Along with providing a useful discussion of categorical data analysis techniques, this book shows how to apply these methods with the SAS System. The authors include practical examples from a broad range of applications to illustrate the use of the FREQ, LOGISTIC, GENMOD, and CATMOD procedures in a variety of analyses. They also discuss other procedures such as PHREG and NPAR1WAY.
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About the Author
Maura E. Stokes is Senior Manager of Statistical Applications Research and Development at SAS Institute. She received her PhD in Biostatistics from the University of North Carolina at Chapel Hill and has taught and written about categorical data analysis for over fifteen years.
Charles S. Davis is Professor of Biostatistics at the University of Iowa. He received his PhD in Biostatistics from the University of Michigan. His research and teaching interests include categorical data analysis and methods for the analysis of repeated measures.
Gary Koch is Professor of Biostatistics and Director of the biometrics Consulting Laboratory at the University of North Carolina at Chapel Hill. He has had a prominent roll in the field of categorical data analysis for the last thirty years. He teaches classes and seminars in categorical data analysis, consults in areas of statistical practice, conducts research, and trains many Biostatistics students.
Charles S. Davis is Professor of Biostatistics at the University of Iowa. He received his PhD in Biostatistics from the University of Michigan. His research and teaching interests include categorical data analysis and methods for the analysis of repeated measures.
Gary Koch is Professor of Biostatistics and Director of the biometrics Consulting Laboratory at the University of North Carolina at Chapel Hill. He has had a prominent roll in the field of categorical data analysis for the last thirty years. He teaches classes and seminars in categorical data analysis, consults in areas of statistical practice, conducts research, and trains many Biostatistics students.
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Most Helpful Customer Reviews
26 of 27 people found the following review helpful:
5.0 out of 5 stars
Excellent, practical guide for data analysis,
By A Customer
This review is from: Categorical Data Analysis Using the SAS System (Paperback)
I've used the first edition of the book for my own data analysis more than once. It is an invaluable tool for translating statistical theory into practical knowledge. I often read statistical texts, think I understand them, but have no idea how to use that knowledge with a statistical package. This book takes the reader step by step through the theory, with each chapter adding a logical next layer of complexity. All along are SAS examples interspersed with theory so writing programs and interpreting output are greatly simplified. It may be necessary to read the entire book if your data is relatively complicated and the appropriate analysis is covered towards the end of the book, but the effort is well worth it. I have never so well understood the rationale for the statistical tests I've done. I have had a class in SAS, so this book may be too difficult for someone totally unfamiliar with SAS, but I am by no means an expert and have been able to write programs to do everything I wanted. I used this book in conjunction with the SAS/STAT user's guide and found both together totally adequate for my needs.
12 of 12 people found the following review helpful:
5.0 out of 5 stars
Essential reference for SAS users,
By A Customer
This review is from: Categorical Data Analysis Using the SAS System (Paperback)
Almost all data analysis projects involve categorical data, and this text is an excellent reference. More than just a how-to manual for using SAS to do the analysis, it provides excellent practical advice from model selection to interpretation of results. Every example is complete with code, output, and thorough discussion of the research questions, conclusions, and interpretations. For several examples, more than one approach is illustrated. As a statistical consultant, this has been the book I used most often for reference with discrete data analysis. While it is not a replacement for a graduate text such as Agresti, it is an excellent resource for an applied statistician.
7 of 7 people found the following review helpful:
5.0 out of 5 stars
Gets at the heart of statistical analysis,
By
This review is from: Categorical Data Analysis Using the SAS System (Paperback)
This book gets at the fundamentals of statistical analysis that are lacking in many other volumes. It starts off with analyzing the smallest units within an experimental design and works up from there. A great practitioner's reference--even for the non-SAS user.
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Inside This Book
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First Sentence:
Data analysts often encounter response measures that are categorical in nature; Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
h, knowledg news, residual score statistic, mean score statistic, placebo fair, race agegroup, row mean scores, modified ridit scores, cmh nocol nopct, gre none, low yes non, lot placebo, genmod descending, underlying contingency table, hierarchical loglinear models, exact conditional logistic regression, multiple response functions, uncomplicated diagnosis, proc catmod, general association statistic, full rank parameterization, generalized logits model, class diagnosis treatment, freq count, times higher odds Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Prob, Nonzero Correlation, Row Mean Scores Differ, Cochran-Mantel-Haenszel Statistics, Statistic Alternative Hypothesis, Mantel-Haenszel Chi-Square, Row Pct, Contingency Coefficient, Phi Coefficient, Analysis Chi-Source, Modeling Repeated Measurements Data, Test Chi-Square, Value Value, Response Profiles Response, Confidence Parameter Estimate Error Limits, Continuity Adj, Quantal Bioassay Analysis, Table Probability, Col Pct, Correlation Matrix Dimension, Minimum Cluster Size, Lambda Asymmetric, Table Analysis of Variance Source, Wald Effect Estimate Confidence Limits, Analysis Score Statistics For Type New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Data analysts often encounter response measures that are categorical in nature; Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
h, knowledg news, residual score statistic, mean score statistic, placebo fair, race agegroup, row mean scores, modified ridit scores, cmh nocol nopct, gre none, low yes non, lot placebo, genmod descending, underlying contingency table, hierarchical loglinear models, exact conditional logistic regression, multiple response functions, uncomplicated diagnosis, proc catmod, general association statistic, full rank parameterization, generalized logits model, class diagnosis treatment, freq count, times higher odds Key Phrases - Capitalized Phrases (CAPs): (learn more)
Value Prob, Nonzero Correlation, Row Mean Scores Differ, Cochran-Mantel-Haenszel Statistics, Statistic Alternative Hypothesis, Mantel-Haenszel Chi-Square, Row Pct, Contingency Coefficient, Phi Coefficient, Analysis Chi-Source, Modeling Repeated Measurements Data, Test Chi-Square, Value Value, Response Profiles Response, Confidence Parameter Estimate Error Limits, Continuity Adj, Quantal Bioassay Analysis, Table Probability, Col Pct, Correlation Matrix Dimension, Minimum Cluster Size, Lambda Asymmetric, Table Analysis of Variance Source, Wald Effect Estimate Confidence Limits, Analysis Score Statistics For Type New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
See all 100 books this book cites
- Applied logistic regression (Wiley Series in probability and statistics) by David W. Hosmer in Back Matter (1), Back Matter (2), and Back Matter (3)
- Applied Regression Analysis (Wiley Series in Probability and Statistics) by Norman R. Draper in Back Matter (1), and Back Matter (2)
- Categorical Data Analysis (Wiley Series in Probability and Statistics) by Alan Agresti in Back Matter (1), and Back Matter (2)
- Design and Analysis of Clinical Experiments (Wiley Classics Library) by Joseph L. Fleiss in Back Matter (1), and Back Matter (2)
- Alternative Methods of Regression (Wiley Series in Probability and Statistics) by David Birkes in Back Matter (1), and Back Matter (2)
See all 100 books this book cites
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