Applications of regression for categorical outcomes using R/ Melamed, David & Doan, Long

Includes bibliography and index

Introducton -- Introduction to R studio and packages -- Overview of OLS regression and introduction to the generalized linear model -- describing categorical variables and some useful tests of association -- Regression for binary outcomes -- regression for binary outcomes- moderation and squared terms -- Regression for ordinal outcomes -- Regression for nominal outcomes -- Regression for count outcomes -- Additional outcomes types -- Special topics: comparing between models and missing data

This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide.