Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences) (2ND)

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Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences) (2ND)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 111 p.
  • 言語 ENG
  • 商品コード 9780761922087
  • DDC分類 519.536

Full Description


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