Should I allow my confirmatory factors to correlate during factor score extraction? Implications for the applied researcher

Jessica A. R. Logan (), Hui Jiang, Nathan Helsabeck and Gloria Yeomans-Maldonado
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Jessica A. R. Logan: The Ohio State University
Hui Jiang: The Ohio State University
Nathan Helsabeck: The Ohio State University
Gloria Yeomans-Maldonado: The Ohio State University

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 4, No 12, 2107-2131

Abstract: Abstract With complex models becoming increasingly popular in the social sciences, many researchers have begun using latent variable modeling in multiple-steps, saving, estimating, or otherwise extracting factor scores from one confirmatory factor analysis (CFA) for use in a second inferential analysis. With two or more factors identified in a CFA, there exist few practical guidelines as to how researchers should proceed. In Study 1, we examine two common practices when CFAs have two or more factors: Fitting separate CFAs or allowing them to correlate in the model used for extraction. We provide a simulation study to demonstrate the bias introduced in each of the two approaches. In Study 2, we demonstrate that the between-factor correlation bias can be mitigated through the use of a different estimator; using ten Berge estimation shows near zero bias on the critical correlations between factors. Finally, we demonstrate this with an example dataset.

Keywords: Confirmatory factor analysis; Factor extraction; Factor score regression; CFA (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11135-021-01202-x

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