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Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences Series)

AUTHOR: Scott W. Menard
ISBN: 0761922083

SHORT 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...

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         Editorial Review

Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences Series)
- Book Review,
by Scott W. Menard


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. 


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         Book Review

Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences Series)
- Book Reviews,
by Scott W. Menard

Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences Series)

ANNOTATION

Incl. linear regression & the logistic regression model/ interpreting the logistic regression coefficients/etc.

FROM THE PUBLISHER

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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