Multilevel Models for Binary Data

Autor/in	Powers, Daniel A.
Titel	Multilevel Models for Binary Data
Quelle	In: New Directions for Institutional Research, (2012) 154, S.57-75 (19 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0271-0579
DOI	10.1002/ir.20014
Schlagwörter	Institutional Research; Educational Research; Data Analysis; Research Methodology; Prediction; Models; Probability; Multivariate Analysis; Graduate Students; Student Placement; Biochemistry; Dropouts; Error Patterns; Student Characteristics + Suchen Sie Ihr Suchwort? Institutionelle Forschung; Bildungsforschung; Pädagogische Forschung; Auswertung; Research method; Forschungsmethode; Vorhersage; Analogiemodell; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Multivariate Analyse; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Schülerpraktikum; Biochemie; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Fehlertyp
Abstract	The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are perhaps the most widely applied models in categorical data analysis and may be the most relevant for institutional research where predictions such as program participation, graduation, or dropout are particularly relevant. These particular categorical models are also the most fully developed in the literature on multilevel models. In this article, the author provides a brief overview of two of the most important models for categorical data analysis to show how these models are adapted to the multilevel or mixed modeling framework using the generalized linear mixed model. He then examines a simple model of program placement from both the conventional modeling and then multilevel perspectives. Finally, he considers a more ambitious multilevel analysis of program dropout. (Contains 6 figures and 3 tables.) (ERIC).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10