Examining Quadratic Relationships Between Traits and Methods in Two Multitrait-Multimethod Models.

Multitrait-multimethod (MTMM) analysis is one of the most frequently employed methods to examine the validity of psychological measures. Confirmatory factor analysis (CFA) is a commonly used analytic tool for examining MTMM data through the specification of trait and method latent variables. Most contemporary CFA-MTMM models either do not allow estimating correlations between the trait and method factors or they are restricted to linear trait-method relationships. There is no theoretical reason why trait and method relationships should always be linear, and quadratic relationships are frequently proposed in the social sciences. In this article, we present two approaches for examining quadratic relations between traits and methods through extended latent difference and latent means CFA-MTMM models (Pohl et al., 2008; Pohl and Steyer, 2010). An application of the new approaches to a multi-rater study of the nine inattention symptoms of attention-deficit/hyperactivity disorder in children (N = 752) and the results of a Monte Carlo study to test the applicability of the models under a variety of data conditions are described.

A latent state-trait model for analyzing states, traits, situations, method effects, and their interactions.

OBJECTIVE: Studies on situation and Person × Situation interaction effects often use only one method of measurement, so that the extent to which the effects may be method-specific cannot be determined. We introduce a new multimethod latent state-trait model for random and fixed situations (MM-LST-RF), which allows examining person, situation, and Person × Situation interaction effects in the context of multimethod measurement designs (e.g., studies with multiple reporters), and discuss potential areas of application for the new model in personality research. METHOD: The new model allows analyzing novel features of multimethod, multi-situation data, including (a) the convergent validity and method specificity of trait (person) effects, situation effects, and Person × Situation interaction effects; (b) the degree of situation specificity of method effects; and (c) potential Method × Situation interactions. An application to smoker's affect (N = 235; 57% female; 93% Caucasian) before and after quitting smoking is presented with positively and negatively worded items as methods. RESULTS: The MM-LST-RF model fit the smoking data well. Method specificity of many effects was high. CONCLUSIONS: The MM-LST-RF model provides researchers with a new framework for testing method specificity of person, situation, and interaction effects.