what is mediator variable in research

Mediator vs moderator—which is right for you?

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18 April 2023

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Miroslav Damyanov

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Including mediators and moderators in your research not only allows you to study the relationship between two variables but can also help you avoid biases that could occur without them. Read further to learn more about how to distinguish and apply mediators and moderators in research.  

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• What is mediation in research?

Mediators provide a way for independent variables (aka causal variables or intervention) to impact a dependent variable (aka an outcome or effect). They explain the how and why relationship between the two. A mediator is always caused by the independent variable and influences the dependent variable. Consider a mediator as the “middleman” between independent and dependent variables. 

When using mediators, you can learn how or why an effect takes place. When using mediation analysis, you’re testing a hypothetical causal chain. If variable A affects variable B, then it turns into variable C.   

Perhaps an easier way to understand it is mediation analysis gives us information about how or why the independent variable affects the dependent variable. Taking the mediator out of the model in complete mediation eliminates the relationship between the dependent and independent variables . This is because the mediator thoroughly explains the relationship between the dependent and independent variables, and without it, there’s no relationship.

You can use a mediator in your research to see if the influence of the mediator is stronger than the direct influence of the independent variable. A mediator carries an effect to the direct variable from the indirect variable. 

A mediation flow chart might read: the independent variable causes the mediator, the mediator must change the dependent variable, and the mediator changes the relationship or correlation between the independent and the dependent variables.   

• What is moderation in research?

While mediation answers the how and why, moderators look at interactions. Moderation analysis is the level that the relationship between the independent and dependent variables changes as a function of a third variable or the moderator. 

Moderator variables are sometimes referred to as interactions or products because they can affect the strength or direction of the relationship between the independent and dependent variables. They make the relationship stronger or weaker or change it from strong to moderate to no influence at all.

Moderators can be either quantitative or qualitative. Examples of quantitative moderators might be numerical values such as test scores, weight, age, IQ, etc. Examples of qualitative moderators would be factors with no numeric value, such as gender, race, or education. 

The moderator can also be changed to determine the amount of change in the relationship between the variables since it influences the level, direction, or strength of the relationship.  

The purpose of using a moderator in your research is to help you determine what categorical or quantitative variables change the relationship between your independent and dependent variables or to determine if there’s any validity to the moderator you have chosen and the changes predicted.

• Mediator vs moderator variables

Partial or complete mediation

We can talk about partial or complete mediation. 

In complete or full mediation, a mediator explains the relationship between the independent and dependent variables. If the mediator is removed, a relationship no longer exists, explaining why it’s called complete.  

With partial mediation, when the mediator is removed from the model, there’s still a statistical relationship between the independent and dependent variables, meaning the mediator only partially explains the relationship.

Strength and direction of the effect

Moderation in research refers to the extent to which the relationship between two variables changes depending on the level of a third variable, which is known as the moderator variable. A moderator can change the strength and direction of the relationship between the independent and dependent variables.  

Mediation analysis

Mediation analysis uses either an analysis of variance or linear regression analysis to test whether a variable is a mediator. As explained previously, mediation may either be partial or complete. Mediators are caused by the independent variable, and they also influence the dependent variable. 

Moderation analysis

Moderation analysis is a commonly used technique in statistical modeling to help understand the nature of the relationships between variables and to identify the conditions under which those relationships are strongest or weakest. Moderation analysis can be conducted using various statistical methods, such as multiple regression, ANOVA, and structural equation modeling. 

Moderators used in research can be categorical, such as race, gender, religion, etc., and are usually not quantifiable, or they can be quantitative, such as age, income, weight, etc. If a moderator is removed, a relationship will still exist between the independent and dependent variables—unlike when a mediator is removed from complete mediation.  

• Mediator vs moderator examples

A simple example of a mediator variable:

Exercise affects family relationships because it creates endorphins.

Exercise is the independent variable, and family relationships are the dependent variable.  Endorphins are the mediator.

