A Simulation Study of Missing Data with Multiple Missing X's

Autor/inn/en	Rubright, Jonathan D.; Nandakumar, Ratna; Glutting, Joseph J.
Titel	A Simulation Study of Missing Data with Multiple Missing X's
Quelle	In: Practical Assessment, Research & Evaluation, 19 (2014) 10, (8 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1531-7714
Schlagwörter	Simulation; Data; Comparative Analysis; Predictor Variables; Bias; Multiple Regression Analysis; Adjustment Scales for Children and Adolescents + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Daten; Prädiktor
Abstract	When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type of missingness (MCAR, MAR), degree of missingness (10%, 25%, and 50%), and where missingness occurs (one predictor, two predictors, or two predictors with overlap). Using a real dataset, cells are systematically deleted to create various scenarios of missingness so that parameter estimates from listwise deletion and multiple imputation may be compared to the "true" estimates from the full dataset. Results suggest the multiple imputation works well, even when the imputation model itself is missing data. (As Provided).
Anmerkungen	Dr. Lawrence M. Rudner. e-mail: editor@pareonline.net; Web site: http://pareonline.net
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10