Interaction Effects in Multiple Regression


Author: James Jaccard,Jim Jaccard,Robert Turrisi
Publisher: SAGE
ISBN: 9780761927426
Category: Mathematics
Page: 92
View: 5126

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This is a practical introduction to conducting analyses of interaction effects in the context of multiple regression. This new edition expands coverage on the analysis of three-way interactions in multiple regression analysis.

Interaction Effects in Logistic Regression


Author: James Jaccard,Jim Jaccard
Publisher: SAGE
ISBN: 9780761922070
Category: Mathematics
Page: 70
View: 9083

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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.

LISREL Approaches to Interaction Effects in Multiple Regression


Author: James Jaccard,Choi K Wan
Publisher: SAGE
ISBN: 9780803971790
Category: Social Science
Page: 98
View: 4843

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With detailed examples, this book demonstrates the use of the computer program LISREL and how it can be applied to the analysis of interactions in regression frameworks. The authors consider a wide range of applications including: qualitative moderator variables; longitudinal designs; and product term analysis. They describe different types of measurement error and then present a discussion of latent variable representations of measurement error which serves as the foundation for the analyses described in later chapters. Finally they offer a brief introduction to LISREL and show how it can be used to execute the analyses. Readers can use this book without any prior training in LISREL and will find it an excellent introduction to analytic methods that deal with the problem of measurement error in the analysis of interactions.

Regression with Dummy Variables


Author: Melissa A. Hardy
Publisher: SAGE
ISBN: 9780803951280
Category: Social Science
Page: 90
View: 6517

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It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.

Fixed Effects Regression Models


Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1483389278
Category: Social Science
Page: 136
View: 8029

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This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here

Understanding Regression Assumptions


Author: William D. Berry
Publisher: SAGE Publications
ISBN: 1506315828
Category: Social Science
Page: 104
View: 8532

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Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.

Applied logistic regression analysis


Author: Scott W. Menard
Publisher: Sage Publications, Inc
ISBN: N.A
Category: Mathematics
Page: 98
View: 7668

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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.

Mediation Analysis


Author: Dawn Iacobucci
Publisher: SAGE
ISBN: 141292569X
Category: Mathematics
Page: 85
View: 9571

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Social science data analysts have long considered the mediation of intermediate variables of primary importance in understanding individuals' social, behavioural and other kinds of outcomes. In this book Dawn Iacobucci uses the method known as structural equation modeling (SEM) in modeling mediation in causal analysis. This approach offers the most flexibility and allows the researcher to deal with mediation in the presence of multiple measures, mediated moderation, and moderated mediation, among other variations on the mediation theme. The wide availability of software implementing SEM gives the reader necessary tools for modeling mediation so that a proper understanding of causal relationship is achieved.

Multiple Regression

Testing and Interpreting Interactions
Author: Leona S. Aiken,Stephen G. West,Raymond R. Reno
Publisher: SAGE
ISBN: 9780761907121
Category: Business & Economics
Page: 212
View: 5656

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This successful book, now available in paperback, provides academics and researchers with a clear set of prescriptions for estimating, testing and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models, or for those enrolled in courses on multivariate statistics.

Linear Probability, Logit, and Probit Models


Author: John H. Aldrich,Forrest D. Nelson,E. Scott Adler
Publisher: SAGE
ISBN: 9780803921337
Category: Mathematics
Page: 95
View: 4788

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Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise "limited" dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.

Logistic Regression Models for Ordinal Response Variables


Author: Ann A. O'Connell
Publisher: SAGE
ISBN: 9780761929895
Category: Mathematics
Page: 107
View: 1077

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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.

Regression Models

Censored, Sample Selected, Or Truncated Data
Author: Richard Breen
Publisher: SAGE
ISBN: 9780803957107
Category: Social Science
Page: 80
View: 9402

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What techniques can social scientists use when an outcome variable for a sample is not representative of the population for whom they would like to generalize the results? This book provides an introduction to regression models for such data including censored, sample-selected and truncated data.

