what is criterion variable in research

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What is a Criterion Variable? (Explanation + Examples)

A  criterion variable  is simply another name for a  dependent variable  or a  response variable . This is the variable that is being predicted in a statistical analysis.

Just as explanatory variables have different names like  predictor variables  or  independent variables , a response variable also has interchangeable names like  dependent variable  or  criterion variable .

What are Some Examples of Criterion Variables?

The following scenarios illustrate examples of criterion variables in several different settings.

Example 1: Simple Linear Regression

Simple linear regression   is a statistical method we use to understand the relationship between two variables, x and y. One variable, x, is known as the predictor variable. The other variable, y, is known as the  criterion variable , or  response variable .

In simple linear regression, we find a “line of best fit” that describes the relationship between the predictor variable and the criterion variable.

For example, we may fit a simple linear regression model to a dataset using  hours studied  as the predictor variable and  test score  as the criterion variable. In this case, we would use simple linear regression to attempt to predict the value of our criterion variable  test score .

Or, as another example, we may fit a simple linear regression model to a dataset using  weight  to predict the value for  height  for a group of people. In this case, our criterion variable is  height  since that’s the value we’re interested in predicting.

If we plotted the values for height and weight on a scatterplot , the criterion variable  height  would be on the y-axis:

In general, the criterion variablewill be along the y-axis when we create a scatterplot and the predictor variable will be along the x-axis.

Example 2: Multiple Linear Regression

Multiple linear regression  is similar to simple linear regression, except we use several predictor variables to predict the value of one criterion variable. 

For example, we may use the predictor variables  hours studied  and hours of sleep the night before the test to predict the value of the criterion variable  test score . In this case, our criterion variable is the variable being predicted in this analysis.

Example 3: ANOVA

An  ANOVA  (analysis of variance) is a statistical technique we use to find out if there is a statistically significant difference between the means of three or more independent groups.

For example, we may want to determine if three different exercise programs impact weight loss differently. The predictor variable we’re studying is  exercise program  and it has three levels .

The criterion variable is  weight loss,  measured in pounds. We can conduct a one-way ANOVA to determine if there is a statistically significant difference between the resulting weight loss from the three programs.

In this case, we’re interested in understanding whether the value of the criterion variable  weight loss  differs among the three exercise programs. 

If we instead analyzed  exercise program  and   average hours slept per night, we would conduct a two-way ANOVA since we are interested in seeing how two factors impact weight loss.

Once again, though, our criterion variable is still weight loss  because we are interested in how the value of this variable differs for different levels of  exercise and  sleep .

Additional Reading: A Simple Explanation of Criterion Validity

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HI , I have problem to define the independent and the dependent variable, I can read and understand pretty well the concept but at the moment of applying them, my brain blanks, specially when I use the SPSS computational app, it does not give me results because I believe didn’t placed the variables in the right order, can you give me tip to really understand the variables so I can utilized the right way, between that and the ordinal, nominal and scale I am getting nuts, some tips please

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What is a Criterion Variable? (Explanation + Examples)

A  criterion variable  is simply another name for a  dependent variable  or a  response variable . This is the variable that is being predicted in a statistical analysis.

Just as explanatory variables have different names like  predictor variables  or  independent variables , a response variable also has interchangeable names like  dependent variable  or  criterion variable .

What are Some Examples of Criterion Variables?

The following scenarios illustrate examples of criterion variables in several different settings.

Example 1: Simple Linear Regression

Simple linear regression   is a statistical method we use to understand the relationship between two variables, x and y. One variable, x, is known as the predictor variable. The other variable, y, is known as the  criterion variable , or  response variable .

In simple linear regression, we find a “line of best fit” that describes the relationship between the predictor variable and the criterion variable.

For example, we may fit a simple linear regression model to a dataset using  hours studied  as the predictor variable and  test score  as the criterion variable. In this case, we would use simple linear regression to attempt to predict the value of our criterion variable  test score .

Or, as another example, we may fit a simple linear regression model to a dataset using  weight  to predict the value for  height  for a group of people. In this case, our criterion variable is  height  since that’s the value we’re interested in predicting.

If we plotted the values for height and weight on a scatterplot , the criterion variable  height  would be on the y-axis:

In general, the criterion variablewill be along the y-axis when we create a scatterplot and the predictor variable will be along the x-axis.

