#3210. Small but Nontrivial: A Comparison of Six Strategies to Handle Cross-Loadings in Bifactor Predictive Models

September 2026publication date
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Journal’s subject area:
Statistics and Probability;
Arts and Humanities (miscellaneous);
Experimental and Cognitive Psychology;
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Abstract:
The bifactor model is a promising alternative to traditional modeling techniques for studying the predictive validity of hierarchical constructs. The research presents a systematic examination of the statistical performance of six modeling strategies to handle cross-loadings in bifactor predictive models. Results revealed four clear patterns: 1) forcing even small cross-loadings to zero was detrimental to empirical identification, estimation bias, power and Type I error rates; 2) the performance of ESEM with target rotation was unexpectedly weak; 3) augmented BSEM had satisfactory performance in an absolute sense; 4) augmentation improved the performance of ESEM and SEM. These findings can help users of bifactor predictive models design better studies, choose more appropriate analytical strategies, and obtain more reliable results.
Keywords:
augmentation; Bifactor predictive model; BSEM; cross-loadings; ESEM

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