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HANDBOOKS
ADVANCED QUANTITATIVE TECHNIQUES IN THE SOCIAL SCIENCES SERIES
INTERACTION EFFECTS IN LINEAR AND GENERALIZED LINEAR MODELS:
Examples and Applications using Stata
Robert L. Kaufman, Temple University
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 unifi ed 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, and offers a series of start-to-fi nish application examples to
show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS
regression.
CONTENTS 1: Introduction and Background / 2: Basics of Interpreting the Focal Variable's Effect in the Modeling Component / 3: The Varying
Signifi cance of the Focal Variable's Effect / 4: Linear (Identity Link) Models: Using the Predicted Outcome for Interpretation / 5: Non-identity Link
Functions: Challenges of Interpreting Interactions in Non-Linear Models / 6: ICALC Toolkit: Syntax, Options, and Examples / 7: Linear Regression
Model Applications / 8: Logistic Regression and Probit Applications / 9: Multinomial Logistic Regression Applications / 10: Ordinal Regression
Models / 11: Count Models / 12: Extensions and Final Thoughts
HARDCOVER ISBN: 978-1-5063-6537-4 • ©2019 • 608 PAGES •
online
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ADVANCED ISSUES IN PARTIAL LEAST SQUARES STRUCTURAL EQUATION
MODELING
Joseph F. Hair, Jr., University of South Alabama • Marko Sarstedt, Otto-von-Guericke University,
Magdeburg, Germany and University of Newcastle, Australia • Christian M. Ringle, Hamburg University
of Technology, Germany and University of Newcastle, Australia • Siegfried P. Gudergan, University of
Newcastle, Australia
Written as an extension of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Second Edition,
this easy-to-understand, practical guide covers advanced content on PLS-SEM to help students and researchers apply
techniques to research problems and accurately interpret results. The book provides a brief overview of basic concepts
before moving to the more advanced material. Offering extensive examples on SmartPLS 3 software (www.smartpls.
com) and accompanied by free downloadable data sets, the book emphasizes that any advanced PLS-SEM approach
should be carefully applied to ensure that it fi ts the appropriate research context and the data characteristics that
underpin the research.
CONTENTS 1. An Overview of Recent and Emerging Developments in PLS-SEM / 2. Advanced Modeling / 3. Advanced Model Assessment / 4.
Modeling Observed Heterogeneity / 5. Modeling Unobserved Heterogeneity
PAPERBACK ISBN: 978-1-4833-7739-1 • ©2018 • 272 PAGES •
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AN R COMPANION TO APPLIED REGRESSION THIRD EDITION
John Fox, McMaster University, Canada • Sanford Weisberg, University of Minnesota
An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in
the context of applied regression analysis. The authors provide a step-by-step guide to using the high-quality free
statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage
of generalized linear models, and substantial web-based support materials. This best-selling text is intended as a
companion for courses on modern applied regression analysis. The authors have substantially updated both the car
and effects packages for R for this new edition, and include coverage of RStudio and R Markdown.
CONTENTS 1. Getting Started with R and RStudio / 2. Reading and Manipulating Data / 3. Exploring and Transforming Data / 4. Fitting Linear
Models / 5. Standard Errors, Confi dence Intervals, Tests / 6. Fitting Generalized Linear Models / 7. Fitting Mixed-Effects Models / 8. Regression
Diagnostics / 9. Drawing Graphs / 10. An Introduction to R Programming
PAPERBACK ISBN: 978-1-5443-3647-3 • ©2019 • 608 PAGES •
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APPLIED REGRESSION ANALYSIS & GENERALIZED LINEAR MODELS THIRD EDITION
John Fox, McMaster University, Canada
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition
provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as
bootstrapping and missing data. Updated throughout, this edition includes new chapters on mixed-effects models for
hierarchical and longitudinal data.
CONTENTS 1. Statistical Models and Social Science / I DATA CRAFT / 2. What Is Regression Analysis? / 3. Examining Data / 4. Transforming Data /
II LINEAR MODELS AND LEAST SQUARES / 5. Linear Least-Squares Regression / 6. Statistical Inference for Regression / 7. Dummy-Variable Regression
/ 8. Analysis of Variance / 9. Statistical Theory for Linear Models / 10. The Vector Geometry of Linear Models / III LINEAR-MODEL DIAGNOSTICS / 11.
Unusual and Inuential Data / 12 Non-Normality, Nonconstant Variance, Nonlinearity / 13. Collinearity and Its Purported Remedies / IV GENERALIZED
LINEAR MODELS / 14. Logit and Probit Models / 15. Generalized Linear Models / V EXTENDING LINEAR AND GENERALIZED LINEAR MODELS / 16
Time-Series Regression and Generalized Least-Squares / 17. Nonlinear Regression / 18. Nonparametric Regression / 19. Robust Regression / 20.
Missing Data in Regression Models / 21. Bootstrapping Regression Models / 22. Model Selection, Averaging, and Validation / VI MIXED-EFFECTS
MODELS / 23. Linear Mixed-Effects Models / 24. Generalized Linear and Nonlinear Mixed-Effects Models
HARDCOVER ISBN: 978-1-4522-0566-3 • ©2016 • 816 PAGES •
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