding that may not be possible with quantitative variables alone. Qualitative data can provide details and context that quantitative data may miss.
• Contextualization : Qualitative variables can provide contextualization, allowing researchers to understand the cultural, social, and historical factors that shape attitudes and behaviors.
• Theory development: Qualitative variables can be useful for developing new theories or refining existing ones. By gathering rich data and analyzing it using qualitative methods, researchers can identify patterns and relationships that can inform the development of new theories.
• Researcher reflexivity : Qualitative variables require the researcher to be reflexive and acknowledge their own biases and assumptions. This can help to ensure that the research is ethical and inclusive, and that the data collected is valid and reliable.

Limitations of Qualitative Variable

Some Limitations of Qualitative Variable are as follows:

• Subjectivity : Qualitative data is often collected through open-ended questions or prompts, which can lead to subjective responses that are difficult to quantify or compare. This can make it challenging to establish inter-rater reliability and can limit the generalizability of the findings.
• Limited sample size : Qualitative research often involves small sample sizes, which can limit the generalizability of the findings. While qualitative research is typically focused on gaining a deep understanding of a particular issue, the findings may not be representative of the broader population.
• Time-consuming: Qualitative research can be time-consuming, particularly when collecting and analyzing data. Researchers must spend significant amounts of time in the field, conducting interviews or focus groups, and then transcribing and analyzing the data.
• Limited control: Qualitative research often involves limited control over the research environment and the participants. This can make it challenging to ensure that the data collected is valid and reliable.
• Limited generalizability: Qualitative research is typically focused on gaining a deep understanding of a particular issue, rather than testing hypotheses or making generalizations about the broader population. As a result, the findings may be less generalizable than those obtained through quantitative research methods.
• Ethical concerns: Qualitative research often involves collecting sensitive or personal information from participants. Researchers must take care to ensure that participants are fully informed about the research, that their privacy is protected, and that they are not harmed in any way by their participation.
• Bias : Qualitative research can be subject to bias, particularly if the researcher has a vested interest in the outcome of the research. Researchers must take care to acknowledge their own biases and assumptions, and to use multiple sources of data to ensure the validity and reliability of the findings.

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Muhammad Hassan

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Essay about Kinds of Variables and Their Uses

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An independent variable is one that is manipulated and controlled by the researcher; on the other hand the dependent variable is the one that is observed and measured so as to understand the effects of manipulating its independent counterpart.

However, these two variables are not the only ones present in an experiment. Extraneous variables may also be present and play a great role in influencing the research.

If a researcher wanted to study the effects that inadequate instructions have on level of anger or frustration, variable identification is requisite. The ability of the researcher to define the variables in an operational approach is very important because it makes the study’s outcomes more valid and reliable for use at present times and in future.

In this case, the independent variable is represented by the inadequate instructions while the level of frustration or anger is a variable that solely depends on the inadequate instructions given.

Having identified the variables, the next step is to make them operational. At this point, the researcher should select a concept to ensure that anger is a result of the instructions given. However, the concept chosen should be within the limits of time, feasibility and financial ability. For instance, a researcher in this case may come up with a rewarding competition where the competitors are given limiting instructions.

The competition may be presented inform of an aptitude exam. It should be clearly stipulated that a competitor can only win by completing a given task within a specified time frame. When a short but logical period of time is set, it may result into frustration of the subjects being studied.

In order to measure anger, various methods could be used. For example, an oral interview at the end of the competition may give the researcher an opportunity to evaluate anger or frustration level especially for those who did not win. Another method that could be used is the filling of a questionnaire at the end of the study. A set of questions could be asked and based on the response; the researcher may be able to measure the level of frustration or anger.

In many experiments, extraneous and confounding variables will always be there. An extraneous variable can be described as unwanted variable that influences the experiment other than the variables being studied.

These variables are objectionable because in most cases they produce erroneous outcomes. They are divided into two, the first one being participant variables that are linked to the characteristics of an individual participant and which may have an impact on the response.

