Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data

Autor/inn/en	Xu, Shu; Blozis, Shelley A.
Titel	Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data
Quelle	In: Journal of Educational and Behavioral Statistics, 36 (2011) 2, S.237-256 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998610375836
Schlagwörter	Data Analysis; Longitudinal Studies; Models + Suchen Sie Ihr Suchwort? Auswertung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Analogiemodell
Abstract	Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR), may be handled. While the mechanism giving rise to the missing data cannot be determined by the observations, the sensitivity of parameter estimates to missing data assumptions can be studied, for example, by fitting multiple models that make different assumptions about the missing data process. Sensitivity analysis of a mixed model that may include nonlinear parameters when some data are missing is discussed. An example is provided. (Contains 3 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10