NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ970834
Record Type: Journal
Publication Date: 2012
Pages: 19
Abstractor: ERIC
Reference Count: 24
ISSN: ISSN-0271-0579
Multilevel Models for Binary Data
Powers, Daniel A.
New Directions for Institutional Research, n154 p57-75 Sum 2012
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.)
Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A