These variables may include factors like intelligence, mood and background differences. In the example of the aptitude test case given here, the extraneous variable might be pre-knowledge that competitors have on the questions being asked. If the competitors had seen the questions, then they might have an easy time and as result may not show any signs of frustration.

The other category of extraneous variables is the situational ones, which are associated with environmental factors that may influence the response of the participant. For example, if the test is being given in a room that is very hot, the high temperature may frustrate some participants while others may remain comfortable despite the impediment.

In conclusion, extraneous variables should be avoided as much as possible by carefully choosing a concept that will ensure they are minimized. For instance, the researcher should ensure that the test is being given in a room where all the participants feel comfortable.

The room should neither be very cold nor very hot. The participants should be carefully selected to ensure fairness; for example, they should be of the same age and of the same social background. Failure to alleviate these variables may produce incorrect results, and therefore compromise the validity and reliability of the research.

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How to identify relevant variables from a literature review?

Variables are simply factors that represent properties, qualities, or characteristics of a person, thing, or concept. Any research paper like a thesis or dissertation requires relevant variables to be defined correctly. They can be as simple as demographic characteristics such as age, gender, or income (Marudhar, 2018). Other times they can be more complex like the psychological traits of an individual. Researchers do a thorough literature review to identify these complex variables.

Different types of variables in a research

Variables of any study are of four main types.

A dependent variable is a variable that is dependent on the value of another variable. So if anything in the first variable changes then the dependent variable changes too.

‘Weight’ is often dependent on the ‘age’ of an individual. Here, ‘weight’ is the dependent variable. Marudhar, 2018; USCLibraries, 2017

An independent variable is a variable that is unaffected by other variables of the study. In studies, these variables could be regarded as the factors affecting the key research term.

‘Age’ is not related to ‘gender’, i.e. if a person’s gender changes then his age does not change. Therefore they both are independent variables.

A demographic variable is a variable that represents the basic demographic characteristics of the nature of respondents included in the study. They are neither independent nor dependent. Common demographic variables are age, gender, income, location, profession and marital status.

An extraneous variable is also known as an intervening variable and they make a relationship stronger or weaker.

While assessing how age affects a person’s tolerance to alcohol, weight is an extraneous variable that may or may not actually affect the tolerance.

Categorising relevant variables as dependent and independent variables

Literature review for major studies serves as a major methodological tool to answer research questions. It aims to delve deep into the background of the research topic. For this, researchers study review relevant research papers published in reputable publications.

Xiang et al. (2014) examined the factors influencing project governance in china and determined that aspects like system management, stakeholder management, risk management, integrity management, cost management, and audit management affect the project performance (strategic and operational). Agemb & Oyugi (2017) further explored the critical success factors for project performance and stated that factors like project manager competency, project team competency, rules and procedure compliance, and subcontractor services influence the project performance. Lastly, Ramesh et al. (2018) in their examination of project success derived that human resource management, task management, schedule management, and feasibility study contribute to influencing project performance.

With the empirical review of the literature in the above example, it can be derived that project performance is the dependent variable represented by:

• project success,
• strategic performance,
• and operational performance.

While aspects like:

• system management,
• stakeholder management,
• risk management,
• integrity management,
• cost management,
• audit management,
• project manager competency,
• project team competency,
• rules and procedure compliance,
• subcontractor services,
• human resource management,
• task management,
• schedule management and,
• feasibility study

are possible independent variables.

To find out an answer to the research question of how many Indonesian consumers liked to consume nicotine and why:

• First, do a background check of how many Indonesians are actually consuming nicotine.
• Procure 20 good studies from reputed publications in the last 10 years to make a literature review and understand all the possible reasons people consume nicotine.
• Identify the independent variables such as ‘stress’, ‘easy availability, or ‘peer pressure which cause everyone to consume nicotine.
• Lastly, find out the hidden (extraneous) factors which indirectly affect Indonesians to consume nicotine, such as ‘alcohol is not easily available.

