Simple averaging is the most commonly used approach to creating a composite variable. When original variables are continuous, simple averaging or weighted averaging can be considered.

The specific method of averaging can be chosen based on whether the original variables intended to be combined (original variables in short) are continuous or categorical and also depend on the existing knowledge of the original variables. There are several ways to accomplish averaging. In this presentation, several commonly used methods to create composite variables are outlined, and actual studies and a numerical illustration are used to demonstrate the importance of these issues in the use of composite variables. However, there has been little discussion about these issues. The consequences of combining related variables into a composite variable can include alterations of the relationship strength with outside variables (e.g., outcome variables), changes in statistical power, over-reduction or loss of information, and challenges in interpreting the composite variable itself or the relationships with outside variables. Using composite variables is a common practice for controlling Type I error rate (e.g., when a sample size is not sufficient for testing multiple comparisons), addressing multicollinearity for regression analysis, or organizing multiple highly correlated variables into more digestible or meaningful information.

The individual variables making up a composite variable may be scales, single or global ratings, or categorical variables. A composite variable is a variable made up of two or more variables or measures that are highly related to one another conceptually or statistically ( Ley, 1972).

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