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Book Description
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.
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.
Inside This Book
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First Sentence
In linear regression analysis, it is possible to test whether two variables are linearly related and to calculate the strength of the linear relationship if the relationship between the variables can be described by an equation of the form Y = a + BX, where Y is the variable being predicted (the dependent, criterion, outcome, or endogenous variable), X is a variable whose values are being used to predict Y (the independent, exogenous, or predictor variable),1 and a and B are population parameters to be estimated. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Back Cover
In linear regression analysis, it is possible to test whether two variables are linearly related and to calculate the strength of the linear relationship if the relationship between the variables can be described by an equation of the form Y = a + BX, where Y is the variable being predicted (the dependent, criterion, outcome, or endogenous variable), X is a variable whose values are being used to predict Y (the independent, exogenous, or predictor variable),1 and a and B are population parameters to be estimated. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Back Cover
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4.5 out of 5 stars (6 customer reviews)
39 of 39 people found the following review helpful
5.0 out of 5 stars
Excellent Guide to Logistic Regression, April 11 2000
By Scott Gallagher - Published on Amazon.com
This review is from: Applied Logistic Regression Analysis (Paperback)
As its title suggests, this book is an excellent guide to using logistic regression in data analysis. I purchased this book because I needed to do several logistic regression runs for my dissertation. It turned out to be an extremely useful book for two reasons. First, it presents logistic regression alongside more traditional ordinary least squares (OLS) models. Therefore, if you already have a good understanding of OLS models, this book is very easy to follow. Second, its discussion of logistic regression issues in the context of SPSS or SAS makes it very easy to follow along with your own data analysis as you move through the book. Since statistical packages are always improving, this does date the book a little. However, this is a very minor concern. I believe Dr. Menard is to be commended for including issues regarding popular software packages in this work.When compared to SAS's documentation, this book's greatest advantage is explaining in english (rather than mathematical notation) the assumptions and limitations of SAS's (and SPSS'S) algorithms. Its chapter on logistic regression diagnostics is alone worth the price of the book. In short, if you need to use logistic regression analysis and you already understand OLS, you cannot go wrong with this book.
9 of 9 people found the following review helpful
5.0 out of 5 stars
Very understandable and a bargain, Aug 21 2005
By Maria - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Applied Logistic Regression Analysis (Paperback)
I bought this book to teach myself logistic regression after buying a much much more expensive text . If you've had the experience of trying to learn a stats technique on your own then you know that you'll probably need more than one book. If I could go back, I would buy this one first and then move on to other more expensive and comprehensive texts. I had a good grasp of multiple regression already and found this book's orientation to logistic regression, done by drawing parallels with multiple regression, very understandable. It was easy to read cover to cover and gave great explanations of the background math, without being at all heavy with formulas. If you are taking a logistic regression course and are having a hard time following the explinations in the text assigned for the class, this would likely provide a good alternative for helping you grasp the concepts.
9 of 11 people found the following review helpful
3.0 out of 5 stars
A Nice Overview, Oct 14 2000
By David C. Frye - Published on Amazon.com
Amazon Verified Purchase(What's this?)
This review is from: Applied Logistic Regression Analysis (Paperback)
A good, cheap overview of logistic regression analysis.I bought and I'm glad I did, but I don't refer to it like I do Hosmer and Lemeshow's text.
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