The Operations Triad Model and Youth Mental Health Assessments: Catalyzing a Paradigm Shift in Measurement Validation.

Researchers strategically assess youth mental health by soliciting reports from multiple informants. Typically, these informants (e.g., parents, teachers, youth themselves) vary in the social contexts where they observe youth. Decades of research reveal that the most common data conditions produced with this approach consist of discrepancies across informants' reports (i.e., informant discrepancies). Researchers should arguably treat these informant discrepancies as domain-relevant information: data relevant to understanding youth mental health domains (e.g., anxiety, depression, aggression). Yet, historically, in youth mental health research as in many other research areas, one set of paradigms has guided interpretations of informant discrepancies: Converging Operations and the Multi-Trait Multi-Method Matrix (MTMM). These paradigms (a) emphasize shared or common variance observed in multivariate data, and (b) inspire research practices that treat unique variance (i.e., informant discrepancies) as measurement confounds, namely random error and/or rater biases. Several yearsw ago, the Operations Triad Model emerged to address a conceptual problem that Converging Operations does not address: Some informant discrepancies might reflect measurement confounds, whereas others reflect domain-relevant information. However, addressing this problem requires more than a conceptual paradigm shift beyond Converging Operations. This problem necessitates a paradigm shift in measurement validation. We advance a paradigm (Classifying Observations Necessitates Theory, Epistemology, and Testing [CONTEXT]) that addresses problems with using the MTMM in youth mental health research. CONTEXT optimizes measurement validity by guiding researchers to leverage (a) informants that produce domain-relevant informant discrepancies, (b) analytic procedures that retain domain-relevant informant discrepancies, and (c) study designs that facilitate detecting domain-relevant informant discrepancies.