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Tang W., He H., Tu X. Applied Categorical and Count Data Analysis
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Boca Raton: CRC Press, 2012. — 363 p.This book focuses on statistical analysis of discrete data, including categorical and count outcomes. Discrete variables are abundant in practice, and knowledge about and ability to analyze such data is important for professionals and practitioners in a wide range of biomedical and psychosocial research areas.
Although there are some excellent books on this general subject such as those by Agresti (2002, 2007), Long (1997), Long and Freese (2006), and Stokes et al. (2009), a book that includes models for longitudinal data, real data examples with detailed programming codes, as well as intuitive explanations of the models and their interpretations and differences thereupon will complement the repertoire of existing texts. Motivated by the lack of such a text, we decided to write this book five years ago when preparing a graduate-level biostatistics course on this topic for students within a medical school setting at the University of Rochester. The lecture notes from which this book has evolved have been used for the course over the past five years.
In addition to the classic concepts such as contingency tables and popular topics such as logistic and Poisson regression models, as covered by most available textbooks on categorical data analysis, this book also includes many modern topics. These include models for zero modified count outcomes, longitudinal data analysis (both parametric and semiparametric), reliability analysis, and popular methods for dealing with missing values. More importantly, programming codes are provided for all the examples in the book for the four major software packages, R, SAS, SPSS, and Stata, so that when reading the examples readers can immediately put their knowledge into practice by trying
out the codes with the data in the examples using the statistical packages of their choice, and/or adapt and even extend them to fit settings arising from their own studies.NB: missing pages iii - xix (in particular, table of contents), 30 - 38, 236 -238, 255 - 258, 287 - 298, 309 - 318, 357 - 359
Although there are some excellent books on this general subject such as those by Agresti (2002, 2007), Long (1997), Long and Freese (2006), and Stokes et al. (2009), a book that includes models for longitudinal data, real data examples with detailed programming codes, as well as intuitive explanations of the models and their interpretations and differences thereupon will complement the repertoire of existing texts. Motivated by the lack of such a text, we decided to write this book five years ago when preparing a graduate-level biostatistics course on this topic for students within a medical school setting at the University of Rochester. The lecture notes from which this book has evolved have been used for the course over the past five years.
In addition to the classic concepts such as contingency tables and popular topics such as logistic and Poisson regression models, as covered by most available textbooks on categorical data analysis, this book also includes many modern topics. These include models for zero modified count outcomes, longitudinal data analysis (both parametric and semiparametric), reliability analysis, and popular methods for dealing with missing values. More importantly, programming codes are provided for all the examples in the book for the four major software packages, R, SAS, SPSS, and Stata, so that when reading the examples readers can immediately put their knowledge into practice by trying
out the codes with the data in the examples using the statistical packages of their choice, and/or adapt and even extend them to fit settings arising from their own studies.NB: missing pages iii - xix (in particular, table of contents), 30 - 38, 236 -238, 255 - 258, 287 - 298, 309 - 318, 357 - 359
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