1.1.4 - Variables

There may be many variables in a study. The variables may play different roles in the study. Variables can be classified as either explanatory or response variables

A variable is any characteristic, number, or quantity that can be measured, counted, or observed for record.
Response Variable
Variable that about which the researcher is posing the question. May also be called the outcome or the dependent variable
Explanatory Variable
Variable that serve to explain changes in the response. They may also be called the predictor or independent variables.
Note! A variable can serve as an explanatory variable in one study but response in another.

Example 1-3: Response and Explanatory Variables Section

Consider the variables Sex (Female, Male) and Height (in inches). Which variable do you believe explains the other? In other words, would it make more sense to say a person's sex more likely explains that person's height, or to say a person's height explains that person's sex?
In this case, Sex would explain Height, making Sex the explanatory variable and Height the response.
Consider the variable Height and Weight. Which is the response? Which is the explanatory?
In this case, a person's height would more likely explain their weight than the other way around.

Other Variables Section

Other types of variables include:

Lurking variable
A variable that is neither the explanatory variable nor the response variable but has a relationship (e.g. may be correlated) with the response and the explanatory variable. It is not considered in the study but could influence the relationship between the variables in the study.
Confounding variable
A variable that is in the study and is related to the other study variables, thus having an effect on the relationship between these variables.

A lurking variable, if included in the study, could have a confounding effect and then be classified as a confounding variable.

Example 1-4: Lurking and Confounding Variables Section

Suppose you teach a class where students must submit weekly homework and then take a weekly quiz. You want to see if there is a relationship between the scores on the two assignments (i.e. higher homework scores are aligned with higher quiz scores). As you look at the data you begin to consider whether the submission date of the homework has an effect on the quiz grades; that is, do students who submit the homework several days before taking the quiz perform better overall on the quiz than students who do not leave much of a time gap between completing the assignments (e.g. they do both on the same day). The rational is that students who allow time between the homework and quiz to study may perform better compared to the other group.


In this example, “days between submission of homework and quiz” would be a lurking variable as it was not included in the study. Now once you got that information and re-examined the relationship between the two assignments taking into consideration the time gap, if you saw a change in the relationship between the two assignments (i.e. the relationship changed somewhat from the analysis without the time gap compared to when the time gap was included) then this “days between submission” would be considered a confounding variable.

In an experiment where treatments are randomly assigned, one assumes these variables get evenly shared across the groups with the intention that any influence they may have on the outcome is negated or reduced.