Sum Scores of Behavioral Measurement Scales: When Not to Recommend Them and How Structural Equation Modeling Can Help

This article revisits the popular and widely used sum scores resulting from multiple-component measuring instruments, in the light of a recent resurgence of interest in them. We draw attention to a potentially serious disadvantage of sum scores that can be associated with (i) substantially larger bias and mean squared error than an alternative approach to parameter estimation, in addition to (ii) the inconsistency feature of an estimator frequently of special interest when utilizing the sum scores in behavioral and social studies. This drawback can be counteracted using a readily applicable structural equation modeling approach, which we outline and illustrate with data from empirically relevant settings. The article concludes with a discussion of extensions and limitations of the described procedure for examining the relationships between response variables and constructs evaluated with multi-component scales.