Abstract
ABSTRACTMultirater (multimethod, multisource) studies are increasingly applied in psychology. Eid and colleagues (2008) proposed a multilevel confirmatory factor model for multitrait-multimethod (MTMM) data combining structurally different and multiple independent interchangeable methods (raters). In many studies, however, different interchangeable raters (e.g., peers, subordinates) are asked to rate different targets (students, supervisors), leading to violations of the independence assumption and to cross-classified data structures. In the present work, we extend the ML-CFA-MTMM model by Eid and colleagues (2008) to cross-classified multirater designs. The new C4 model (Cross-Classified CTC[M-1] Combination of Methods) accounts for nonindependent interchangeable raters and enables researchers to explicitly model the interaction between targets and raters as a latent variable. Using a real data application, it is shown how credibility intervals of model parameters and different variance components can be obtained using Bayesian estimation techniques.
Highlights
IntroductionA growing body of research is devoted to multirater (multimethod, multisource) measurement designs (Campbell & Fiske, 1959; Eid & Diener, 2006; Kenny, 1995)
A growing body of research is devoted to multirater measurement designs (Campbell & Fiske, 1959; Eid & Diener, 2006; Kenny, 1995)
Due to the sampling procedure, measurement designs with interchangeable raters imply a multilevel data structure
Summary
A growing body of research is devoted to multirater (multimethod, multisource) measurement designs (Campbell & Fiske, 1959; Eid & Diener, 2006; Kenny, 1995). Structurally different raters cannot be replaced by one another, given that they do not belong to a common pool of raters, but differ with respect to their role or relation with the target (e.g., student selfreports, parent reports). They may have fundamentally different perspectives and information about the target’s behavior (e.g., physiological measures vs self-reports vs implicit measures). They may have fundamentally different perspectives and information about the target’s behavior (e.g., physiological measures vs. self-reports vs. implicit measures). Eid et al (2008) proposed different CFA-MTMM models for measurement designs with structurally different, interchangeable, and a combination
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