Structural Equation Models for Multitrait-Multimethod Data

Abstract

The aim of this chapter is to illustrate advantages by presenting several multitrait-multimethod (MTMM) models that have been defined in the framework of structural equation modeling (SEM). In the first part of the chapter, the authors discuss models that have been developed to analyze an MTMM matrix with the typical structure described by Campbell and Fiske (1959) and Schmitt (chap. 2, this volume). An important characteristic of this first type of MTMM models is that there is only one indicator for each trait-method unit. The major limitation of these single-indicator MTMM models is that unsystematic measurement error and systematic method-specific influences can be separated only if strong assumptions are fulfilled. The second part of the chapter shows how this limitation can be circumvented by selecting several indicators for each trait-method unit (multiple-indicator models). In this chapter the authors note that they will not present all MTMM models that have been developed in the confirmatory factor analysis (CFA) framework. Instead they will concentrate on those models that are most often applied and discuss their strengths and weaknesses. Moreover, the authors assume that all variables are centered (deviations from the mean), which means that they focus on covariance structures and do not deal with mean structures.