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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.
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