Modeling and Interpreting Interactive Hypotheses in Regression Analysis


Author: Robert Franzese,Cindy Kam
Publisher: University of Michigan Press
ISBN: 9780472022991
Category: Political Science
Page: 168
View: 8972

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Social scientists study complex phenomena about which they often propose intricate hypotheses tested with linear-interactive or multiplicative terms. While interaction terms are hardly new to social science research, researchers have yet to develop a common methodology for using and interpreting them. Modeling and Interpreting Interactive Hypotheses in Regression Analysis provides step-by-step guidance on how to connect substantive theories to statistical models and how to interpret and present the results. "Kam and Franzese is a must-have for all empirical social scientists interested in teasing out the complexities of their data." ---Janet M. Box-Steffensmeier, Ohio State University "Kam and Franzese have written what will become the definitive source on dealing with interaction terms and testing interactive hypotheses. It will serve as the standard reference for political scientists and will be one of those books that everyone will turn to when helping our students or doing our work. But more than that, this book is the best text I have seen for getting students to really think about the importance of careful specification and testing of their hypotheses." ---David A. M. Peterson, Texas A&M University "Kam and Franzese have given scholars and teachers of regression models something they've needed for years: a clear, concise guide to understanding multiplicative interactions. Motivated by real substantive examples and packed with valuable examples and graphs, their book belongs on the shelf of every working social scientist." ---Christopher Zorn, University of South Carolina "Kam and Franzese make it easy to model what good researchers have known for a long time: many important and interesting causal effects depend on the presence of other conditions. Their book shows how to explore interactive hypotheses in your own research and how to present your results. The book is straightforward yet technically sophisticated. There are no more excuses for misunderstanding, misrepresenting, or simply missing out on interaction effects!" ---Andrew Gould, University of Notre Dame Cindy D. Kam is Assistant Professor, Department of Political Science, University of California, Davis. Robert J. Franzese Jr. is Associate Professor, Department of Political Science, University of Michigan, and Research Associate Professor, Center for Political Studies, Institute for Social Research, University of Michigan. For datasets, syntax, and worksheets to help readers work through the examples covered in the book, visit: www.press.umich.edu/KamFranzese/Interactions.html

Applied regression

an introduction
Author: Michael S. Lewis-Beck
Publisher: Sage Publications, Inc
ISBN: N.A
Category: Mathematics
Page: 79
View: 4356

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Applied regression allows social scientists who are not specialists in quantitative techniques to arrive at clear verbal explanations of their numerical results. Provides a lucid discussion of more specialized subjects: analysis of residuals, interaction effects, specification error, multicollinearity, standardized coefficients, and dummy variables.

Missing Data


Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1452207909
Category: Social Science
Page: 104
View: 8623

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Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

LISREL Approaches to Interaction Effects in Multiple Regression


Author: James Jaccard,Choi K Wan
Publisher: SAGE
ISBN: 9780803971790
Category: Social Science
Page: 98
View: 8822

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With detailed examples, this book demonstrates the use of the computer program LISREL and how it can be applied to the analysis of interactions in regression frameworks. The authors consider a wide range of applications including: qualitative moderator variables; longitudinal designs; and product term analysis. They describe different types of measurement error and then present a discussion of latent variable representations of measurement error which serves as the foundation for the analyses described in later chapters. Finally they offer a brief introduction to LISREL and show how it can be used to execute the analyses. Readers can use this book without any prior training in LISREL and will find it an excellent introduction to analytic methods that deal with the problem of measurement error in the analysis of interactions.

Logistic Regression

A Primer
Author: Fred C. Pampel
Publisher: SAGE Publications
ISBN: 1452207615
Category: Social Science
Page: 96
View: 8607

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Pampel's book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked-out examples. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.

Interaction Effects in Linear and Generalized Linear Models

Examples and Applications Using Stata
Author: Robert L. Kaufman
Publisher: SAGE Publications
ISBN: 1506365396
Category: Social Science
Page: 608
View: 9500

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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.

Regression Analysis and Linear Models

Concepts, Applications, and Implementation
Author: Richard B. Darlington,Andrew F. Hayes
Publisher: Guilford Publications
ISBN: 1462527981
Category: Social Science
Page: 661
View: 2951

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Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable’s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Regression Models for Categorical, Count, and Related Variables

An Applied Approach
Author: John P. Hoffmann
Publisher: Univ of California Press
ISBN: 0520289293
Category: Social Science
Page: 432
View: 7068

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Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes—all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book.