Throwing Out the Bathwater but Keeping the Baby: Extending Campbell-Fiske’s Multitrait-Multimethod Framework

The 65th anniversary of Campbell and Fiske’s (1959) multitrait-multimethod (MTMM) framework provides a timely opportunity to revisit and modernize this foundational model for construct validation. Although structural equation modeling (SEM)-based MTMM approaches have enhanced the field, their widespread application remains constrained by convergence problems, ambiguous trait-method distinctions, and a lack of consensus regarding optimal model specifications. We propose an Extended Campbell-Fiske Framework that resolves these limitations while preserving the original Guidelines’ conceptual strengths. Our key innovation is to apply the MTMM logic to a fully latent multitrait-multidomain (MTMD) correlation matrix, derived from a rigorously tested multiple-indicator measurement model. By treating both traits and methods (i.e., domains, occasions, informants, contexts, or another method facet) as fully symmetrical, substantive facets, the approach eliminates reliance on manifest correlations, corrects for measurement error, and introduces formal asymptotic parameter comparisons to test each validity criterion. This framework provides a formatively heuristic structure that retains the original appeal of the MTMM logic for applied research while meeting current psychometric standards for transparency, reproducibility, and inferential rigor expected by leading academic journals. We illustrate the method using a large, multidimensional dataset (TIMSS 2007; N = 18,047), but the framework generalizes across domains of psychological science. The Extended Framework offers applied researchers a flexible, powerful tool for evaluating convergent and discriminant validity, diagnosing trait-domain interactions, and clarifying measurement quality. By “keeping the baby” while refreshing the empirical implementation, our approach affirms the enduring value of the Campbell-Fiske logic while aligning it with the demands of modern research practice.