9780761922087

Applied Logistic Regression Analysis

by
  • ISBN13:

    9780761922087

  • ISBN10:

    0761922083

  • Edition: 2nd
  • Format: Paperback
  • Copyright: 2001-10-09
  • Publisher: Sage Publications, Inc
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Supplemental Materials

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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

Summary

The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail and examples using SAS and SPSS included. New to this edition:· More detailed consideration of grouped as opposed to casewise data throughout the book· Updated discussion of the properties and appropriate use of goodness of fit measures, R2 analogues, and indices of predictive efficiency· Discussion of the misuse of odds ratios to represent risk ratios, and of overdispersion and underdispersion for grouped data· Updated coverage of unordered and ordered polytomous logistic regression models.

Table of Contents

Series Editor's Introduction v
Author's Introduction to the Second Edition vii
Linear Regression and the Logistic Regression Model
1(16)
Regression Assumptions
4(7)
Nonlinear Relationships and Variable Transformations
11(1)
Probabilities, Odds, Odds Ratios, and the Logit Transformation for Dichotomous Dependent Variables
12(2)
Logistic Regression: A First Look
14(3)
Summary Statistics for Evaluating the Logistic Regression Model
17(24)
R2, F, and Sums of squared Errors
18(2)
Goodness of Fit: GM, R2L, and the Log Likelihood
20(7)
Predictive Efficiency: λp, τp, φp, and the Binomial Test
27(9)
Examples: Assessing the Adequacy of Logistic Regression Models
36(5)
Conclusion: Summary Measures for Evaluating the Logistic Regression Model
41(1)
Interpreting the Logistic Regression Coefficients
41(26)
Statistical Significance in Logistic Regression Analysis
43(5)
Interpreting Unstandardized Logistic Regression Coefficients
48(3)
Substantive Significance and Standardized Coefficients
51(5)
Exponentiated Coefficients or Odds Ratios
56(1)
More on Categorical Predictors: Contrasts and Interpretation
57(4)
Interaction Effects
61(2)
Stepwise Logistic Regression
63(4)
An Introduction to Logistic Regression Diagnostics
67(24)
Specification Error
67(8)
Collinearity
75(3)
Numerical Problems: Zero Cells and Complete Separation
78(2)
Analysis of Residuals
80(9)
Overdispersion and Underdispersion
89(1)
A Suggested Protocol for Logistic Regression Diagnostics
90(1)
Polytomous Logistic Regression and Alternatives to Logistic Regression
91(12)
Polytomous Nominal Dependent Variables
94(3)
Polytomous or Multinomial Ordinal Dependent Variables
97(4)
Conclusion
101(2)
Notes 103(4)
Appendix: Probabilities 107(1)
References 108(3)
About the Author 111

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