Quantitative Methodology Series – serie

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.

Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.

Multilevel and Longitudinal Modeling with IBM SPSS, Third Edition, demonstrates how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Versions 25-27. Annotated screenshots with all relevant output provide readers with a step-by-step understanding of each technique as they are shown how to navigate the program. Throughout, diagnostic tools, data management issues, and related graphics are introduced. SPSS commands show the flow of the menu structure and how to facilitate model building, while annotated syntax is also available for those who prefer this approach. Extended examples illustrating the logic of model development and evaluation are included throughout the book, demonstrating the context and rationale of the research questions and the steps around which the analyses are structured. The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, developing a multilevel model, extensions of the basic two-level model (e.g., three-level models, models for binary and ordinal outcomes), and troubleshooting techniques for everyday-use programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are next developed, followed by models with multivariate outcomes and, finally, models with cross-classified and multiple membership data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues (e.g., missing data, sample weights) to keep in mind in conducting multilevel analyses. Key features of the third edition: Thoroughly updated throughout to reflect IBM SPSS Versions 26-27. Introduction to fixed-effects regression for examining change over time where random-effects modeling may not be an optimal choice. Additional treatment of key topics specifically aligned with multilevel modeling (e.g., models with binary and ordinal outcomes). Expanded coverage of models with cross-classified and multiple membership data structures. Added discussion on model checking for improvement (e.g., examining residuals, locating outliers). Further discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures.Supported by online data sets, the book's practical approach makes it an essential text for graduate-level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in departments of business, education, health, psychology, and sociology. The book will also prove appealing to researchers in these fields. The book is designed to provide an excellent supplement to Heck and Thomas's An Introduction to Multilevel Modeling Techniques, Fourth Edition; however, it can also be used with any multilevel or longitudinal modeling book or as a stand-alone text.

Multilevel and Longitudinal Modeling with IBM SPSS, Third Edition, demonstrates how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Versions 25-27. Annotated screenshots with all relevant output provide readers with a step-by-step understanding of each technique as they are shown how to navigate the program. Throughout, diagnostic tools, data management issues, and related graphics are introduced. SPSS commands show the flow of the menu structure and how to facilitate model building, while annotated syntax is also available for those who prefer this approach. Extended examples illustrating the logic of model development and evaluation are included throughout the book, demonstrating the context and rationale of the research questions and the steps around which the analyses are structured. The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, developing a multilevel model, extensions of the basic two-level model (e.g., three-level models, models for binary and ordinal outcomes), and troubleshooting techniques for everyday-use programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are next developed, followed by models with multivariate outcomes and, finally, models with cross-classified and multiple membership data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues (e.g., missing data, sample weights) to keep in mind in conducting multilevel analyses. Key features of the third edition: Thoroughly updated throughout to reflect IBM SPSS Versions 26-27. Introduction to fixed-effects regression for examining change over time where random-effects modeling may not be an optimal choice. Additional treatment of key topics specifically aligned with multilevel modeling (e.g., models with binary and ordinal outcomes). Expanded coverage of models with cross-classified and multiple membership data structures. Added discussion on model checking for improvement (e.g., examining residuals, locating outliers). Further discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures.Supported by online data sets, the book's practical approach makes it an essential text for graduate-level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in departments of business, education, health, psychology, and sociology. The book will also prove appealing to researchers in these fields. The book is designed to provide an excellent supplement to Heck and Thomas's An Introduction to Multilevel Modeling Techniques, Fourth Edition; however, it can also be used with any multilevel or longitudinal modeling book or as a stand-alone text.

Methodological Advances in Educational Effectiveness Research is an important new work by some of the leading researchers in the field of Educational Effectiveness Research (EER). The book provides a state of the art snapshot of the methodology of EER now and clearly demonstrates the way it is applied in both research and evaluation. It shows how developments in the research methodology area such as the use of multilevel modelling approaches to analyse nested data have promoted the knowledge-base of educational effectiveness. But at the same time, as the authors show, the knowledge-base of educational effectiveness and the attempt to establish theoretical models do paradoxically challenge the development of methodologically appropriate studies including ways of analysing data. Guiding readers though the effective and appropriate use in educational effectiveness of: Longitudinal Studies Experimental Studies Mixed Research Methods Meta-analyses of effectiveness studies Using IRT to measure outcomes and factors Using Generalisability Theory to test the quality of data Multilevel modelling , and Structural Equation Modelling Techniques The authors draw in the expertise of scholars from around the world to show the mathematical background of each technique, the current and future applications, and Specific examples of applying this orientation to help the readers design their own effectiveness studies using specific methodological tools.