Example 2: Multiple Linear Regression

Multiple linear regression  is similar to simple linear regression, except we use several predictor variables to predict the value of one criterion variable. 

For example, we may use the predictor variables  hours studied  and hours of sleep the night before the test to predict the value of the criterion variable  test score . In this case, our criterion variable is the variable being predicted in this analysis.

Example 3: ANOVA

An  ANOVA  (analysis of variance) is a statistical technique we use to find out if there is a statistically significant difference between the means of three or more independent groups.

For example, we may want to determine if three different exercise programs impact weight loss differently. The predictor variable we’re studying is  exercise program  and it has three levels .

The criterion variable is  weight loss,  measured in pounds. We can conduct a one-way ANOVA to determine if there is a statistically significant difference between the resulting weight loss from the three programs.

In this case, we’re interested in understanding whether the value of the criterion variable  weight loss  differs among the three exercise programs. 

If we instead analyzed  exercise program  and   average hours slept per night, we would conduct a two-way ANOVA since we are interested in seeing how two factors impact weight loss.

Once again, though, our criterion variable is still weight loss  because we are interested in how the value of this variable differs for different levels of  exercise and  sleep .

Additional Reading: A Simple Explanation of Criterion Validity

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• The 4 Types of Validity in Research | Definitions & Examples

The 4 Types of Validity in Research | Definitions & Examples

Published on September 6, 2019 by Fiona Middleton . Revised on June 22, 2023.

Validity tells you how accurately a method measures something. If a method measures what it claims to measure, and the results closely correspond to real-world values, then it can be considered valid. There are four main types of validity:

• Construct validity : Does the test measure the concept that it’s intended to measure?
• Content validity : Is the test fully representative of what it aims to measure?
• Face validity : Does the content of the test appear to be suitable to its aims?
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

In quantitative research , you have to consider the reliability and validity of your methods and measurements.

Note that this article deals with types of test validity, which determine the accuracy of the actual components of a measure. If you are doing experimental research, you also need to consider internal and external validity , which deal with the experimental design and the generalizability of results.

Table of contents

Construct validity, content validity, face validity, criterion validity, other interesting articles, frequently asked questions about types of validity.

Construct validity evaluates whether a measurement tool really represents the thing we are interested in measuring. It’s central to establishing the overall validity of a method.

What is a construct?

A construct refers to a concept or characteristic that can’t be directly observed, but can be measured by observing other indicators that are associated with it.

Constructs can be characteristics of individuals, such as intelligence, obesity, job satisfaction, or depression; they can also be broader concepts applied to organizations or social groups, such as gender equality, corporate social responsibility, or freedom of speech.

There is no objective, observable entity called “depression” that we can measure directly. But based on existing psychological research and theory, we can measure depression based on a collection of symptoms and indicators, such as low self-confidence and low energy levels.

What is construct validity?

Construct validity is about ensuring that the method of measurement matches the construct you want to measure. If you develop a questionnaire to diagnose depression, you need to know: does the questionnaire really measure the construct of depression? Or is it actually measuring the respondent’s mood, self-esteem, or some other construct?

To achieve construct validity, you have to ensure that your indicators and measurements are carefully developed based on relevant existing knowledge. The questionnaire must include only relevant questions that measure known indicators of depression.

The other types of validity described below can all be considered as forms of evidence for construct validity.

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Content validity assesses whether a test is representative of all aspects of the construct.

To produce valid results, the content of a test, survey or measurement method must cover all relevant parts of the subject it aims to measure. If some aspects are missing from the measurement (or if irrelevant aspects are included), the validity is threatened and the research is likely suffering from omitted variable bias .

A mathematics teacher develops an end-of-semester algebra test for her class. The test should cover every form of algebra that was taught in the class. If some types of algebra are left out, then the results may not be an accurate indication of students’ understanding of the subject. Similarly, if she includes questions that are not related to algebra, the results are no longer a valid measure of algebra knowledge.

Face validity considers how suitable the content of a test seems to be on the surface. It’s similar to content validity, but face validity is a more informal and subjective assessment.

You create a survey to measure the regularity of people’s dietary habits. You review the survey items, which ask questions about every meal of the day and snacks eaten in between for every day of the week. On its surface, the survey seems like a good representation of what you want to test, so you consider it to have high face validity.