Without the mediator, your variables don’t have a relationship with each other.  However, when looking at moderators, as in the next example, there would still be a relationship, but a moderator explains how the variables are affected.

A simple example of a moderator example:

Seniors are more likely to have accidents due to vision impairments.

Accidents are the independent variable, vision impairments are the dependent variable, and seniors are the moderator (age being the variable you use to create the group 'seniors').

What is the difference between a confounder and a mediator?

A mediator is the mechanism of a relationship between two variables: it explains the process by which they’re related. A confounder, however, is a third variable that affects variables of interest and makes them appear related when they actually are not.

Is a mediator an IV or DV?

IVs (independent variables) and DVs (dependent variables) are different from a mediator. The mediator variable is used to explain the relationship between the independent variable and the dependent variable. Without the mediator variable, there’s no relationship between the other two in the case of complete mediation.

How can you tell if a variable is a mediator?

When mediation is in place, there’s a correlation between the independent variable and the mediator variable, as well as a correlation between the mediator variable and the dependent variable. 

In other words, mediation occurs when the relationship between the independent variable and the dependent variable is partially or completely explained by the mediator variable, which in turn means that there is a correlation between the independent variable and the mediator variable and between the mediator variable and the dependent variable.  

Put simply, it’s seen as a mediator variable if the change in the level of the independent variable significantly accounts for the variation in the other variable. 

What is an example of moderation?

If work experience influences starting salary, how big a role does gender play? Work experience is the independent variable, salary is the dependent variable, and gender is the moderator. 

Can a variable be both moderating and mediating?

Yes, a variable can be both moderating and mediating, although the two roles are distinct and serve different purposes in statistical analysis. It’s dependent on the framing of the study and the independent variable and dependent variable you’re working with. 

Consider that a variable can be both a mediator and a moderator when it affects both the strength and the direction of the relationship between two variables and also explains the mechanism through which the relationship occurs. 

Is a moderator variable a confounder?

A confounder is a variable that’s related to both the predictor of interest and the outcome, but it’s not on the causal pathway. A moderator is a variable that changes the direction or strength of the relationship between the independent and the dependent variables.

How do you identify a moderating variable in research?

To identify a moderating variable in research, you must look for a variable that influences the direction or strength of the relationship between the independent and dependent variables. Overall, identifying a moderating variable requires careful consideration of the research question, theoretical framework, and data analysis techniques used in the study. 

What is a mediator relationship?

A mediator relationship is a type of relationship in which a third variable, called a mediator, explains the relationship between two other variables. More specifically, a mediator variable that acts as a “middleman” explains how or why two other variables are related to each other. 

What is a moderator relationship?

A moderator relationship, also known as an interaction or conditioning effect, is one where the independent and dependent variables have a relationship, but by adding the third, or a moderating variable, the relationship changes. Moderators point out the when, who, or under what circumstances.

Can a mediator be a moderator?

No, a mediator variable and a moderator variable are two distinct concepts in statistical analysis and cannot be the same variable.

A mediator variable explains the relationship between two other variables. A moderator variable affects the direction or strength of the relationship between two other variables.

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Article contents

Mediator variables.

• Matthew S. Fritz Matthew S. Fritz Department of Educational Psychology, University of Nebraska - Lincoln
• , and  Houston F. Lester Houston F. Lester Department of Educational Psychology, University of Nebraska - Lincoln
• https://doi.org/10.1093/acrefore/9780190236557.013.19
• Published online: 22 December 2016

Mediator variables are variables that lie between the cause and effect in a causal chain. In other words, mediator variables are the mechanisms through which change in one variable causes change in a subsequent variable. The single-mediator model is deceptively simple because it has only three variables: an antecedent, a mediator, and a consequent. Determining that a variable functions as a mediator is a difficult process, however, because causation can be inferred only when many strict assumptions are met, including, but not limited to, perfectly reliable measures, correct temporal design, and no omitted confounders. Since many of these assumptions are difficult to assess and rarely met in practice, the significance of a statistical test of mediation alone usually provides only weak evidence of mediation.

New methodological approaches are constantly being developed to circumvent these limitations. Specifically, new methods are being created for the following purposes: (1) to assess the impact of violating assumptions (e.g., sensitivity analyses) and (2) to make fewer assumptions and provide more flexible analysis techniques (e.g., Bayesian analysis or bootstrapping) that may be more robust to assumption violations. Despite these advances, the importance of the design of a study cannot be overstated. A statistical analysis, no matter how sophisticated, cannot redeem a study that measured the wrong variables or used an incorrect temporal design.

• mediator variables
• mediation analysis
• causal analysis
• temporal design
• Bayesian analysis
• bootstrapping

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Paul Spector

Industrial and Organizational Psychology

What Is the Difference Between Mediator and Moderator Variables?

No two statistical concepts are more often confused than mediator and moderator variables. Both are “third” variables that affect the relationship between two other variables, although in different ways. It is easy to confuse the terms because they both start with M, have four syllables, and sound similar. I will note the difference as I answer what is the difference between mediator and moderator variables.

Mediator Variables?

A mediator variable is a link in a causal chain where one variable leads to another that leads to another. If we think of an X variable that is a cause of a Y variable, the mediator variable (M) is the intermediate step between X and Y. In other words, X leads to M and M leads to Y. For example, abusive behavior by supervisors (X) leads to feelings of anger in employees that experience it (M), and the negative feelings lead the person to quit the job (Y). We might say that employee anger mediates the relationship between abusive behavior and quitting. It can be an explanatory variable that accounts for why X leads to Y–abusive behavior leads to quitting, and anger is the intermediate mechanism.

Moderator Variables?

A moderator is a variable that affects the relationship between two other variables. That relationship might be different for high versus low levels of the moderator. For example, we might observe that grades on a classroom exam were related to amount of time students spent studying. We might further expect that there are differences among students in the extent to which studying is helpful. Some students benefit a lot, and others not so much. A variable that accounts for those differences would be a moderator. Students who have a high level of math ability, for example, might benefit more from studying in a math class than students have have a low level of ability. Studying might have a strong relationship with grades for high ability students, and little relationship for low ability students. We would say that ability moderates the relationship between amount of studying and grades.

There are several distinctions between mediator and moderator variables.

• Mediator variables assume a causal process of one variable leading to the mediator and the mediator leading to the next variable in a sequence over time. Moderators do not have to concern causal processes but can affect relationships that are not causal.
• Mediator variables must correlated with the X and Y variables they are mediating. Moderator variables do not have to correlate with either of the other two variables.
• You can have multiple mediator variables that occur in a specific sequence. Although you can have multiple moderator variables, they are not sequential. For example, quality of studying as well as ability might moderate the relationship between amount of studying and grades.
• Moderators are useful in determining boundary conditions under which relationships might be found, and identifying such relationships can be helpful for practitioners as well as academic researchers. Mediators are purely theoretical entities that are used to test theoretical mechanisms primarily of interest to academic researchers.

Mediator and moderator variables can be seen often in studies across industrial-organizational psychology , management, and many other fields. Methods used to analyze them are important tools in the researcher’s toolkit. They are often confused by students and researchers alike, so it is important to keep in mind what they are and how they differ.

Image generated by DALL-E 4.0. ChatGPT 4.0 helped with the studying example.

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2 Replies to “What Is the Difference Between Mediator and Moderator Variables?”

Thank you for clearing up the often-confused concepts of mediator and moderator variables! Could you clarify the section on how moderators determine boundary conditions in relationships, and why this is useful for both practitioners and researchers?

A moderator could illustrate a boundary condition by showing that a relationship occurs for some conditions and not others. Take a simple case of a binary moderator of rural vs. urban location. We might find that customer service values of employees predicts sales in rural locations but not urban locations. Location moderating the relationship between values and sales would indicate a boundary condition in that values works in one setting and not the other.

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Published by Nicolas at March 15th, 2024 , Revised On March 12, 2024

Key Differences Between Mediating Vs Moderating Variables

Mediators: These are intervening variables that explain the process of how an independent variable (IV) influences a dependent variable (DV). They act as the mechanism, clarifying how the IV impacts the DV.