In this case, the dependent variable is the consumption of nicotine and the independent variables are stress, easy availability and peer pressure.

Presenting the identified relevant variables in the conceptual framework

In the literature review, researchers commonly use a diagram known as a conceptual framework to map out the linkage between the variables. A conceptual framework is a visual or written representation of the possible or expected relationships between variables (Swaen, 2021). A conceptual framework helps to identify the correct way to test the relationship between two variables. Thus, the presentation of variables in the conceptual framework should be to state the mix of variables and their linkage based on existing studies (Regoniel, 2015).

From the above example of the empirical review of literature, the conceptual framework can be presented as:

Independent variables

• System management
• Stakeholder management
• Risk management
• Cost management
• Audit management
• Subcontractor services
• Human resource management
• Task management
• Schedule management

Dependent variable

Project performance

Dependent variable components

• Project success
• Strategic performance
• Operational performance

While identifying relevant variables in a literature review, it is important that:

• The variables should be consistent with the goal.
• They should be measurable and replicable. Avoid making ‘feeling’ a variable.
• The stated variables should have been widely used in recent years or at least defined in recent terms.
• The variable should be within the stated study design.
• Variables identified should be valid, reliable, prevalent, and common in the community.
• Should be mentioned or determined based on literature review.
• The variables should not be out of scope, very rare, or time-consuming.
• Agemb, W., & Oyugi, B. (2017). Critical Success Factors of Project Management : Empirical Evidence From Projects Supported By Constitiency development fund in Nyakach sub-country, Kisumu country. International Journal of Marketing and Technology , 7 (8), 20–28.
• Carver, J., & Basili, V. (2003). Identifying implicit process variables to support future empirical work. Journal of the Brazilian Computer Society , 9 (2), 77–87. https://doi.org/10.1590/s0104-65002003000300006
• Joon Sung, W. (2018). The Empirical Study on Digital Literacy from the Viewpoint of Digital Accessibility. International Journal of Engineering & Technology , 7 (3.13), 137. https://doi.org/10.14419/ijet.v7i3.13.16340
• Kroelinger, M. (2002). Strategy for Literature Review Process .
• Marudhar. (2018). Identifying Variables. International Journal of Science and Research (IJSR) , 8 (3), 865–868. https://doi.org/10.32388/od6d1w
• Ramesh, E., Babu, D. R., & Rao, P. R. (2018). The impact of project management in achieving project success- Empirical study. International Journal of Mechanical Engineering and Technology , 9 (13), 237–247.
• Regoniel, P. (2015). Conceptual Framework: A Step by Step Guide on How to Make One. SimplyEducate . simplyeducate.me/2015/01/05/conceptual-framework-guide/
• Swaen, B. (2021). Constructing a conceptual framework. Scribbr . https://www.scribbr.com/methodology/conceptual-framework/
• USCLibraries. (2017). Research Guides . Usc.Edu. https://libguides.usc.edu/writingguide/background
• Xiang, W., Li, Y., & Shou, Y. (2014). An empirical study of critical success factors of project governance in China. IEEE International Conference on Industrial Engineering and Engineering Management , 71072119 , 405–409. https://doi.org/10.1109/IEEM.2013.6962443
• ZamenPub. (2019). The Use and Importance of Colorants . https://www.koelcolours.com/blog/colorants/the-use-and-importance-of-colorants/
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On the use of $\alpha$-stable random variables in Bayesian bridge regression, neural networks and kernel processes.pdf

