What is the difference between moderating variable and intervening variable

Many types of variables exist, and you must choose the right variable to measure when designing studies, selecting tests and interpreting results. A strong understanding of variables can lead to more accurate statistical analyses and results. In this article, we describe the types of variables and answer some frequently asked questions.

Variables are things you measure, manipulate and control in statistics and research. All studies analyze a variable, which can describe a person, place, thing or idea. A variable's value can change between groups or over time.

For example, if the variable in an experiment is a person's eye color, its value can change from brown to blue to green from person to person. Researchers organize variables into a variety of categories, the most common of which include:.

An independent variable is a singular characteristic that the other variables in your experiment cannot change. Age is an example of an independent variable.

Where someone lives, what they eat or how much they exercise are not going to change their age. Independent variables can, however, change other variables. In studies, researchers often try to find out whether an independent variable causes other variables to change and in what way. A dependent variable relies on and can be changed by other components. A grade on an exam is an example of a dependent variable because it depends on factors such as how much sleep you got and how long you studied.

Independent variables can influence dependent variables, but dependent variables cannot influence independent variables. For example, the time you spent studying dependent can affect the grade on your test independent but the grade on your test does not affect the time you spent studying.

When analyzing relationships between study objects, researchers often try to determine what makes the dependent variable change and how. An intervening variable, sometimes called a mediator variable, is a theoretical variable the researcher uses to explain a cause or connection between other study variables—usually dependent and independent ones.

They are associations instead of observations. For example, if wealth is the independent variable, and a long life span is a dependent variable, the researcher might hypothesize that access to quality healthcare is the intervening variable that links wealth and life span.

A moderating or moderator variable changes the relationship between dependent and independent variables by strengthening or weakening the intervening variable's effect. For example, in a study looking at the relationship between economic status independent variable and how frequently people get physical exams from a doctor dependent variable , age is a moderating variable. That relationship might be weaker in younger individuals and stronger in older individuals. Control or controlling variables are characteristics that are constant and do not change during a study.

They have no effect on other variables. Researchers might intentionally keep a control variable the same throughout an experiment to prevent bias. For example, in an experiment about plant development, control variables might include the amounts of fertilizer and water each plant gets.

These amounts are always the same so that they do not affect the plants' growth. Extraneous variables are factors that affect the dependent variable but that the researcher did not originally consider when designing the experiment. These unwanted variables can unintentionally change a study's results or how a researcher interprets those results. Take, for example, a study assessing whether private tutoring or online courses are more effective at improving students' Spanish test scores.

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Academic Web Pages is the leading provider of customized websites for researchers, centers, nonprofits, and universities. Dependent Variables Dependent variables are the main outcomes or characteristics of interest. Other Variables Confounding, Mediating and Moderating Confounding Variable — A confounding variable is a variable that co-occurs with the independent variable and offers a different explanation of the results. Related posts: What metric should we use to measure program success?

We use cookies to analyze site traffic and provide a better browsing experience. If you continue to use this site, you consent to our use of cookies. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

For example, while social media use can predict levels of loneliness, this relationship may be stronger for adolescents than for older adults. Age is a moderator here. This means that the relationship between years of experience and salary would differ between men, women, and those who do not identify as men or women.

To test this statistically, you perform a multiple regression analysis for the data on work experience and salary, with gender identity added in the model. You compare the statistical significance of the model with and without gender identity included to determine whether it moderates the relationship between work experience and salary. A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related. They are important to consider when studying complex correlational or causal relationships. Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place.

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