Methodological Advances in Educational Effectiveness Research is an important new work by some of the leading researchers in the field of Educational Effectiveness Research (EER). The book provides a state of the art snapshot of the methodology of EER now and clearly demonstrates the way it is applied in both research and evaluation. It shows how developments in the research methodology area such as the use of multilevel modelling approaches to analyse nested data have promoted the knowledge-base of educational effectiveness. But at the same time, as the authors show, the knowledge-base of educational effectiveness and the attempt to establish theoretical models do paradoxically challenge the development of methodologically appropriate studies including ways of analysing data. Guiding readers though the effective and appropriate use in educational effectiveness of: Longitudinal Studies Experimental Studies Mixed Research Methods Meta-analyses of effectiveness studies Using IRT to measure outcomes and factors Using Generalisability Theory to test the quality of data Multilevel modelling , and Structural Equation Modelling Techniques The authors draw in the expertise of scholars from around the world to show the mathematical background of each technique, the current and future applications, and Specific examples of applying this orientation to help the readers design their own effectiveness studies using specific methodological tools.

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.

Categorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets.This volume concentrates on latent class analysis and item response theory. These methods use latent variables to explain the relationships among observed categorical variables. Latent class analysis yields the classification of a group of respondents according to their pattern of scores on the categorical variables. This provides insight into the mechanisms producing the data and allows the estimation of factor structures and regression models conditional on the latent class structure. Item response theory leads to the identification of one or more ordinal or interval scales. In psychological and educational testing these scales are used for individual measurement of abilities and personality traits.The focus of this volume is applied. After a method is explained, the potential of the method for analyzing categorical data is illustrated by means of a real data example to show how it can be used effectively for solving a real data problem. These methods are accessible to researchers not trained explicitly in applied statistics. This volume appeals to researchers and advanced students in the social and behavioral sciences, including social, developmental, organizational, clinical and health psychologists, sociologists, educational and marketing researchers, and political scientists. In addition, it is of interest to those who collect data on categorical variables and are faced with the problem of how to analyze such variables--among themselves or in relation to metric variables.

This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to help researchers assess the predictive value of different combinations of variables in large data sets. Methodological findings and conceptual models that explain reliable EDM techniques for predicting and understanding various risk mechanisms are integrated throughout. Numerous examples illustrate the use of these techniques in practice. Contributors provide insight through hands-on experiences with their own use of EDM techniques in various settings. Readers are also introduced to the most popular EDM software programs. A related website at http://mephisto.unige.ch/pub/edm-book-supplement/offers color versions of the book’s figures, a supplemental paper to chapter 3, and R commands for some chapters. The results of EDM analyses can be perilous – they are often taken as predictions with little regard for cross-validating the results. This carelessness can be catastrophic in terms of money lost or patients misdiagnosed. This book addresses these concerns and advocates for the development of checks and balances for EDM analyses. Both the promises and the perils of EDM are addressed.Editors McArdle and Ritschard taught the "Exploratory Data Mining" Advanced Training Institute of the American Psychological Association (APA). All contributors are top researchers from the US and Europe. Organized into two parts--methodology and applications, the techniques covered include decision, regression, and SEM tree models, growth mixture modeling, and time based categorical sequential analysis. Some of the applications of EDM (and the corresponding data) explored include:selection to college based on risky prior academic profilesthe decline of cognitive abilities in older personsglobal perceptions of stress in adulthoodpredicting mortality from demographics and cognitive abilitiesrisk factors during pregnancy and the impact on neonatal developmentIntended as a reference for researchers, methodologists, and advanced students in the social and behavioral sciences including psychology, sociology, business, econometrics, and medicine, interested in learning to apply the latest exploratory data mining techniques. Prerequisites include a basic class in statistics.

