Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences) (v. 106) | 
| Author: Scott William Menard
Publisher: Sage Publications, Inc
Category: Book
List Price: $16.95
Buy New: $14.45
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Avg. Customer Rating:  4 reviews
Sales Rank: 37012
Media: Paperback
Edition: 2nd
Number Of Items: 1
Pages: 128
Shipping Weight (lbs): 0.2
Dimensions (in): 8.2 x 5.4 x 0.3
ISBN: 0761922083
Dewey Decimal Number: 519.536
EAN: 9780761922087
ASIN: 0761922083
Publication Date: October 2001
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Product 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.
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| Customer Reviews:
 Very understandable and a bargain August 21, 2005
4 out of 4 found this review helpful
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.
Excellent Over view November 15, 2001
5 out of 6 found this review helpful
Prof Scott Menard must be commended for writing an excellent book on Logistic Regression. Explaining it in the context of commercially available software packages is a very good idea. I was able replicate some his analysis using SAS on the data set used in this book (available on line from ICPSR, Univ of Michigan).I eagerly await the next edition of this monograph. Thank you!
 A Nice Overview October 15, 2000
8 out of 10 found this review helpful
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.
Excellent Guide to Logistic Regression April 11, 2000
35 out of 35 found this review helpful
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.
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