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Categorical Data Analysis Using the SAS System [Paperback]

Maura E. Stokes (Author), Charles S. Davis (Contributor), Gary G. Koch (Contributor)
4.6 out of 5 stars  See all reviews (14 customer reviews)

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Book Description

August 18, 2009 1580257100 978-1580257107 2
Statisticians and researchers will find Maura Stokes, Charles Davis, and Gary Koch's Categorical Data Analysis Using the SAS System, Second Edition, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, and CATMOD procedures in a variety of analyses. Other procedures discussed include the PHREG and NPAR1WAY procedures. Topics discussed include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic regression, weighted least squares modeling, repeated measurements analyses, log-linear models, and bioassay analysis. The second edition has been revised for use with SAS 8. New topics include additional exact tests, generalized estimating equations, use of the CLASS statement in the LOGISTIC procedure, exact logistic regression using the LOGISTIC procedure, and comparisons of the use of subject-specific models versus population-averaged models.

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Editorial Reviews

About the Author

Maura E. Stokes is Senior Manager of Statistical Applications Research and Development at SAS Institute. She received her DrPH 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 G. 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 role 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.


Product Details

  • Paperback: 648 pages
  • Publisher: SAS Publishing; 2 edition (August 18, 2009)
  • Language: English
  • ISBN-10: 1580257100
  • ISBN-13: 978-1580257107
  • Product Dimensions: 10.9 x 8.5 x 1.2 inches
  • Shipping Weight: 3.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #325,540 in Books (See Top 100 in Books)

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Customer Reviews

14 Reviews
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26 of 27 people found the following review helpful:
5.0 out of 5 stars Excellent, practical guide for data analysis, June 20, 2001
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.
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12 of 12 people found the following review helpful:
5.0 out of 5 stars Essential reference for SAS users, January 2, 2004
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.
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7 of 7 people found the following review helpful:
5.0 out of 5 stars Gets at the heart of statistical analysis, December 29, 2003
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David A. Andrae "dave_a" (Austin, TX United States) - See all my reviews
(REAL NAME)   
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 (learn more)
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
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