On Modeling Missing Data in Structural Investigations Based on Tetrachoric Correlations with Free and Fixed Factor Loadings

Autor/inn/en	Schweizer, Karl; Gold, Andreas; Krampen, Dorothea
Titel	On Modeling Missing Data in Structural Investigations Based on Tetrachoric Correlations with Free and Fixed Factor Loadings
Quelle	In: Educational and Psychological Measurement, 83 (2023) 6, S.1113-1138 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Schweizer, Karl) ORCID (Krampen, Dorothea)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221143145
Schlagwörter	Data; Models; Factor Analysis; Correlation; Goodness of Fit; Data Analysis + Suchen Sie Ihr Suchwort? Daten; Analogiemodell; Faktorenanalyse; Korrelation; Auswertung
Abstract	In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of switching between models with free and fixed factor loadings. In a simulation study, confirmatory factor analysis (CFA) models with and without a missing data latent variable were used for investigating the structure of data with and without missing data. In addition, the numbers of columns of data sets with missing data and the amount of missing data were varied. The root mean square error of approximation (RMSEA) results revealed that an additional missing data latent variable recovered the degree-of-model fit characterizing complete data when tetrachoric correlations served as input while comparative fit index (CFI) results showed overestimation of this degree-of-model fit. Whereas the results for fixed factor loadings were in line with the assumptions of modeling missing data, the other results showed only partial agreement. Therefore, modeling missing data with fixed factor loadings is recommended. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01