A Fully Conditional Specification Approach to Multilevel Imputation of Categorical and Continuous Variables

Autor/inn/en	Enders, Craig K.; Keller, Brian T.; Levy, Roy
Titel	A Fully Conditional Specification Approach to Multilevel Imputation of Categorical and Continuous Variables
Quelle	(2018), (73 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Hierarchical Linear Modeling; Behavioral Science Research; Computer Software; Bayesian Statistics; Computation; Data Analysis; Regression (Statistics); Statistical Analysis; Statistical Bias + Suchen Sie Ihr Suchwort? Auswertung; Regression; Regressionsanalyse; Statistische Analyse
Abstract	Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables, differential relations at level-1 and level-2, and incomplete level-2 variables. Given the limitations of existing imputation tools, the purpose of this manuscript is to describe a flexible imputation approach that can accommodate a diverse set of two-level analysis problems that includes any of the aforementioned features. The procedure employs a fully conditional specification (also known as chained equations) approach with a latent variable formulation for handling incomplete categorical variables. Computer simulations suggest that the proposed procedure works quite well, with trivial biases in most cases. We provide a software program that implements the imputation strategy, and we use an artificial data set to illustrate its use. [This paper was published in "Psychological Methods" v23 n2 p298-317 2018.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01