A Comparison of Approaches to Forming Composite Measures in Structural Equation Models

A common practice in applications of structural equation modeling techniques is to create composite measures from individual items. The purpose of this article was to provide an empirical comparison of several composite formation methods on model fit. Data from 1,177 public school teachers were used to test a model of union commitment in which alternative composite formation methods were used to specify the measurement components of the model. Bootstrapping procedures were used to generate data for two additional sample sizes. Results indicated that the use of composites, in general, resulted in improved overall model fit as com-pared to treating all items as individual indicators. Lambda values and explained criterion variance indicated that this improved model fit was due to the creation of strong measurement models. Implications of these results for researchers using composites are discussed. Structural equation modeling (SEM) has become one of the most widely applied data analytic techniques in organizational research. One of the primary reasons for this widespread use is the ability of the technique to assess simultaneously the fit of meas