The first chapter considers the l_α regularized linear regression, also termed Bridge regression. For α ∈ (0, 1), Bridge regression enjoys several statistical properties of interest such

as sparsity and near-unbiasedness of the estimates (Fan & Li, 2001). However, the main difficulty lies in the non-convex nature of the penalty for these values of α, which makes an

optimization procedure challenging and usually it is only possible to find a local optimum. To address this issue, Polson et al. (2013) took a sampling based fully Bayesian approach to this problem, using the correspondence between the Bridge penalty and a power exponential prior on the regression coefficients. However, their sampling procedure relies on Markov chain Monte Carlo (MCMC) techniques, which are inherently sequential and not scalable to large problem dimensions. Cross validation approaches are similarly computation-intensive. To this end, our contribution is a novel non-iterative method to fit a Bridge regression model. The main contribution lies in an explicit formula for Stein’s unbiased risk estimate for the out of sample prediction risk of Bridge regression, which can then be optimized to select the desired tuning parameters, allowing us to completely bypass MCMC as well as computation-intensive cross validation approaches. Our procedure yields results in a fraction of computational times compared to iterative schemes, without any appreciable loss in statistical performance.

Next, we build upon the classical and influential works of Neal (1996), who proved that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process, when the network weights have bounded prior variance. Neal’s result has been extended to networks with multiple hidden layers and to convolutional neural networks, also with Gaussian process scaling limits. The tractable properties of Gaussian processes then allow straightforward posterior inference and uncertainty quantification, considerably simplifying the study of the limit process compared to a network of finite width. Neural network weights with unbounded variance, however, pose unique challenges. In this case, the classical central limit theorem breaks down and it is well known that the scaling limit is an α-stable process under suitable conditions. However, current literature is primarily limited to forward simulations under these processes and the problem of posterior inference under such a scaling limit remains largely unaddressed, unlike in the Gaussian process case. To this end, our contribution is an interpretable and computationally efficient procedure for posterior inference, using a conditionally Gaussian representation, that then allows full use of the Gaussian process machinery for tractable posterior inference and uncertainty quantification in the non-Gaussian regime.

Finally, we extend on the previous chapter, by considering a natural extension to deep neural networks through kernel processes. Kernel processes (Aitchison et al., 2021) generalize to deeper networks the notion proved by Neal (1996) by describing the non-linear transformation in each layer as a covariance matrix (kernel) of a Gaussian process. In this way, each succesive layer transforms the covariance matrix in the previous layer by a covariance function. However, the covariance obtained by this process loses any possibility of representation learning since the covariance matrix is deterministic. To address this, Aitchison et al. (2021) proposed deep kernel processes using Wishart and inverse Wishart matrices for each layer in deep neural networks. Nevertheless, the approach they propose requires using a process that does not emerge from the limit of a classic neural network structure. We introduce α-stable kernel processes (α-KP) for learning posterior stochastic covariances in each layer. Our results show that our method is much better than the approach proposed by Aitchison et al. (2021) in both simulated data and the benchmark Boston dataset.

Degree Type

• Doctor of Philosophy

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Computational statistics
• Spatial statistics
• Statistical theory

A probabilistic distribution theory of bureaucratic behavior: Policy preferences as random variables

The relationship between Congress and the executive branch agencies is complex and uncertain. Congressional committees authorize and appropriate an immense array of federal programs, each having unique political and fiscal attributes. However, formal models of legislative-bureaucratic interaction tend to simplify the relationship to such an extent that the generic agencies and the generic legislatures interact in a manner that deviates substantially from empirical observation. I examine the current development of formal models in political science and public administration finding that there is a need for the inclusion of an uncertain and probabilistic element that characterizes the lack of precision human beings have about each others preferences and attitudes. Since greater interaction provides higher levels of familiarity, each single interaction can be treated as a sample point from some defined distribution of preference on a specific policy. This construct allows the use of standard statistical tools within the model of legislative-bureaucratic behavior. I find that agencies generally have very little direct ability to manipulate knowledge of congressional preferences to achieve desired program budget and policy levels. The effect of limited information is to severely restrict agency control of the appropriations process. This contradicts the agency dominance literature derived from formal models of legislative/bureaucratic behavior. The scope and range of federal programs in the United States assures us that each person is in some way affected by the administration of the relationships determining policy goals and budget levels. By modeling the fundamental characteristics of a complex set of interrelated behaviors and structures, it is possible to enhance our principle knowledge of how the legislative and bureaucratic branches of government interact under these particular circumstances.

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