A Small Sample Correction for Factor Score Regression

Autor/inn/en	Bogaert, Jasper; Loh, Wen Wei; Rosseel, Yves
Titel	A Small Sample Correction for Factor Score Regression
Quelle	In: Educational and Psychological Measurement, 83 (2023) 3, S.495-519 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bogaert, Jasper)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221105505
Schlagwörter	Factor Analysis; Regression (Statistics); Structural Equation Models; Error of Measurement; Sample Size; Error Correction; Measurement Techniques; Robustness (Statistics); Prediction; Statistical Bias; Scores; Maximum Likelihood Statistics; Simulation + Suchen Sie Ihr Suchwort? Faktorenanalyse; Regression; Regressionsanalyse; Messfehler; Korrektur; Messtechnik; Widerstandsfähigkeit; Vorhersage; Simulation program; Simulationsprogramm
Abstract	Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01