Partial out the effect of a variable is to remove the variance that the variable shares with other variables in the analysis before looking at their relationships. Which term defines this action?

• A. To remove outliers from the data.
• B. To partial out the effect of a variable is to remove the variance that the variable shares with other variables in the analysis before looking at their relationships.
• C. To average the variables to obtain a single score.
• D. To standardize all variables to have the same scale.

Answer: (B)

Partialing out a variable means removing the portion of each variable’s variance that is tied to that control variable so you can inspect relationships without that shared influence. This is a form of statistical control, often implemented by regressing the variables on the control variable and using the residuals, or by looking at a partial correlation that keeps the control variable constant. For example, to see how X relates to Y independent of Z, you remove Z’s influence from both X and Y and then examine the relationship of the resulting residuals. So, the action is called partialing out (controlling for) a variable. Other options describe different data-prep steps—outlier removal, averaging, and standardizing—not the process of removing shared variance to reveal raw relationships.