As face validity is a subjective measure, it’s often considered the weakest form of validity. However, it can be useful in the initial stages of developing a method.

Criterion validity evaluates how well a test can predict a concrete outcome, or how well the results of your test approximate the results of another test.

What is a criterion variable?

A criterion variable is an established and effective measurement that is widely considered valid, sometimes referred to as a “gold standard” measurement. Criterion variables can be very difficult to find.

What is criterion validity?

To evaluate criterion validity, you calculate the correlation between the results of your measurement and the results of the criterion measurement. If there is a high correlation, this gives a good indication that your test is measuring what it intends to measure.

A university professor creates a new test to measure applicants’ English writing ability. To assess how well the test really does measure students’ writing ability, she finds an existing test that is considered a valid measurement of English writing ability, and compares the results when the same group of students take both tests. If the outcomes are very similar, the new test has high criterion validity.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time .
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalizability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritize internal validity over external validity , including ecological validity .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

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Criterion Variable in Psychology: Definition and Usage

Have you ever wondered what a criterion variable is and how it is used in psychology research? In this article, we will explore the definition and importance of criterion variables, the difference between criterion and predictor variables, and how they are used in statistical analysis. We will also discuss how criterion variables are measured, the types of criterion variables, and provide examples of their application in psychology studies. We will address the limitations of using criterion variables in psychological research. Join us as we delve into the fascinating world of criterion variables in psychology!

• Criterion variables are used in psychology to measure an outcome or dependent variable in research.
• They are distinguished from predictor variables, which are used to predict the criterion variable.
• Examples of criterion variables in psychology studies include academic performance, job satisfaction, and mental health status.
• 1.1 What is the Difference Between Criterion and Predictor Variables?
• 2.1 What is the Importance of Criterion Variables in Research?
• 2.2 How is a Criterion Variable Measured?
• 2.3 What are the Types of Criterion Variables?
• 3.1 How is a Criterion Variable Used in Regression Analysis?
• 3.2 What is the Significance of Criterion Variables in Correlation Analysis?
• 4.1 Academic Performance
• 4.2 Job Satisfaction
• 4.3 Mental Health Status
• 4.4 Personality Traits
• 5 What are the Limitations of Using Criterion Variables in Psychological Research?
• 6.1 What is a criterion variable in psychology?
• 6.2 How is a criterion variable used in psychological research?
• 6.3 What are some examples of criterion variables in psychology?
• 6.4 Can a criterion variable change in a study?
• 6.5 How do researchers select a criterion variable?
• 6.6 Why is the use of a criterion variable important in psychology?

What is a Criterion Variable?

A criterion variable is a key concept in research that serves as the dependent variable in a study, meaning it is the outcome or response variable that researchers are interested in predicting or understanding.

It is the variable that is impacted or influenced by changes in the predictor or independent variables being studied. The role of the criterion variable is to establish the relationship between the independent and dependent variables. This relationship is often explored using statistical methods such as regression analysis , where the criterion variable is the focus of the analysis to understand how it is affected by the predictor variables. Researchers carefully select and measure the criterion variable to ensure accurate assessment of the research question being investigated.

What is the Difference Between Criterion and Predictor Variables?

Understanding the distinction between criterion and predictor variables is crucial in research, where the criterion variable is the outcome of interest, while predictor variables are used to forecast or explain variations in the criterion variable.

In research, predictor variables play a vital role in determining the relationship between various factors and the criterion variable. These predictor variables are typically the factors that researchers believe can explain or influence changes in the dependent variable, also known as the criterion variable.

Through the use of statistical techniques like multiple regression , researchers can analyze how these predictor variables collectively impact the criterion variable. This process helps in understanding the extent to which the predictor variables collectively can account for the variations in the criterion variable, known as the explained variable .

How is a Criterion Variable Used in Psychology?

In psychology, criterion variables are employed to measure and analyze various aspects of human behavior and mental processes, providing insights into areas such as public support for policies, mental health outcomes, and therapeutic effectiveness.

Criterion variables play a crucial role in numerous domains beyond psychology, extending their application to fields such as political science, public health, and therapy.

For instance, in political science, these variables are utilized to assess public opinion on climate change legislation and its impact on voting behavior.

