Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives

ISBN-13: 9780367737214 / Angielski / Miękka / 2020 / 172 str.

Andrew S. Fullerton; Jun Xu

Opis

This book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. It explores the advantages of ordered regression models over linear and binary regression models for the analysis of ordinal outc

Spis treści

Introduction. Parallel Models. Partial Models. Nonparallel Models. Testing the Parallel Regression Assumption. Extensions. References. Index.

O autorze

Andrew S. Fullerton is an 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 is an associate professor of sociology at Ball State University. His primary research interests include Asia and Asian Americans, social epidemiology, and statistical modeling and programing. His work has been published in journals such as Social Forces, Social Science & Medicine, Sociological Methods & Research, Social Science Research, and The Stata Journal.