Logit Modeling: Practical Applications / Edition 1 available in Paperback
- Pub. Date:
- SAGE Publications
Logit Modeling represents a breakthrough for researchers because it offers ways for more efficient estimation of models with multiple categorical variables, particularly whenever the measurement assumptions for classical multiple regression fail to be met. Taking an applied approach, De Maris begins by describing the logit model in the context of the general loglinear model, moving its application from two-way to multidimensional tables. He then divides the rest of the book between an examination of the varieties of logit models for contingency tables and logistic regression. Throughout his coverage of both these major areas, De Maris emphasizes interpretation of results. The book concludes with an extension of logistic regression to dependent variables with more than two categories.
|Series:||Quantitative Applications in the Social Sciences Series , #86|
|Edition description:||New Edition|
|Product dimensions:||5.50(w) x 8.50(h) x 0.19(d)|
About the Author
Alfred De Maris is Professor of Sociology and Statistician for the Center for Family and Demographic Research at Bowling Green State University in Bowling Green, Ohio. His substantive interests are focused on romantic relationships, including issues surrounding premarital cohabitation, the prediction of marital instability, and the antecedents and consequences of violence in heterosexual dyads. His work in these areas has appeared in Social Forces, Social Psychology Quarterly, Journal of Marriage and Family, Journal of Family Issues, Family Relations, and Journal of Sex Research. His primary specialty in social statistics is regression modeling, in particular, logistic regression. His articles on this topic have appeared in Social Forces, Psychological Bulletin, and Sociological Methods & Research, among other venues. He is also the author of Logit Modeling: Practical Applications (1992, Sage) and Regression with Social Data: Modeling Continuous and Limited Response Variables (2004, Wiley).
Table of Contents
IntroductionLogit Models Theoretical BackgroundLogit Models for Multidimensional TablesLogistic RegressionAdvanced Topics in Logistic Regression