In public health, criterion variables help evaluate the effectiveness of interventions aimed at reducing recidivism rates among individuals with mental health disorders.

In therapy, these variables aid in measuring the progress of individuals undergoing treatment for various mental health conditions.

What is the Importance of Criterion Variables in Research?

Criterion variables play a vital role in research by enabling researchers to assess the impact of interventions, policy changes, and specific factors on real-world outcomes such as climate change mitigation strategies and recidivism rates in criminal justice systems.

These variables act as the yardstick against which the effectiveness of various initiatives and social measures can be measured. For instance, when examining the influence of education level on employment rates, researchers use criterion variables to determine the correlation between the two. It is through the careful selection and manipulation of these variables that researchers can infer causation or predict outcomes with greater accuracy.

• Criterion variables help quantify the success of support systems in different contexts, providing valuable insights for policymakers and practitioners.

How is a Criterion Variable Measured?

Criterion variables are measured using various methods in research, including statistical modeling techniques like multiple regression and canonical correlation analysis, which help in understanding the relationships between predictor variables and the criterion variable.

Multiple regression involves analyzing how multiple predictor variables are related to a single criterion variable by determining the strength and significance of these relationships. This statistical method allows researchers to assess the impact of each predictor variable on the criterion variable, such as how college GPA and SAT scores predict academic success.

On the other hand, canonical correlation analysis examines the relationships between sets of variables, identifying the underlying dimensions that explain the associations among them. By uncovering these latent factors, researchers can gain a deeper understanding of the complex interplay between different variables in predicting outcomes.

What are the Types of Criterion Variables?

Criterion variables can encompass diverse types, ranging from continuous variables in statistical modeling to categorical variables like SAT scores and college GPAs, each providing unique insights into the relationships between predictor and outcome variables.

Continuous variables, such as age or income, allow researchers to analyze the data with precision, capturing the nuances of the relationships being studied by measuring on a scale.

On the other hand, categorical variables like gender or education level segment the data into distinct groups, making it easier to compare and contrast different subgroups within the study.

By understanding the differences between these types of criterion variables, researchers can identify patterns, establish strong correlations , and effectively manipulate variables to test hypotheses and infer conclusions with statistical significance.

What is the Role of Criterion Variables in Statistical Analysis?

Criterion variables serve a crucial role in statistical analysis by helping researchers test hypotheses, understand the effects of specific policies or interventions, and evaluate the outcomes of policy changes on dependent variables of interest.

These variables, often denoted as criterion measures , play a significant part in theory testing and validation. They provide a basis for comparison and enable researchers to assess the degree of change or impact brought about by different factors.

By incorporating multiple criterion variables, analysts can gain a more comprehensive understanding of the relationships between various independent and dependent variables. In hypothesis testing, criterion variables serve as the pivotal points for drawing conclusions and making informed decisions based on statistical evidence.

How is a Criterion Variable Used in Regression Analysis?

In regression analysis, the criterion variable is typically designated as the dependent variable, outcome variable, or response variable, representing the key factor researchers seek to predict or explain through the analysis of predictor variables.

Identifying the criterion variable is crucial in building regression models as it serves as the foundation for the analysis. By understanding the influence of predictor variables on the criterion variable, researchers can uncover patterns, trends, and relationships within the data. These criterion variables are carefully selected based on their hypothesized impact on the system being studied, guiding the construction of the regression model.

What is the Significance of Criterion Variables in Correlation Analysis?

Criterion variables are essential in correlation analysis, where they help researchers explore the relationships between variables in economic contexts, non-experimental research designs, and other domains where direct manipulation is not feasible.

By serving as the variable that is predicted or affected in a study, criterion variables play a crucial role in understanding the interplay among different factors. In economic studies, these variables provide insights into key relationships, such as how changes in one variable affect another without the need for experimental control. By distinguishing between endogenous and explained variables , researchers can better grasp the underlying mechanisms and factors influencing a particular phenomenon, leading to more accurate interpretations of the data obtained.

What are Some Examples of Criterion Variables in Psychology Studies?

Psychology studies feature diverse examples of criterion variables, ranging from public health outcomes and therapy effectiveness to mental health assessments, education levels, and recidivism rates in criminal justice research.

For instance, in public health, criterion variables could include measures like the prevalence of diseases, mortality rates, or access to healthcare services. These variables are crucial for determining the impact of psychological interventions on physical well-being and community health.

