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.

PrefaceChapter 1. Introduction: Linear Regression and Logistic RegressionChapter 2. Log-Linear Analysis, Logit Analysis, and Logistic RegressionChapter 3. Quantitative Approaches to Model Fit and Explained VariationChapter 4. Prediction Tables and Qualitative Approaches to Explained VariationChapter 5. Logistic Regression CoefficientsChapter 6. Model Specification, Variable Selection, and Model BuildingChapter 7. Logistic Regression Diagnostics and Problems of InferenceChapter 8. Path Analysis With Logistic Regression (PALR)Chapter 9. Polytomous Logistic Regression for Unordered Categorical VariablesChapter 10. Ordinal Logistic RegressionChapter 11. Clusters, Contexts, and Dependent Data: Logistic Regression for Clustered Sample Survey DataChapter 12. Conditional Logistic Regression Models for Related SamplesChapter 13. Longitudinal Panel Analysis With Logistic RegressionChapter 14. Logistic Regression for Historical and Developmental Change Models: Multilevel Logistic Regression and Discrete Time Event History AnalysisChapter 15. Comparisons: Logistic Regression and Alternative ModelsAppendix A: ESTIMATION FOR LOGISTIC REGRESSION MODELSAppendix B: PROOFS RELATED TO INDICES OF PREDICTIVE EFFICIENCYAppendix C: ORDINAL MEASURES OF EXPLAINED VARIATIONReferencesIndex

Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally. The book begins by showing how logistic regression combines aspects of multiple linear regression and loglinear analysis to overcome problems both techniques have with the analysis of dichotomous dependent variables with continuous predictors. The logistic regression model is then examined in detail, including how to evaluate the overall model and how to evaluate the impact of the different predictors in the model for different types of research questions. Unique to this book is the extensive consideration qualitative (prediction tables) as well as quantitative indices of how well the model predicts the dependent variable. The book then examines what can go wrong with the model and how to detect and correct it; the use of logistic regression in path analysis; nominal and ordinal dependent variables; modifications to the logistic regression model when the cases are not completely independent of one another; the use of logistic regression models for longitudinal data with few and with many repeated measurements; and alternatives to logistic regression.