Methodology in the Social Sciences – serie

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (www.guilford.com/geiser-materials) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.

This book offers a complete, practical guide to doing an intensive longitudinal study with individuals, dyads, or groups. It provides the tools for studying social, psychological, and physiological processes in everyday contexts, using methods such as diary and experience sampling. A range of engaging, worked-through research examples with datasets are featured. Coverage includes how to: select the best intensive longitudinal design for a particular research question, apply multilevel models to within-subject designs, model within-subject change processes for continuous and categorical outcomes, assess the reliability of within-subject changes, assure sufficient statistical power, and more. Several end-of-chapter write-ups illustrate effective ways to present study findings for publication. Datasets and output in SPSS, SAS, Mplus, HLM, MLwiN, and R for the examples are available on the companion website (www.intensivelongitudinal.com).

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.

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.

With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website (www.guilford.com/brown3-materials) offers data and program syntax files for most of the research examples, as well as links to CFA-related resources.New to This Edition*Updated throughout to incorporate important developments in latent variable modeling.*Chapter on Bayesian CFA and multilevel measurement models.*Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables.*Utilizes the latest versions of major latent variable software packages.

With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website (www.guilford.com/brown3-materials) offers data and program syntax files for most of the research examples, as well as links to CFA-related resources.New to This Edition*Updated throughout to incorporate important developments in latent variable modeling.*Chapter on Bayesian CFA and multilevel measurement models.*Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables.*Utilizes the latest versions of major latent variable software packages.

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.

Grounded in current knowledge and professional practice, this book provides up-to-date coverage of psychometric theory, methods, and interpretation of results. Essential topics include measurement and statistical concepts, scaling models, test design and development, reliability, validity, factor analysis, item response theory, and generalizability theory. Also addressed are norming and test equating, topics not typically covered in traditional psychometrics texts. Examples drawn from a dataset on intelligence testing are used throughout the book, elucidating the assumptions underlying particular methods and providing SPSS (or alternative) syntax for conducting analyses. The companion website presents datasets for all examples as well as PowerPoint slides of figures and key concepts. Pedagogical features include equation boxes with explanations of statistical notation, and end-of-chapter glossaries. The Appendix offers extensions of the topical chapters with example source code from SAS, SPSS, IRTPRO, BILOG-MG, PARSCALE, TESTFACT, and DIMTEST.

Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and Practical Matters sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs.

Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website ([ital]www.guilford.com/weakliem-materials[/ital]) supplies data and syntax files for the book's examples.

Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results.User-Friendly Features*Real, worked-through longitudinal data examples serving as illustrations in each chapter.*Script boxes that provide code for fitting the models to example data and facilitate application to the reader's own data.*"Important Considerations" sections offering caveats, warnings, and recommendations for the use of specific models.*Companion website supplying datasets and syntax for the book's examples, along with additional code in SAS/R for linear mixed-effects modeling.Winner--Barbara Byrne Book Award from the Society of Multivariate Experimental Psychology

Which types of validity evidence should be considered when determining whether a scale is appropriate for a given measurement situation? What about reliability evidence? Using clear explanations illustrated by examples from across the social and behavioral sciences, this engaging text prepares students to make effective decisions about the selection, administration, scoring, interpretation, and development of measurement instruments. Coverage includes the essential measurement topics of scale development, item writing and analysis, and reliability and validity, as well as more advanced topics such as exploratory and confirmatory factor analysis, item response theory, diagnostic classification models, test bias and fairness, standard setting, and equating. End-of-chapter exercises (with answers) emphasize both computations and conceptual understanding to encourage readers to think critically about the material. The companion website (www.guilford.com/bandalos-materials) provides annotated examples, syntax, and datasets in both SPSS and SAS (for most chapters), so that readers can redo the analyses in each chapter.

An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state–trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest–posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice. Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility. Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.

Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest–posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice. Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility. Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.

This accessible, hands-on text has now been revised and updated, with expanded coverage of topics including how theory may emerge from exploratory data analysis. The book prepares graduate students, new researchers, and even seasoned investigators to develop their own theories and build on existing ones. Concrete strategies are provided to help readers generate ideas, define constructs, and think through relationships and processes that link constructs. Compelling examples from multiple disciplines illustrate the use of quantitative, qualitative, and mixed methods orientations to theory construction. The text also offers practical advice for writing effectively about theories in papers and grant applications. Readers learn by doing via application and concept exercises, demonstration boxes, and practical guidelines. The extensive companion website (www.theory-construction.com) includes PowerPoint slides of all of the book’s figures, primers on advanced topics, video demonstrations, supplemental exercises, and other resources.New to This Edition*Emergent theory is now covered in mixed methods as well as qualitative approaches, plus in a new chapter on exploratory quantitative methods that can help generate new theory through data mining.*Chapter on whether and how to revise a theory when faced with disconfirmatory data.*Two chapters on the theoretical underpinnings of measurement practices.*New or expanded discussions of what constitutes a theoretical contribution, conceptual logic models, thought experiments, and more.Pedagogical Features*Application and concept exercises in every chapter.*Lists of key terms and engaging topical boxes.*Annotated suggestions for further reading.*New companion website with rich resources for students and instructors.*Chapters stand on their own and can be used in any order.