In therapy effectiveness research, outcome measures such as symptom reduction, quality of life improvements, and client satisfaction levels serve as key criterion variables.

Similarly, in mental health assessments, factors like depression severity, anxiety levels, and cognitive functioning are essential criteria for evaluating treatment efficacy and patient progress.

Education levels are another vital area where criterion variables play a significant role, including variables like academic performance, graduation rates, and career outcomes.

In criminal justice research, recidivism rates are often used as criterion variables to assess the effectiveness of rehabilitation programs and intervention strategies in reducing repeat offenses.

Understanding and supporting the relevance of these criterion variables in diverse psychological studies are fundamental to advancing research in areas such as public health promotion, therapy efficacy, mental health interventions, educational policies, and criminal justice reform.

Academic Performance

Academic performance is a common criterion variable in psychology studies, influenced by various factors such as study habits, learning environment, and economic background, with researchers analyzing data to understand these influences.

One key factor that significantly influences academic performance is the level of parental involvement in a student’s education. Research has shown that students whose parents are actively engaged in their academic life tend to perform better in school. This could include activities like helping with homework, attending parent-teacher conferences, and providing a supportive home environment conducive to learning.

Job Satisfaction

Job satisfaction serves as a criterion variable in psychology, reflecting the influence of workplace conditions, job roles, and economic factors on employees’ contentment within organizational systems.

It is crucial to understand how economic factors such as salary, benefits, and job security can deeply impact job satisfaction .

Workplace conditions, including the physical environment, organizational culture, and interpersonal relationships, also play a significant role in how content an employee feels in their role.

Systemic elements like leadership styles, communication practices, and opportunities for growth and development can greatly influence job satisfaction levels.

Mental Health Status

Mental health status is a significant criterion variable in psychological research, often studied through non-experimental research designs to assess the impact of external factors, including stress, trauma, and environmental changes like climate change.

Researchers analyze mental health status to understand how individuals are influenced by various phenomena, making it a vital aspect of many studies.

Terms such as endogenous variables are used to evaluate the effects of policy changes on mental health outcomes, providing a framework to explore the intricate relationships between different factors.

Personality Traits

Personality traits are criterion variables studied in psychology to test hypotheses, understand individual differences, and inform specific policies or interventions related to areas such as education, mental health, and social support.

Exploring personality traits as criterion variables provides insight into the causation and relationship between individual behavior and environmental factors. By examining traits like extraversion, openness, conscientiousness, agreeableness, and neuroticism, researchers can draw connections to various outcomes, from academic performance to job satisfaction. Understanding the role of personality traits in shaping behaviors not only enriches psychological studies but also plays a crucial part in designing effective interventions and targeted support programs for diverse populations.

What are the Limitations of Using Criterion Variables in Psychological Research?

While criterion variables are valuable in psychological research, limitations arise when attempting to capture complex real-world differences, model multifaceted phenomena, or account for diverse educational levels that can impact study outcomes.

Frequently Asked Questions

What is a criterion variable in psychology.

A criterion variable in psychology is a measurable outcome or behavior that is being studied or predicted in a research study. It is also known as a dependent variable, as it is influenced by the independent variable being manipulated in the study.

How is a criterion variable used in psychological research?

A criterion variable is used to determine the effectiveness of a treatment or intervention on a specific outcome. It allows researchers to measure the impact of the independent variable on the dependent variable and draw conclusions about the relationship between the two.

What are some examples of criterion variables in psychology?

Some examples of criterion variables in psychology include test scores, behavior ratings, physiological measures, and self-report measures. These variables are used to assess changes or differences in behavior, thoughts, or emotions in response to an independent variable.

Can a criterion variable change in a study?

Yes, a criterion variable can change in a study. This change can be due to the manipulation of the independent variable or other factors that may influence the outcome. It is important for researchers to carefully control for these potential confounding variables to ensure accurate results.

How do researchers select a criterion variable?

Researchers typically select a criterion variable that is relevant to their study and can be reliably measured. They may also use multiple criterion variables to assess different aspects of the outcome or behavior being studied.

Why is the use of a criterion variable important in psychology?

