Journal article
Bo Zhang, Tianjun Sun, Mengyang Cao, F. Drasgow
2020
2020
Semantic Scholar
DOI
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APA
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Zhang, B., Sun, T., Cao, M., & Drasgow, F. (2020). Using Bifactor Models to Examine the Predictive Validity of Hierarchical Constructs: Pros, Cons, and Solutions.
Chicago/Turabian
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Zhang, Bo, Tianjun Sun, Mengyang Cao, and F. Drasgow. “Using Bifactor Models to Examine the Predictive Validity of Hierarchical Constructs: Pros, Cons, and Solutions” (2020).
MLA
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Zhang, Bo, et al. Using Bifactor Models to Examine the Predictive Validity of Hierarchical Constructs: Pros, Cons, and Solutions. 2020.
BibTeX Click to copy
@article{bo2020a,
title = {Using Bifactor Models to Examine the Predictive Validity of Hierarchical Constructs: Pros, Cons, and Solutions},
year = {2020},
author = {Zhang, Bo and Sun, Tianjun and Cao, Mengyang and Drasgow, F.}
}
Abstract
The use of bifactor models has increased substantially in the past decade. However, bifactor models are prone to a nonidentification problem in the context of prediction that is not well recognized in the general research community. Moreover, the practical consequences of adopting different conceptualizations of hierarchical constructs when examining their predictive validity has received little attention. Therefore, Study 1 examined the statistical performance of bifactor models and investigated the effectiveness of an augmentation strategy to remedy the nonidentification problem. Monte Carlo simulations showed that the augmentation strategy is effective. The second simulation study demonstrated that researchers may arrive at different conclusions regarding the predictive validity of hierarchical constructs depending on their choice of models. In general, augmented bifactor models, which are restricted variants of the more general bifactor-(S·I-1) model, reasonably recovered the overall predictive validity (R 2) of hierarchical constructs and led to correct substantive conclusions regarding the incremental validity of facets regardless of the true data-generation model given a sufficiently large sample (n ≥ 600). The authors discussed implications of those findings and made practical recommendations for further users of bifactor models.