Analysis of Interactions and Nonlinear Effects with Missing Data. A Factored Regression Modeling Approach Using Maximum Likelihood Estimation.

Autor/inn/en	Lüdtke, Oliver; Robitzsch, Alexander; West, Stephen G.
Titel	Analysis of Interactions and Nonlinear Effects with Missing Data. A Factored Regression Modeling Approach Using Maximum Likelihood Estimation.
Quelle	In: Multivariate behavioral research, 55 (2020) 3, S. 361-381 PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	0027-3171; 1532-7906
DOI	10.1080/00273171.2019.1640104
Schlagwörter	Datenerhebung; Statistische Auswertung; Validität; Fehlerbewertung; Fehlertoleranz; Datenanalyse; Datenauswertung; Regressionsanalyse; Prädiktor; Messfehler; Modellierung; Statistische Methode
Abstract	When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x2) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/1