Evaluating construct equivalence of youth depression measures across multiple measures and multiple studies

Abstract

Construct equivalence of measures across studies is necessary for synthesizing results when combining data in meta-analysis or integrative data analysis. We discuss several assumptions required for construct equivalence, and review methods using individual-level data and item response theory (IRT) analysis for detecting or adjusting for violations of these assumptions. We apply IRT to data from 7 measures of depressive symptoms for 4,283 youth from 16 randomized prevention trials. Findings indicate that these data violate assumptions of conditional independence. Bifactor IRT models find that depression measures contain substantial reporter variance, and indicate that a single common factor model would be substantially biased. Separate analyses of ratings by youth find stronger evidence for construct equivalence, but factor invariance across sex and age does not hold. We conclude that data synthesis studies employing measures of youth depression should analyze results separately by reporter, explore more complex approaches to integrate these different perspectives, and explore methods that adjust for sex and age differences in item functioning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Figure 1.

Causal models of construct equivalence. These include: (1a) equivalence at the measure level; (1b) equivalence at the item level; (1c) equivalence at the item level after accounting for measure-specific effects (bifactor model).

Figure 2.

Predicted regression lines illustrating associations between youth report common factor scores from the single common factor model and the configural model (based on bifactor model with four specific measure factors). Lines cover the range of −/+ 2 SD on single factor model scores.