Table of Contents

Moderators: These are conditioning variables that alter the strength or direction of the relationship between the IV and DV. They act as the context, revealing under what conditions the effect of the IV on the DV is modified.

What Are Mediating Variables 

Mediating variables play a crucial role in understanding the mechanism or process through which an independent variable influences a dependent variable. In simpler terms, they help to explain why or how the relationship between the independent and dependent variables exists.

Mediating variables are often referred to as intermediate variables or mechanisms in dissertations and research papers , because they mediate or intervene in the relationship between the independent and dependent variables. They provide insight into the underlying processes or pathways that link the variables of interest.

For example, let’s say we’re studying the relationship between exercise (independent variable) and mental health (dependent variable). A mediating variable in this scenario could be self-esteem. Through self-esteem, exercise may positively influence mental health by boosting confidence and reducing stress levels. Therefore, self-esteem acts as a mediator in explaining the relationship between exercise and mental health.

How Mediating Variables Work

Mediating variables operate by transmitting the effects of the independent variable to the dependent variable. They serve as a bridge or mechanism through which the influence of the independent variable is conveyed to the dependent variable in a thesis or dissertation .

In statistical terms, mediating variables are often tested using mediation analysis techniques. These techniques assess the indirect effect of the independent variable on the dependent variable through the mediator.

The strength and significance of the indirect effect help researchers determine the extent to which the mediating variable explains the relationship between the independent and dependent variables.

Mediating variables can operate through various mechanisms, including cognitive processes, emotional responses, physiological changes, or behavioural pathways. Understanding how these mechanisms function is essential for accurately identifying and interpreting mediating variables in research studies.

Examples Of Mediating Variables

In a study investigating the relationship between socioeconomic status (independent variable) and academic achievement (dependent variable), the mediating variable could be parental involvement in education . Parental involvement may mediate the relationship between socioeconomic status and academic achievement by influencing factors such as parental support, access to educational resources, and motivation.

In research exploring the impact of job satisfaction (independent variable) on employee performance (dependent variable), a mediating variable could be organizational commitment. Organizational commitment may mediate the relationship by fostering greater engagement, loyalty, and effort among employees, thereby enhancing their performance.

In a study examining the effects of stress (independent variable) on physical health outcomes (dependent variable), a mediating variable could be coping strategies. Coping strategies, such as problem-solving or seeking social support, may mediate the relationship by influencing the body’s physiological response to stress and buffering against its negative effects on health.

What Are Moderating Variables 

Moderating variables, also known as interaction variables, serve to qualify or alter the relationship between an independent variable and a dependent variable. Unlike mediating variables, which explain why or how a relationship exists, moderating variables influence the strength or direction of the relationship under different conditions.

In essence, moderating variables answer the question: “When does the relationship between the independent and dependent variables change?”

Moderating variables introduce variability into the relationship between the independent and dependent variables by affecting the conditions under which the relationship holds or differs. They can highlight the circumstances under which the effects of the independent variable on the dependent variable are enhanced, attenuated, or even reversed.

How Moderating Variables Work

Moderating variables operate by altering the nature of the relationship between the independent and dependent variables across different levels or conditions of the moderating variable.

For instance, let’s consider a study examining the relationship between study time (independent variable) and academic performance (dependent variable), with motivation as a moderating variable. In this scenario, motivation may influence the strength of the relationship between study time and academic performance. Students with high motivation may demonstrate a stronger positive relationship between study time and performance compared to students with low motivation.

Moderating variables are often tested using interaction terms in statistical analyses, such as regression analysis. Interaction terms allow researchers to assess whether the effect of the independent variable on the dependent variable varies depending on different levels or conditions of the moderating variable.

Examples of Moderating Variables

In research investigating the impact of mentoring programs (independent variable) on career advancement (dependent variable) among employees, years of experience could serve as a moderating variable.

