期刊
MULTIVARIATE BEHAVIORAL RESEARCH
卷 47, 期 5, 页码 667-696出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/00273171.2012.715555
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资金
- NIH Roadmap for Medical Research [AR052177]
- Consortium for Neuropsychiatric Phenomics, NIH Roadmap for Medical Research [UL1-DE019580, RL1DA024853]
- NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [U01AR052177] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DENTAL &CRANIOFACIAL RESEARCH [UL1DE019580] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON DRUG ABUSE [RL1DA024853] Funding Source: NIH RePORTER
Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling construct-relevant multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman, 1957) and highlight its relations with correlated-factors and second-order exploratory factor models. After describing limitations of the Schmid-Leiman, 2 newer methods of exploratory bifactor modeling are considered, namely, analytic bifactor (Jennrich & Bentler, 2011) and target bifactor rotations (Reise, Moore, & Maydeu-Olivares, 2011). Then I discuss limited- and full-information estimation approaches to confirmatory bifactor models that have emerged from the item response theory and factor analysis traditions, respectively. Comparison of the confirmatory bifactor model to alternative nested confirmatory models and establishing parameter invariance for the general factor also are discussed. Finally, important applications of bifactor models are reviewed. These applications demonstrate that bifactor modeling potentially provides a solid foundation for conceptualizing psychological constructs, constructing measures, and evaluating a measure's psychometric properties. However, some applications of the bifactor model may be limited due to its restrictive assumptions.
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