About this product

Product Identifiers

PublisherSAGE Publications, Incorporated

ISBN-10150636537X

ISBN-139781506365374

eBay Product ID (ePID)16038707969

Product Key Features

Number of Pages608 Pages

LanguageEnglish

Publication NameInteraction Effects in Linear and Generalized Linear Models : Examples and Applications Using Stata

SubjectMethodology, Probability & Statistics / Regression Analysis, Probability & Statistics / General, Statistics

Publication Year2018

TypeTextbook

AuthorRobert L. Kaufman

Subject AreaMathematics, Social Science

SeriesAdvanced Quantitative Techniques in the Social Sciences Ser.

FormatHardcover

Dimensions

Item Height1 in

Item Weight40.1 Oz

Item Length10.2 in

Item Width7.2 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2018-015288

Dewey Edition23

Reviews"This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results." -- Nicole Kalaf-Hughes "Interaction Effects in Linear and Generalized Linear Models provides an intuitive approach that benefits both new users of Stata getting acquainted with these statistical models as well as experienced students looking for a refresher. The topic of interactions is greatly important given that many of our main theories in the social and behavioral sciences rely on moderating effects of variables. This book does a terrific job of guiding the reader through the various statistical commands available in Stata and explaining the results and taking the reader through different considerations in graphically presenting their results." -- Jennifer Hayes Clark, This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results., Interaction Effects in Linear and Generalized Linear Modelsprovides an intuitive approach that benefits both new users of Stata getting acquainted with these statistical models as well as experienced students looking for a refresher. The topic of interactions is greatly important given that many of our main theories in the social and behavioral sciences rely on moderating effects of variables. This book does a terrific job of guiding the reader through the various statistical commands available in Stata and explaining the results and taking the reader through different considerations in graphically presenting their results.

IllustratedYes

Dewey Decimal519.5/36

Table Of ContentSeries Editor's IntroductionPrefaceAcknowledgmentsAbout the Author1. Introduction and BackgroundOverview: Why Should You Read This Book?The Logic of Interaction Effects in Linear Regression ModelsThe Logic of Interaction Effects in GLMsDiagnostic Testing and Consequences of Model MisspecificationRoadmap for the Rest of the BookChapter 1 NotesPART I. PRINCIPLES2. Basics of Interpreting the Focal Variable's Effect in the Modeling ComponentMathematical (Geometric) Foundation for GFIGFI Basics: Algebraic Regrouping, Point Estimates, and Sign ChangesPlotting EffectsSummarySpecial TopicsChapter 2 Notes3. The Varying Significance of the Focal Variable's EffectTest Statistics and Significance LevelsJN Mathematically Derived Significance RegionEmpirically Defined Significance RegionConfidence Bounds and Error Bar PlotsSummary and RecommendationsChapter 3 Notes4. Linear (Identity Link) Models: Using the Predicted Outcome for InterpretationOptions for Display and Reference ValuesReference Values for the Other Predictors (Z)Constructing Tables of Predicted Outcome ValuesCharts and Plots of the Expected Value of the OutcomeConclusionSpecial TopicsChapter 4 Notes5. Nonidentity Link Functions: Challenges of Interpreting Interactions in Nonlinear ModelsIdentifying the IssuesMathematically Defining the Confounded Sources of NonlinearityRevisiting Options for Display and Reference ValuesSolutionsSummary and RecommendationsDerivations and CalculationsChapter 5 NotesPART II. APPLICATIONS6. ICALC Toolkit: Syntax, Options, and ExamplesOverviewINTSPEC: Syntax and OptionsGFI Tool: Syntax and OptionsSIGREG Tool: Syntax and OptionsEFFDISP Tool: Syntax and OptionsOUTDISP Tool: Syntax and OptionsNext StepsChapter 6 Notes7. Linear Regression Model ApplicationsOverviewSingle-Moderator ExampleTwo-Moderator ExampleSpecial TopicsChapter 7 Notes8. Logistic Regression and Probit ApplicationsOverviewOne-Moderator Example (Nominal by Nominal)Three-Way Interaction Example (Interval by Interval by Nominal)Special TopicsChapter 8 Notes9. Multinomial Logistic Regression ApplicationsOverviewOne-Moderator Example (Interval by Interval)Two-Moderator Example (Interval by Two Nominal)Special TopicsChapter 9 Notes10. Ordinal Regression ModelsOverviewOne-Moderator Example (Interval by Nominal)Two-Moderator Interaction Example (Nominal by Two Interval)Special TopicsChapter 10 Notes11. Count ModelsOverviewOne-Moderator Example (Interval by Nominal)Three-Way Interaction Example (Interval by Interval by Nominal)Special TopicsChapter 11 Notes12. Extensions and Final ThoughtsExtensionsFinal Thoughts: Dos, Don'ts, and CautionsChapter 12 NotesAppendix: Data for ExamplesChapter 2: One-Moderator ExampleChapter 2: Two-Moderator Mixed ExampleChapter 2: Two-Moderator Interval ExampleChapter 2: Three-Way Interaction ExampleChapter 3: One-Moderator ExampleChapter 3: Two-Moderator ExampleChapter 3: Three-Way Interaction ExampleChapter 4: Tables One-Moderator Example and Figures Example 3Chapter 4: Tables Two-Moderator ExampleChapter 4: Figures Examples 1 and 2Chapter 4: Figures Example 4Chapter 4: Tables Three-Way Interaction Example and Figures Example 5Chapter 5: Examples 1 and 2Chapter 5: Example 3Chapter 5: Example 4Chapter 6: One-Moderator ExampleChapter 6: Two-Moderator ExampleChapter 6: Three-Way Interaction ExampleChapter 7: One-Moderator ExampleChapter 7: Two-Moderator ExampleChapter 8: One-Moderator ExampleChapter 8: Three-Way Interaction ExampleChapter 9: One-Moderator ExampleChapter 9: Two-Moderator ExampleChapter 10: One-Moderator ExampleChapter 10: Two-Moderator ExampleChapter 11: One-Moderator ExampleChapter 11: Three-Way Interaction ExampleChapter 12: Polynomial ExampleChapter 12: Heckman ExampleChapter 12: Survival Analysis ExampleReferencesData SourcesIndex

Synopsis"This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results." -Nicole Kalaf-Hughes, Bowling Green State 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 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, 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 author's website provides a downloadable toolkit of Stata® routines to produce the calculations, tables, and graphics for each interpretive tool discussed. Also available are the Stata® dataset files to run the examples in the book., 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., "This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results." -Nicole Kalaf-Hughes, Bowling Green State 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 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, 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 author's website at www.icalcrlk.com provides a downloadable toolkit of Stata (R) routines to produce the calculations, tables, and graphics for each interpretive tool discussed. Also available are the Stata (R) dataset files to run the examples in the book., 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.

LC Classification NumberQA278.2.K38 2019