Fullerton / Xu Ordered Regression Models

Parallel, Partial, and Non-Parallel Alternatives
Erscheinungsjahr 2016
ISBN: 978-1-4665-6974-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 188 Seiten

Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Ordered regression models differ from nominal outcome models in that the category order is meaningful. Thus, changing the order of the categories affects the results. This book provides in-depth coverage on a wide range of ordered regression models, including several models that are quite useful yet not included in other books on ordered models. It discusses multiple estimation approaches, including person-level and person/threshold-level estimators. The book assumes a basic background in OLS and binary regression models.

Autoren/Hrsg.

Fullerton, Andrew S.

Xu, Jun

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Introduction. Parallel Models. Partial Models. Non-Parallel Models. Testing the Parallel Regression Assumption. Extensions. Online Supplement.

Über Autor(innen)

Andrew S. Fullerton, PhD, is Associate Professor of Sociology at Oklahoma State University. His primary research interests include work and occupations, social stratification, and quantitative methods. His work has been published in journals, such as Social Forces, Social Problems, Sociological Methods & Research, Public Opinion Quarterly, and Social Science Research.

Jun Xu, PhD, is Associate Professor of Sociology at Ball State University. His primary research interests include Asia and Asian America, social epidemiology, and statistical modeling and programming. His work has been published in journals, such as Social Forces, Social Science & Medicine, Sociological Methods & Research, Social Science Research, and The Stata Journal.