This volume introduces the latest popular methods for conducting business research. The goal of each chapter author--a leading authority in a particular subject area--is to provide an understanding of each method with a minimum of mathematical derivations. The chapters are organized within three general interrelated topics--Measurement, Decision Analysis, and Modeling. The chapters on measurement discuss generalizability theory, latent trait and latent class models, and multi-faceted Rasch modeling. The chapters on decision analysis feature applied location theory models, data envelopment analysis, and heuristic search procedures. The chapters on modeling examine exploratory and confirmatory factor analysis, dynamic factor analysis, partial least squares and structural equation modeling, multilevel data analysis, modeling of longitudinal data by latent growth curve methods and structures, and configural models of longitudinal categorical data.

This volume introduces the latest popular methods for conducting business research. The goal of each chapter author--a leading authority in a particular subject area--is to provide an understanding of each method with a minimum of mathematical derivations. The chapters are organized within three general interrelated topics--Measurement, Decision Analysis, and Modeling. The chapters on measurement discuss generalizability theory, latent trait and latent class models, and multi-faceted Rasch modeling. The chapters on decision analysis feature applied location theory models, data envelopment analysis, and heuristic search procedures. The chapters on modeling examine exploratory and confirmatory factor analysis, dynamic factor analysis, partial least squares and structural equation modeling, multilevel data analysis, modeling of longitudinal data by latent growth curve methods and structures, and configural models of longitudinal categorical data.

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

D-scoring Method of Measurement presents a unified framework of classical and latent measurement referred to as D-scoring method of measurement (DSM). Provided are detailed descriptions of DSM procedures and illustrative examples of how to apply the DSM in various scenarios of measurement. The DSM is designed to combine merits of the traditional CTT and IRT for the purpose of transparency, ease of interpretations, computational simplicity of test scoring and scaling, and practical efficiency, particularly in large-scale assessments. Through detailed descriptions of DSM procedures, this book shows how practical applications of such procedures are facilitated by the inclusion of operationalized guidance for their execution using the computer program DELTA for DSM-based scoring, equating, and item analysis of test data. In doing so, the book shows how DSM procedures can be readily translated into computer source codes for other popular software packages such as R. D-scoring Method of Measurement equips researchers and practitioners in the field of educational and psychological measurement with a comprehensive understanding of the DSM as a unified framework of classical and latent scoring, equating, and psychometric analysis.

D-scoring Method of Measurement presents a unified framework of classical and latent measurement referred to as D-scoring method of measurement (DSM). Provided are detailed descriptions of DSM procedures and illustrative examples of how to apply the DSM in various scenarios of measurement. The DSM is designed to combine merits of the traditional CTT and IRT for the purpose of transparency, ease of interpretations, computational simplicity of test scoring and scaling, and practical efficiency, particularly in large-scale assessments. Through detailed descriptions of DSM procedures, this book shows how practical applications of such procedures are facilitated by the inclusion of operationalized guidance for their execution using the computer program DELTA for DSM-based scoring, equating, and item analysis of test data. In doing so, the book shows how DSM procedures can be readily translated into computer source codes for other popular software packages such as R. D-scoring Method of Measurement equips researchers and practitioners in the field of educational and psychological measurement with a comprehensive understanding of the DSM as a unified framework of classical and latent scoring, equating, and psychometric analysis.

This new edition provides a step-by-step guide to applying the Rasch model in R, a probabilistic model used by researchers across the social sciences to measure unobservable (“latent”) variables. Although the focus is on simple R code, the book provides updated guidance through the point-and-click menus of BlueSky Statistics software.The book covers all Rasch models frequently used in social sciences, from the Simple Rasch model to the Rating Scale, Partial Credit, and Many-Facets Rasch models. Using a pragmatic approach to model-data fit, this book offers helpful practical examples to investigate Rasch model assumptions. In addition to traditional Rasch model approaches, it introduces the Rasch model as a special case of a Generalized Mixed Effects Model. Readers will also benefit from the online support material which includes all the code used in the book in downloadable and useable files.It also provides a comprehensive guide to R programming and practical guidance on using BlueSky Statistics software's point-and-click menus. This dual approach enables readers to experiment with data analysis using the provided data sets, enhancing their understanding and application of statistical concepts. It will be a valuable resource for both students and researchers who want to use Rasch models in their research.

