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The role of the bifactor model in resolving dimensionality issues in health outcomes measures

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Abstract

Objectives

We propose the application of a bifactor model for exploring the dimensional structure of an item response matrix, and for handling multidimensionality.

Background

We argue that a bifactor analysis can complement traditional dimensionality investigations by: (a) providing an evaluation of the distortion that may occur when unidimensional models are fit to multidimensional data, (b) allowing researchers to examine the utility of forming subscales, and, (c) providing an alternative to non-hierarchical multidimensional models for scaling individual differences.

Method

To demonstrate our arguments, we use responses (N =  1,000 Medicaid recipients) to 16 items in the Consumer Assessment of Healthcare Providers and Systems (CAHPS©2.0) survey.

Analyses

Exploratory and confirmatory factor analytic and item response theory models (unidimensional, multidimensional, and bifactor) were estimated.

Results

CAHPS© items are consistent with both unidimensional and multidimensional solutions. However, the bifactor model revealed that the overwhelming majority of common variance was due to a general factor. After controlling for the general factor, subscales provided little measurement precision.

Conclusion

The bifactor model provides a valuable tool for exploring dimensionality related questions. In the Discussion, we describe contexts where a bifactor analysis is most productively used, and we contrast bifactor with multidimensional IRT models (MIRT). We also describe implications of bifactor models for IRT applications, and raise some limitations.

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Acknowledgments

This paper was supported by grant number 5 U18 HS-00924 from the Agency for Healthcare Research and Quality. Dr. Hays was also supported in part by the UCLA/DREW Project EXPORT, National Institutes of Health, National Center on Minority Health & Health Disparities, (P20-MD00148-01) and the UCLA Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging Research, National Institutes of Health, National Institute of Aging, (AG-02-004).

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Authors and Affiliations

  1. Department of Psychology, University of California, Franz Hall, Los Angeles, CA, 90095-1563, USA

    Steven P. Reise, Julien Morizot & Ron D. Hays

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  1. Steven P. Reise
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Correspondence to Steven P. Reise.

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Reise, S.P., Morizot, J. & Hays, R.D. The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Qual Life Res 16 (Suppl 1), 19–31 (2007). https://doi.org/10.1007/s11136-007-9183-7

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