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This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the coefficients of interactive logistic models for a wide range of situations encountered in the research literature. The volume is oriented toward the applied researcher with a rudimentary background in multiple regression and logistic regression and does not include complex formulas that could be intimidating to the applied researcher.
Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata (downloadable from the Robert L. Kaufman’s website), and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The data sets and the Stata code to reproduce the results of the application examples are available online.
Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis. Learn more about "The Little Green Book" - QASS Series! Click Here
This book presents a method for bringing data analysis and statistical technique into line with theory. The author begins by describing the elaboration model for analyzing the empirical association between variables. She then introduces a new concept into this model, the focal relationship. Building upon the focal relationship as the cornerstone for all subsequent analysis, two analytic strategies are developed to establish its internal validity: an exclusionary strategy to eliminate alternative explanations, and an inclusive strategy which looks at the interconnected set of relationships predicted by theory. Using real examples of social research, the author demonstrates the use of this approach for two common forms of analysis, multiple linear regression and logistic regression. Whether learning data analysis for the first time or adding new techniques to your repertoire, this book provides an excellent basis for theory-based data analysis.
Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics.
Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.
Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables. The book is aimed at readers with a background in bivariate and multiple linear regression.

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