This new edition provides a step-by-step guide to applying the Rasch model in R, a probabilistic model used by researchers across the social sciences to measure unobservable (“latent”) variables. Although the focus is on simple R code, the book provides updated guidance through the point-and-click menus of BlueSky Statistics software.The book covers all Rasch models frequently used in social sciences, from the Simple Rasch model to the Rating Scale, Partial Credit, and Many-Facets Rasch models. Using a pragmatic approach to model-data fit, this book offers helpful practical examples to investigate Rasch model assumptions. In addition to traditional Rasch model approaches, it introduces the Rasch model as a special case of a Generalized Mixed Effects Model. Readers will also benefit from the online support material which includes all the code used in the book in downloadable and useable files.It also provides a comprehensive guide to R programming and practical guidance on using BlueSky Statistics software's point-and-click menus. This dual approach enables readers to experiment with data analysis using the provided data sets, enhancing their understanding and application of statistical concepts. It will be a valuable resource for both students and researchers who want to use Rasch models in their research.

This is the first book to illustrate the use of Network Analysis for the analysis of rating datasets. It uses a multi-disciplinary approach to focus on the quantification and visualization of rater effects for exploration, diagnosis, reporting, decision-making, and planning.Combining cutting-edge theories of rater effects with step-by-step practical examples using publicly available data and open-source code (R packages), this book provides an introduction to the concept of rating datasets. It uses simple language to discuss data requirements, model assumptions, and other important details, and it follows all theoretical sections with practical applications that analyze empirical data step-by-step. The book also demonstrates how the proposed methods can be integrated with more established approaches, such as Rasch models, to make the analysis of relevant data more efficient. With its accessible language and gentle introduction to graph theory and network analysis, this book is suitable for readers unfamiliar with the methodology. throughout the book, readers will become able to plan a rater-effects study, format the dataset, conduct the analysis, and report the findings. The data and code are available as supplementary online material to encourage readers to replicate the analysis and reflect on the results.This book will be of great interest to graduate students and researchers who conduct research on using ratings, and will also be an important reference for anyone studying measurement, research, and quantitative methods. Researchers and practitioners in the fields of testing services, awarding bodies and social sciences in general will also find this book useful

This is the first book to illustrate the use of Network Analysis for the analysis of rating datasets. It uses a multi-disciplinary approach to focus on the quantification and visualization of rater effects for exploration, diagnosis, reporting, decision-making, and planning.Combining cutting-edge theories of rater effects with step-by-step practical examples using publicly available data and open-source code (R packages), this book provides an introduction to the concept of rating datasets. It uses simple language to discuss data requirements, model assumptions, and other important details, and it follows all theoretical sections with practical applications that analyze empirical data step-by-step. The book also demonstrates how the proposed methods can be integrated with more established approaches, such as Rasch models, to make the analysis of relevant data more efficient. With its accessible language and gentle introduction to graph theory and network analysis, this book is suitable for readers unfamiliar with the methodology. throughout the book, readers will become able to plan a rater-effects study, format the dataset, conduct the analysis, and report the findings. The data and code are available as supplementary online material to encourage readers to replicate the analysis and reflect on the results.This book will be of great interest to graduate students and researchers who conduct research on using ratings, and will also be an important reference for anyone studying measurement, research, and quantitative methods. Researchers and practitioners in the fields of testing services, awarding bodies and social sciences in general will also find this book useful

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.

Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.

