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Synopsis
The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included.
- More detailed consideration of grouped as opposed to case-wise data throughout the book
- Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency
- Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data
Updated coverage of unordered and ordered polytomous logistic regression models.
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About the Author
Scott Menard is a Professor of Criminal Justice at Sam Houston State University and a research associate in the Institute of Behavioral Science at the University of Colorado, Boulder. He received his A.B. at Cornell University and his Ph.D. at the University of Colorado, Boulder, both in Sociology. His interests include quantitative methods and statistics, life course criminology, substance abuse, and criminal victimization. His publications include Longitudinal Research (second edition Sage 2002), Applied Logistic Regression Analysis (second edition Sage 2002), Good Kids from Bad Neighborhoods (Cambridge University Press 2006, with Delbert S. Elliott, Bruce Rankin, Amanda Elliott, William Julius Wilson, and David Huizinga), Youth Gangs (Charles C. Thomas 2006, with Robert J. Franzese and Herbert C. Covey), and the Handbook of Longitudinal Research (Elsevier 2008), as well as other books and journal articles in the areas of criminology, delinquency, population studies, and statistics.
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- PublisherSAGE Publications, Inc
- Publication date2001
- ISBN 10 0761922083
- ISBN 13 9780761922087
- BindingPaperback
- LanguageEnglish
- Edition number2
- Number of pages128
- Rating
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Applied Logistic Regression Analysis
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Applied Logistic Regression Analysis
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