Posted on April 13, 2022 by childrenslearninginstitute
Published:
July 23, 2021 publish date. Issue date August 2022.
Publication:
CLI Author:
Gloria Yeomans-Maldonado, PhD
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.
Citation:
Logan, J.A.R., Jiang, H., Helsabeck, N. et al. (2022). Should I allow my confirmatory factors to correlate during factor score extraction? Implications for the applied researcher. Qual Quant 56, 2107–2131. https://doi.org/10.1007/s11135-021-01202-x
DOI:
http://doi.org/10.1007/s11135-021-01202-x
