Fitting Ordinal Factor Analysis Models with Missing Data: A Comparison between Pairwise Deletion and Multiple Imputation

Autor/inn/en	Shi, Dexin; Lee, Taehun; Fairchild, Amanda J.; Maydeu-Olivares, Alberto
Titel	Fitting Ordinal Factor Analysis Models with Missing Data: A Comparison between Pairwise Deletion and Multiple Imputation
Quelle	In: Educational and Psychological Measurement, 80 (2020) 1, S.41-66 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shi, Dexin) ORCID (Lee, Taehun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164419845039
Schlagwörter	Factor Analysis; Statistical Analysis; Computation; Goodness of Fit; Models; Sample Size; Data + Suchen Sie Ihr Suchwort? Faktorenanalyse; Statistische Analyse; Analogiemodell; Daten
Abstract	This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a wide range of conditions, including number of response categories, sample size, percent of missingness, and degree of model misfit. Results indicate that both PD and MI yield parameter estimates similar to those from analysis of complete data under conditions where the data are missing completely at random (MCAR). When the data are missing at random (MAR), PD parameter estimates are shown to be severely biased across parameter combinations in the study. When the percentage of missingness is less than 50%, MI yields parameter estimates that are similar to results from complete data. However, the fit indices (i.e., X[superscript 2], RMSEA, and WRMR) yield estimates that suggested a worse fit than results observed in complete data. We recommend that applied researchers use MI when fitting ordinal factor models with missing data. We further recommend interpreting model fit based on the TLI and CFI incremental fit indices. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01