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)|
Table of Contents
Logit Models for Multidimensional Tables
Advanced Topics in Logistic Regression