Intended to help improve measurement and data collection methods in the behavioral, social, and medical sciences, this book demonstrates an expanded and accessible use of Generalizability Theory (G theory). G theory conceptually models the way in which the reliability of measurement is ascertained. Sources of score variation are identified as potential contributors to measurement error and taken into account accordingly. The authors demonstrate the powerful potential of G theory by showing how to improve the quality of any kind of measurement, regardless of the discipline.Readers will appreciate the conversational style used to present a comprehensive review of G theory and its application using the freeware EduG. To maximize understanding the authors illustrate all fundamental principles with concrete examples from different fields and contexts. Annotated applications lead students through the main concepts of G theory, while illustrating both the use of EduG and interpretation of its output. Formulas are avoided wherever possible. Exercises with data sets available on the Psychology Press website allow readers to carry out their own analyses to reinforce understanding.Brief overviews of analysis of variance, estimation, and the statistical error model are provided for review. The procedures involved in carrying out a generalizability study using EduG follow, as well as guidance in the interpretation of results. Real-world applications of G theory to the assessment of depression, managerial ability, attitudes, and writing and mathematical skills are then presented. Next, annotated exercises provide an opportunity for readers to use EduG and interpret its results. The book concludes with a review of the development of G theory and possible new directions of application. Finally, for those with a strong statistical background, the appendixes provide the formulas used by EduG.Ideal as a supplement for courses on measurement theory and/or generalizability theory taught in departments of psychology, education, medicine, and the social sciences, this text will also appeal to researchers from a variety of fields interested in learning how to apply G theory to their studies.

Intended to help improve measurement and data collection methods in the behavioral, social, and medical sciences, this book demonstrates an expanded and accessible use of Generalizability Theory (G theory). G theory conceptually models the way in which the reliability of measurement is ascertained. Sources of score variation are identified as potential contributors to measurement error and taken into account accordingly. The authors demonstrate the powerful potential of G theory by showing how to improve the quality of any kind of measurement, regardless of the discipline.Readers will appreciate the conversational style used to present a comprehensive review of G theory and its application using the freeware EduG. To maximize understanding the authors illustrate all fundamental principles with concrete examples from different fields and contexts. Annotated applications lead students through the main concepts of G theory, while illustrating both the use of EduG and interpretation of its output. Formulas are avoided wherever possible. Exercises with data sets available on the Psychology Press website allow readers to carry out their own analyses to reinforce understanding.Brief overviews of analysis of variance, estimation, and the statistical error model are provided for review. The procedures involved in carrying out a generalizability study using EduG follow, as well as guidance in the interpretation of results. Real-world applications of G theory to the assessment of depression, managerial ability, attitudes, and writing and mathematical skills are then presented. Next, annotated exercises provide an opportunity for readers to use EduG and interpret its results. The book concludes with a review of the development of G theory and possible new directions of application. Finally, for those with a strong statistical background, the appendixes provide the formulas used by EduG.Ideal as a supplement for courses on measurement theory and/or generalizability theory taught in departments of psychology, education, medicine, and the social sciences, this text will also appeal to researchers from a variety of fields interested in learning how to apply G theory to their studies.

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced.Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to help researchers assess the predictive value of different combinations of variables in large data sets. Methodological findings and conceptual models that explain reliable EDM techniques for predicting and understanding various risk mechanisms are integrated throughout. Numerous examples illustrate the use of these techniques in practice. Contributors provide insight through hands-on experiences with their own use of EDM techniques in various settings. Readers are also introduced to the most popular EDM software programs. A related website at http://mephisto.unige.ch/pub/edm-book-supplement/offers color versions of the book’s figures, a supplemental paper to chapter 3, and R commands for some chapters. The results of EDM analyses can be perilous – they are often taken as predictions with little regard for cross-validating the results. This carelessness can be catastrophic in terms of money lost or patients misdiagnosed. This book addresses these concerns and advocates for the development of checks and balances for EDM analyses. Both the promises and the perils of EDM are addressed.Editors McArdle and Ritschard taught the "Exploratory Data Mining" Advanced Training Institute of the American Psychological Association (APA). All contributors are top researchers from the US and Europe. Organized into two parts--methodology and applications, the techniques covered include decision, regression, and SEM tree models, growth mixture modeling, and time based categorical sequential analysis. Some of the applications of EDM (and the corresponding data) explored include:selection to college based on risky prior academic profilesthe decline of cognitive abilities in older personsglobal perceptions of stress in adulthoodpredicting mortality from demographics and cognitive abilitiesrisk factors during pregnancy and the impact on neonatal developmentIntended as a reference for researchers, methodologists, and advanced students in the social and behavioral sciences including psychology, sociology, business, econometrics, and medicine, interested in learning to apply the latest exploratory data mining techniques. Prerequisites include a basic class in statistics.