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A criterion variable, also known as a dependent variable, is a measurable factor that is affected by one or more independent variables in a study or experiment. It is used to assess the impact or effectiveness of the independent variable(s) on the outcome being measured. The relationship between the independent and dependent variables is often examined through statistical analysis.
Examples of criterion variables include test scores, reaction times, and survey responses. In a study examining the effects of exercise on weight loss, the criterion variable would be the amount of weight lost, while the independent variable would be the frequency or intensity of exercise. In a survey measuring job satisfaction, the criterion variable would be the overall level of satisfaction, while the independent variables could be factors such as salary, work-life balance, and job responsibilities.
In summary, a criterion variable is a crucial component in research and serves as a measurable outcome that is influenced by independent variables. It helps to determine the effectiveness or impact of the independent variables on the outcome being studied.
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 aone-way ANOVAto 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.
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