An Examination of G-Theory Methods for Modeling Multitrait–Multimethod Data

Abstract

For nearly three decades, the predominant approach to modeling the latent structure of multitrait–multimethod (MTMM) data in organizational research has involved confirmatory factor analysis (CFA). Despite the frequency with which CFA is used to model MTMM data, commonly used CFA models may produce ambiguous or even erroneous results. This article examines the potential of generalizability theory (G-theory) methods for modeling MTMM data and makes such methods more accessible to organizational researchers. Although G-theory methods have existed for more than half a century, the research literature has yet to provide a clear description and integration of latent models implied by univariate and multivariate G-theory with MTMM data, notions of construct validity, and CFA. To help fill this void, the authors first provide a jargon-free overview of the univariate and multivariate G-theory models and analytically demonstrate linkages between their parameters (variance and covariance components), elements of the MTMM matrices, indices of convergent and discriminant validity, and CFA. The authors conclude with a discussion and empirical illustration of a G-theory-based modeling process that helps clarify the use of G-theory methods for modeling MTMM data.