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Description of  Interpreting and Comparing Effects in Logistic, Probit, and Logit Regression (Quantitative Applications in the Social Sciences)

1544364016 pdf Log-linear, logit and logistic regression models are the most common ways of analyzing data when (at least) the dependent variable is categorical. This volume shows how to compare coefficient estimates from regression models for categorical dependent variables in three typical research situations: (i) within one equation, (ii) between identical equations estimated in different subgroups, and (iii) between nested equations. This volume presents a practical, unified treatment of these topics, and considers the advantages and disadvantages of each approach, and when to use them, so that applied researchers can make the best choice related to their research problem. The techniques are illustrated with data from simulation experiments and from publicly available surveys.