The use of a criterion variable allows researchers to objectively measure the effects of an independent variable and make informed conclusions about its impact. It also helps to ensure the validity and reliability of a study by providing a clear and measurable outcome to be analyzed.

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Criterion Validity: Definition, Types of Validity

Design of Experiments > Criterion Validity

What is Criterion Validity?

• A job applicant takes a performance test during the interview process. If this test accurately predicts how well the employee will perform on the job, the test is said to have criterion validity.
• A graduate student takes the GRE . The GRE has been shown as an effective tool (i.e. it has criterion validity) for predicting how well a student will perform in graduate studies.

The first measure (in the above examples, the job performance test and the GRE) is sometimes called the predictor variable or the estimator . The second measure is called the criterion variable as long as the measure is known to be a valid tool for predicting outcomes.

One major problem with criterion validity, especially when used in the social sciences, is that relevant criterion variables can be hard to come by.

Types of Criterion Validity

The three types are:

• Predictive Validity : if the test accurately predicts what it is supposed to predict. For example, the SAT exhibits predictive validity for performance in college. It can also refer to when scores from the predictor measure are taken first and then the criterion data is collected later.
• Concurrent Validity : when the predictor and criterion data are collected at the same time. It can also refer to when a test replaces another test (i.e. because it’s cheaper). For example, a written driver’s test replaces an in-person test with an instructor.
• Postdictive validity : if the test is a valid measure of something that happened before. For example, does a test for adult memories of childhood events work?

Beyer, W. H. CRC Standard Mathematical Tables, 31st ed. Boca Raton, FL: CRC Press, pp. 536 and 571, 2002. Dodge, Y. (2008). The Concise Encyclopedia of Statistics . Springer. Vogt, W.P. (2005). Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Sciences . SAGE. Wheelan, C. (2014). Naked Statistics . W. W. Norton & Company

What is a Criterion Variable? (Explanation + Examples)

A  criterion variable  is simply another name for a  dependent variable  or a  response variable . This is the variable that is being predicted in a statistical analysis.

Just as explanatory variables have different names like  predictor variables  or  independent variables , a response variable also has interchangeable names like  dependent variable  or  criterion variable .

What are Some Examples of Criterion Variables?

The following scenarios illustrate examples of criterion variables in several different settings.

Example 1: Simple Linear Regression

Simple linear regression   is a statistical method we use to understand the relationship between two variables, x and y. One variable, x, is known as the predictor variable. The other variable, y, is known as the  criterion variable , or  response variable .

In simple linear regression, we find a “line of best fit” that describes the relationship between the predictor variable and the criterion variable.

For example, we may fit a simple linear regression model to a dataset using  hours studied  as the predictor variable and  test score  as the criterion variable. In this case, we would use simple linear regression to attempt to predict the value of our criterion variable  test score .

Or, as another example, we may fit a simple linear regression model to a dataset using  weight  to predict the value for  height  for a group of people. In this case, our criterion variable is  height  since that’s the value we’re interested in predicting.

If we plotted the values for height and weight on a scatterplot , the criterion variable  height  would be on the y-axis:

In general, the criterion variablewill be along the y-axis when we create a scatterplot and the predictor variable will be along the x-axis.

Example 2: Multiple Linear Regression

Multiple linear regression  is similar to simple linear regression, except we use several predictor variables to predict the value of one criterion variable. 

For example, we may use the predictor variables  hours studied  and hours of sleep the night before the test to predict the value of the criterion variable  test score . In this case, our criterion variable is the variable being predicted in this analysis.

Example 3: ANOVA

An  ANOVA  (analysis of variance) is a statistical technique we use to find out if there is a statistically significant difference between the means of three or more independent groups.

The criterion variable is  weight loss,  measured in pounds. We can conduct a one-way ANOVA to determine if there is a statistically significant difference between the resulting weight loss from the three programs.

In this case, we’re interested in understanding whether the value of the criterion variable  weight loss  differs among the three exercise programs. 

If we instead analyzed  exercise program  and   average hours slept per night, we would conduct a two-way ANOVA since we are interested in seeing how two factors impact weight loss.

Once again, though, our criterion variable is still weight loss  because we are interested in how the value of this variable differs for different levels of  exercise and  sleep .

Additional Reading:

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 .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Very informative, concise and helpful. Thank you

Helping information.Thanks

practical and well-demonstrated

Very helpful and insightful

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