Regression Analysis for Categorical Moderators
Regression Analysis for Categorical Moderators

Author: Herman Aguinis

Publisher: Guilford Press

Published: 2004-01-01

Total Pages: 230

ISBN-13: 9781572309692

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Does the stability of personality vary by gender or ethnicity? Does a particular therapy work better to treat clients with one type of personality disorder than those with another? Providing a solution to thorny problems such as these, Aguinis shows readers how to better assess whether the relationship between two variables is moderated by group membership through the use of a statistical technique, moderated multiple regression (MMR). Clearly written, the book requires only basic knowledge of inferential statistics. It helps students, researchers, and practitioners determine whether a particular intervention is likely to yield dissimilar outcomes for members of various groups. Associated computer programs and data sets are available at the author's website (http: //mypage.iu.edu/ haguinis/mmr).

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

Author: Joseph F. Hair Jr.

Publisher: Springer Nature

Published: 2021-11-03

Total Pages: 208

ISBN-13: 3030805190

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Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.

Doing Meta-Analysis with R
Doing Meta-Analysis with R

Author: Mathias Harrer

Publisher: CRC Press

Published: 2021-09-15

Total Pages: 500

ISBN-13: 1000435636

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Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Theory-Based Data Analysis for the Social Sciences
Theory-Based Data Analysis for the Social Sciences

Author: Carol S. Aneshensel

Publisher: SAGE

Published: 2013

Total Pages: 473

ISBN-13: 1412994357

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This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.

Multiple Regression
Multiple Regression

Author: Leona S. Aiken

Publisher: SAGE

Published: 1991

Total Pages: 228

ISBN-13: 9780761907121

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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.

Interaction Effects in Logistic Regression
Interaction Effects in Logistic Regression

Author: James Jaccard

Publisher: SAGE Publications

Published: 2001-02-21

Total Pages: 84

ISBN-13: 1544332599

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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.

Regression Analysis and Linear Models
Regression Analysis and Linear Models

Author: Richard B. Darlington

Publisher: Guilford Publications

Published: 2016-08-22

Total Pages: 689

ISBN-13: 1462527981

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Emphasizing 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.

Doing Statistical Mediation and Moderation
Doing Statistical Mediation and Moderation

Author: Paul E. Jose

Publisher: Guilford Press

Published: 2013-02-25

Total Pages: 354

ISBN-13: 1462508235

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Written in a friendly, conversational style, this book offers a hands-on approach to statistical mediation and moderation for both beginning researchers and those familiar with modeling. Starting with a gentle review of regression-based analysis, Paul Jose covers basic mediation and moderation techniques before moving on to advanced topics in multilevel modeling, structural equation modeling, and hybrid combinations, such as moderated mediation. User-friendly features include numerous graphs and carefully worked-through examples; "Helpful Suggestions" about procedures and pitfalls; "Knowledge Boxes" delving into special topics, such as dummy coding; and end-of-chapter exercises and problems (with answers). The companion website (www.guilford.com/jose-materials) provides downloadable data and syntax files for the book's examples and exercises, as well as links to Jose's online programs, MedGraph and ModGraph. Appendices present SPSS, Amos, and Mplus syntax for conducting the key types of analyses.

Regression with Dummy Variables
Regression with Dummy Variables

Author: Melissa A. Hardy

Publisher: SAGE

Published: 1993-02-25

Total Pages: 100

ISBN-13: 9780803951280

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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.