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.
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.
| Series Editor's Introduction |
|
v | |
| Author's Introduction to the Second Edition |
|
vii | |
|
Linear Regression and the Logistic Regression Model |
|
|
1 | (16) |
|
|
|
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) |
|
|
|
61 | (2) |
|
Stepwise Logistic Regression |
|
|
63 | (4) |
|
An Introduction to Logistic Regression Diagnostics |
|
|
67 | (24) |
|
|
|
67 | (8) |
|
|
|
75 | (3) |
|
Numerical Problems: Zero Cells and Complete Separation |
|
|
78 | (2) |
|
|
|
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) |
|
|
|
101 | (2) |
| Notes |
|
103 | (4) |
| Appendix: Probabilities |
|
107 | (1) |
| References |
|
108 | (3) |
| About the Author |
|
111 | |
Scott Menard is a Professor of Criminal Justice at Sam Houston State University and a research associate in the Institute of Behavioral Science at the University of Colorado, Boulder. He received his A.B. at Cornell University and his Ph.D. at the University of Colorado, Boulder, both in Sociology. His interests include quantitative methods and statistics, life course criminology, substance abuse, and criminal victimization. His publications include Longitudinal Research (second edition Sage 2002), Applied Logistic Regression Analysis (second edition Sage 2002), Good Kids from Bad Neighborhoods (Cambridge University Press 2006, with Delbert S. Elliott, Bruce Rankin, Amanda Elliott, William Julius Wilson, and David Huizinga), Youth Gangs (Charles C. Thomas 2006, with Robert J. Franzese and Herbert C. Covey), and the Handbook of Longitudinal Research (Elsevier 2008), as well as other books and journal articles in the areas of criminology, delinquency, population studies, and statistics.
