Identifying Useful Auxiliary Variables for Incomplete Data Analyses: A Note on a Group Difference Examination Approach

Autor/inn/en	Raykov, Tenko; Marcoulides, George A.
Titel	Identifying Useful Auxiliary Variables for Incomplete Data Analyses: A Note on a Group Difference Examination Approach
Quelle	In: Educational and Psychological Measurement, 74 (2014) 3, S.537-550 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164413511326
Schlagwörter	Data Analysis; Identification; Maximum Likelihood Statistics; Statistical Analysis; Computation; Models; Older Adults; Intelligence Tests; Raven Advanced Progressive Matrices + Suchen Sie Ihr Suchwort? Auswertung; Identifikation; Identifizierung; Statistische Analyse; Analogiemodell; Älterer Erwachsener; Intelligence test; Intelligenztest
Abstract	This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably the plausibility of the underlying assumption of data missing at random. The auxiliary variables can also be considered for inclusion alternatively in imputation models for following multiple imputation analyses. The approach can be particularly helpful in empirical settings where violations of missing at random are suspected, and is illustrated with data from an aging research study. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10