This accessible, hands-on text has now been revised and updated, with expanded coverage of topics including how theory may emerge from exploratory data analysis. The book prepares graduate students, new researchers, and even seasoned investigators to develop their own theories and build on existing ones. Concrete strategies are provided to help readers generate ideas, define constructs, and think through relationships and processes that link constructs. Compelling examples from multiple disciplines illustrate the use of quantitative, qualitative, and mixed methods orientations to theory construction. The text also offers practical advice for writing effectively about theories in papers and grant applications. Readers learn by doing via application and concept exercises, demonstration boxes, and practical guidelines. The extensive companion website (www.theory-construction.com) includes PowerPoint slides of all of the book’s figures, primers on advanced topics, video demonstrations, supplemental exercises, and other resources.New to This Edition*Emergent theory is now covered in mixed methods as well as qualitative approaches, plus in a new chapter on exploratory quantitative methods that can help generate new theory through data mining.*Chapter on whether and how to revise a theory when faced with disconfirmatory data.*Two chapters on the theoretical underpinnings of measurement practices.*New or expanded discussions of what constitutes a theoretical contribution, conceptual logic models, thought experiments, and more.Pedagogical Features*Application and concept exercises in every chapter.*Lists of key terms and engaging topical boxes.*Annotated suggestions for further reading.*New companion website with rich resources for students and instructors.*Chapters stand on their own and can be used in any order.

An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state–trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl’s structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and new self-tests on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor.New to This EditionChapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis. Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM. Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects. Pedagogical FeaturesNew self-tests of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers. End-of-chapter suggestions for further reading and exercises with answers. Troublesome examples from real data, with guidance for handling typical problems in analyses. Topic boxes on special issues and boxed rules to remember. Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the book’s detailed examples.

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl’s structural causal model), and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and new self-tests on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor.New to This EditionChapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis. Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM. Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects. Pedagogical FeaturesNew self-tests of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers. End-of-chapter suggestions for further reading and exercises with answers. Troublesome examples from real data, with guidance for handling typical problems in analyses. Topic boxes on special issues and boxed rules to remember. Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the book’s detailed examples.

This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.

Regression toward the mean is a complex statistical principle that plays a crucial role in any research involving the measurement of change. This primer is designed to help researchers more fully understand this phenomenon and avoid common errors in interpretation. The book presents new methods of graphing regression toward the mean, facilitating comprehension with a wealth of figures and diagrams. Special attention is given to applications related to program or treatment evaluation. Numerous concrete examples illustrate the ways researchers all too often attribute effects to an intervention or other causal variable without considering regression artifacts as an alternative explanation for change. Also discussed are instances when problems are actually created, instead of solved, by correction for regression toward the mean. Throughout, the authors strive to use nontechnical language and to keep simulations and formulas as accessible as possible.

Regression toward the mean is a complex statistical principle that plays a crucial role in any research involving the measurement of change. This primer is designed to help researchers more fully understand this phenomenon and avoid common errors in interpretation. The book presents new methods of graphing regression toward the mean, facilitating comprehension with a wealth of figures and diagrams. Special attention is given to applications related to program or treatment evaluation. Numerous concrete examples illustrate the ways researchers all too often attribute effects to an intervention or other causal variable without considering regression artifacts as an alternative explanation for change. Also discussed are instances when problems are actually created, instead of solved, by correction for regression toward the mean. Throughout, the authors strive to use nontechnical language and to keep simulations and formulas as accessible as possible.

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 companion website (www.guilford.com/aguinis-materials).

While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed--such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures--the book also explains how to make sound decisions about their use. Patrick E. McKnight's website offers a periodically updated annotated bibliography on missing data and links to other Web resources that address missing data.

While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed--such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures--the book also explains how to make sound decisions about their use. Patrick E. McKnight's website offers a periodically updated annotated bibliography on missing data and links to other Web resources that address missing data.