Hierarchically Nested Covariance Structure Models for Multitrait-Multimethod Data

TLDR

The paper presents a taxonomy of covariance structure models for representing multitrait‑multimethod data. Using this taxonomy, the authors formulate hierarchically nested models that enable significance testing of model differences and assessment of convergent, discriminant validity, and method variance. Applying the framework to three multitrait‑multimethod matrices resolved contradictory conclusions from earlier studies, demonstrating its utility.

Abstract

A taxonomy of covariance structure models for rep resenting multitrait-multimethod data is presented. Us ing this taxonomy, it is possible to formulate alternate series of hierarchically ordered, or nested, models for such data. By specifying hierarchically nested models, significance tests of differences between competing models are available. Within the proposed framework, specific model comparisons may be formulated to test the significance of the convergent and the discriminant validity shown by a set of measures as well as the ex tent of method variance. Application of the proposed framework to three multitrait-multimethod matrices al lowed resolution of contradictory conclusions drawn in previously published work, demonstrating the utility of the present approach.