Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)
By admin • Jan 3rd, 2009 • Category: Economics •
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Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)
by Scott William Menard
Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)
By Scott William Menard
Publisher: Sage Publications, Inc
Number Of Pages: 104
Publication Date: 1995-06-29
ISBN-10 / ASIN: 0803957572
ISBN-13 / EAN: 9780803957572
Binding: Paperback
“Here is another, number 106, of the concise, practical, and economical little paperbacks in the publisher’’s Quantitative Applications in the Social Sciences Series. . . . this is one of the best. . . . the author systematically presents each aspect of MLR practice, then introduces the corresponding aspect of logistic regression analysis (LRA) by the method of comparison and contrast. It is a very effective style. . . . The book is well written and interesting to read in its entirety. Concepts are clearly presented, logically motivated, and effectively illustrated. . . . Any statistician would be proud to have written this book.” –Technometrics Aimed at the student or professional whose statistical background may not extend beyond ordinary least squares multiple regression analysis, Brown introduces logistic regression as an extension of linear regression and emphasizes the parallels between the two. Unique features of the book include detailed consideration of goodness of fit and indices of predictive efficiency, standardized logistic regression coefficients, and the use of existing SPSS and SAS software for loglinear and logit models to calculate logistic regression models for polytomous nominal and ordinal dependent variables. With an emphasis on application of computer statistical packages rather than theorems and proofs, Brown offers a nontechnical and applied introduction to this useful topic.
Summary: Very understandable and a bargain
Rating: 5
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.
Summary: Excellent Over view
Rating: 5
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!
Summary: A Nice Overview
Rating: 3
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.
Summary: Excellent Guide to Logistic Regression
Rating: 5
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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