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Applied Ordinal Logistic Regression Using Stata : From Single-Level to Multilevel Modeling (Paperback)
About this item
Liu presents a supplementary textbook for graduate quantitative methods courses on logistic regression models, ordinal regression models, categorical data analysis, or multi-level modeling in education or in social or behavioral sciences. He works the examples in the proprietary statistics package Stata. Among his topics are a review of basic statistics, logistic regression for binary data, partial proportional odds models and generalized ordinal logistic regression models, stereotype logistic regression models, multi-level modeling for continuous and binary response variables, and multi-level modeling for ordinal response variables. Annotation ©2016 Ringgold, Inc., Portland, OR (protoview.com)
Categorical data are abundant in applied research (e.g. gender, ethnicity, socioeconomic status, educational attainment). Students and researchers are increasingly interested in performing statistical analyses on categorical data, particularly ordinal categorical response variables. However, a lack of experience in advanced statistical methods and unfamiliarity with statistical software packages can make such a task daunting.Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling is intended to provide readers with advanced techniques of analyzing ordinal response variables using the statistical package Stata. Xing Liu presents a comprehensive coverage of modern ordinal regression techniques from proportional odds models to complex multi-level models in a systematic way. It will be the first book on this topic providing a unified framework for both single-level and multi-level modeling of ordinal categorical data in a single text. The book provides step-by-step instructions on how to conduct ordinal logistic regression analysis using Stata, how to interpret results from Stata output, and how to present the results in scholarly writing.
Number of Pages: 523
Genre: Social Science
Publisher: Sage Pubns
Author: Xing Liu
Street Date: November 11, 2015
Item Number (DPCI): 247-49-3839
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