The relationship between mentoring programs and career advancement may be stronger for employees with fewer years of experience, as they may benefit more from guidance and support compared to seasoned employees.

In a study exploring the effects of parenting style (independent variable) on adolescent behaviour (dependent variable), family income could act as a moderating variable. The relationship between parenting style and adolescent behaviour may differ based on different levels of family income.

For example, here is a hypothesis based on the above statement, authoritative parenting may have a more positive influence on behaviour among adolescents from higher-income families compared to those from lower-income families.

In research examining the impact of advertising (independent variable) on purchasing behaviour (dependent variable), product relevance could serve as a moderating variable.

The relationship between advertising and purchasing behaviour may be stronger for products that are highly relevant to consumers’ needs or desires compared to products with low relevance.

Mediating variables intervene in the relationship between the independent and dependent variables, explaining the underlying mechanism or process through which the independent variable affects the dependent variable. They provide insight into why or how the relationship exists and are often referred to as intermediate variables.

Moderating variables, on the other hand, qualify or alter the strength or direction of the relationship between the independent and dependent variables under different conditions. They influence the circumstances under which the relationship holds or differs, rather than explaining why or how it exists.

The primary function of mediating variables is to elucidate the mechanism or process through which the independent variable influences the dependent variable. They help researchers understand the underlying pathways or intervening variables that transmit the effect of the independent variable to the dependent variable.

Moderating variables function to qualify or alter the relationship between the independent and dependent variables under different conditions. They introduce variability into the relationship and thesis statement by influencing the strength or direction of the relationship across different levels or conditions of the moderating variable.

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Relationship With The Independent And Dependent Variables

Mediating variables intervene between the independent and dependent variables, mediating or transmitting the effect of the independent variable to the dependent variable. They provide a deeper understanding of the causal mechanisms underlying the relationship between the independent and dependent variables.

Moderating variables interact with the independent and dependent variables, influencing the nature of their relationship under different conditions. They do not intervene in the relationship itself but instead modify or qualify the relationship based on different levels or conditions of the moderating variable.

Consider a study examining the relationship between job training programs (independent variable) and job performance (dependent variable), with skill acquisition as a potential mediator. In this scenario, skill acquisition mediates the relationship between job training programs and job performance by explaining how the acquisition of new skills enhances job performance.

Now, imagine the same study but with job satisfaction as a potential moderator. Here, job satisfaction may moderate the relationship between job training programs and job performance by influencing the extent to which employees benefit from the training programs.

For instance, employees with high job satisfaction may exhibit a stronger positive relationship between training programs and performance compared to those with low job satisfaction.

Frequently Asked Questions

What is the difference between moderating and mediating variables.

Mediating variables explain the mechanism or process through which an independent variable affects a dependent variable, while moderating variables influence the strength or direction of the relationship between the independent and dependent variables under different conditions.

What is an example of a mediating variable?

In a study on the relationship between exercise (independent variable) and mental health (dependent variable), self-esteem serves as a mediating variable, explaining how exercise boosts confidence and reduces stress levels, thereby improving mental health outcomes.

What is a moderating variable example?

In research on the impact of teaching methods (independent variable) on student performance (dependent variable), student engagement (moderating variable) may influence the strength of the relationship, with higher engagement enhancing the effectiveness of certain teaching methods.

What is the difference between intervening variables and moderating variables?

Intervening variables, like mediating variables, explain the relationship between independent and dependent variables. Moderating variables, however, influence the strength or direction of this relationship under varying conditions, rather than explaining the mechanism itself.

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Introduction to the Mediation Model

• First Online: 07 October 2023

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• J. P. Verma 4 &
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Part of the book series: Synthesis Lectures on Mathematics & Statistics ((SLMS))

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Mediation analysis is a powerful statistical technique used to understand the relationship between two variables and how one variable influences the other through a mediator variable.

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Verma, J.P., Verma, P. (2024). Introduction to the Mediation Model. In: Understanding Structural Equation Modeling. Synthesis Lectures on Mathematics & Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-32673-8_4

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