Categorical Data Analysis With Sas and Spss ApplicationsThis book covers the fundamental aspects of categorical data analysis with an emphasis on how to implement the models used in the book using SAS and SPSS. This is accomplished through the frequent use of examples, with relevant codes and instructions, that are closely related to the problems in the text. Concepts are explained in detail so that students can reproduce similar results on their own. Beginning with chapter two, exercises at the end of each chapter further strengthen students' understanding of the concepts by requiring them to apply some of the ideas expressed in the text in a more advanced capacity. Most of these exercises require intensive use of PC-based statistical software. Numerous tables with results of analyses, including interpretations of the results, further strengthen students' understanding of the material. Categorical Data Analysis With SAS(R) and SPSS Applications features: *detailed programs and outputs of all examples illustrated in the book using SAS(R) 8.02 and SPSS on the book's CD; *detailed coverage of topics often ignored in other books, such as one-way classification (ch. 3), the analysis of doubly classified data (ch. 11), and generalized estimating equations (ch. 12); and *coverage of SAS(R) PROC FREQ, GENMOD, LOGISTIC, PROBIT, and CATMOD, as well as SPSS PROC CROSSTABS, GENLOG, LOGLINEAR, PROBIT, LOGISTIC, NUMREG, and PLUM. This book is ideal for upper-level undergraduate or graduate-level courses on categorical data analysis taught in departments of biostatistics, statistics, epidemiology, psychology, sociology, political science, and education. A prerequisite of one year of calculus and statistics is recommended. The book has been class tested by graduate students in the department of biometry and epidemiology at the Medical University of South Carolina. |
Contents
Preface | |
introduces readers to the various types of variables commonly | |
Probability Models | |
OneWay Classification | |
Models for 22 Contingency Tables | |
The General IxJ Contingency Table | |
LogLinear Models for Contingency Tables | |
Strategies for LogLinear Model Selection | |
discusses the oneway classification exact and large sample tests | |
Logit and Multinomial Response Models | |
Models in Ordinal Contingency Tables | |
Analysis of Doubly Classified Data | |
Analysis of Repeated Measures Data | |
Bibliography | |
Models for Binary Responses | |
Common terms and phrases
approximation association model asymptotic binomial distribution cancer chapter Chi-Square ChiSq Clogg column computed consider constraints contingency table correlation structures corresponding covariates data in Table datalines defined degrees of freedom discussed drug effect equivalent example expected values factor variable fits the data function gender given gives Hence homogeneity hypergeometric distribution implemented in SAS indicates interaction term Lawal link=log log odds ratios log-linear model logistic regression logit model marginal totals matrix Maximum Likelihood Estimates model fits model of independence multinomial multinomial distribution null hypothesis observed frequencies obtained odds ratio order=data ordinal parameter estimates Poisson distribution probability PROC CATMOD proc genmod PROC LOGISTIC proc print procedure pvalue regression respectively response variable sampling scheme SAS software output SAS software program saturated model scores significant Similarly standard errors subtable symmetry model test statistic type3 variance vector weight count zero