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Factor Analysis, Structural Equation, Multilevel & Longitudinal Modeling

New Titles and Key Backlist 2008 ­ 2009


Heck & Thomas: An Introduction to Multilevel Modeling Techniques, Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Card et al.: Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Cohen: Applied Data Analytic Techniques for Turning Points Research . . . . 3 MacKinnon: Introduction to Statistical Mediation Analysis . . . . . . . . . . . . . 3 van de Vijver et al.: Multilevel Analysis of Individuals and Cultures . . . . . . 4 Little et al.: Modeling Contextual Effects in Longitudinal Studies . . . . . . . . 4 Cudeck & MacCallum: Factor Analysis at 100 . . . . . . . . . . . . . . . . . . . . . . 5 Blossfeld et al.: Event History Analysis With Stata . . . . . . . . . . . . . . . . . . . 5 van Montfort et al.: Longitudinal Models in the Behavioral and Related Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Duncan et al.: An Introduction to Latent Variable Growth Curve Modeling, Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Byrne: Structural Equation Modeling With EQS, Second Edition . . . . . . . . . 7 Raykov & Marcoulides: A First Course in Structural Equation Modeling, Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Schumacker & Lomax: A Beginner's Guide to Structural Equation Modeling, Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Loehlin: Latent Variable Models, Fourth Edition . . . . . . . . . . . . . . . . . . . . . 8 Hox: Multilevel Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Byrne: Structural Equation Modeling With AMOS . . . . . . . . . . . . . . . . . . . . 8 Byrne: Structural Equation Modeling With LISREL, PRELIS, and SIMPLIS . . 9 Raykov & Marcoulides: An Introduction to Applied Multivariate Analysis . . 9 Todman & Dugard: Approaching Multivariate Analysis . . . . . . . . . . . . . . . . 9 Multivariate Behavioral Research Journal . . . . . . . . . . . . . . . . . . . . . . . . 10 Structural Equation Modeling Journal . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

This brochure includes titles formerly published by Lawrence Erlbaum Associates (LEA).


An Introduction to Multilevel Modeling Techniques

Second Edition Ronald H. Heck, University of Hawaii at Manoa, USA Scott L. Thomas, University of Georgia, USA

Series: Quantitative Methodology

"The new edition by Heck and Thomas offers an expanded introduction to the popular statistical method of multilevel data modeling. By providing real data sets and completely updated computer examples analyzed with programs like HLM, LISREL, and Mplus, the book offers readers the latest information, steps, and procedures needed to competently conduct multilevel analyses." - George Marcoulides, University of California, Riverside Featuring a comprehensive, applied approach to multilevel analysis, this book is distinguished by its wide range of applications relevant to the behavioral, educational, organizational, and social sciences. Both univariate and multivariate models are used to promote a greater understanding for designing studies and analyzing data. The text emphasizes how the conceptual and methodological approaches underpinning a study are an essential part of the research design. Readers are encouraged to consider the questions and theoretical orientation underlying their study, the structure of the data, and the strengths and limitations of each technique before selecting their approach. Numerous examples of cross-sectional and longitudinal data are provided along with related exercises. Examples of input programs from HLM (Version 6) and Mplus (Version 4) demonstrate how to set up and run the models. A website with datasets and program setups in HLM, Mplus, and LISREL (version 8) for the book's examples and exercises is provided. Readers can also use MLwin, Stata, and SAS to conduct most of the analyses in the book. The text emphasizes a latent variable conceptual framework for exploring hierarchical data. This framework allows the authors to show the commonality of the various approaches to multilevel modeling. Part 1 is devoted to conceptual issues while the remainder of the book features increasingly popular multilevel techniques including the univariate multilevel model, the multilevel measurement model, multilevel structural equation models, longitudinal multilevel models, and continuous and categorical latent variable mixture models. Introducing a systematic approach to the conceptual issues at the beginning of the book helps to make each technique covered in Part 2 more accessible. Conceptual and computational problems at the end of each chapter allow readers to test their understanding of the techniques. The new edition features: · New chapters on multilevel longitudinal analysis and on multilevel models · 80% new exercises and examples · A website with datasets and program setups in HLM, Mplus, and LISREL · A greater emphasis on demonstrating how multilevel modeling techniques are used to examine changes in individuals and organizations over time. Ideal for introductory graduate-level courses on multilevel and/or latent variable modeling, this book is intended for students and researchers in fields such as psychology, business, education, health, and sociology interested in understanding the advantages (and pitfalls) of multilevel modeling. Prerequisites include an introduction to data analysis and first-year univariate statistics. A companion website is available at CONTENTS

1. Introduction. 2. Investigating Organizational Structures, Processes, and Outcomes. 3. The Development of Multilevel Modeling Techniques. 4. Multilevel Regression Models. 5. Defining Multilevel Latent Variables. 6. Multilevel Structural Equation Models. 7. Methods for Examining Individual and Organizational Change. 8. Multilevel Models with Categorical Variables. 9. Afterword.

Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences

Noel A. Card, University of Arizona, USA; James P. Selig, Todd D. Little, University of Kansas, USA (Eds.)

"There are relatively few guides for researchers who explore the interdependence of human functioning. ... This book clearly rectifies that limitation. ... This book ... is ... of great value to many psychologists ... [and] for doctoral seminars in developmental psychology or biostatistics. ... I highly recommend this book. ... It is ground-breaking." - Theresa Thorkildsen, University of Illinois, Chicago This book reviews methods of conceptualizing, measuring, and analyzing interdependent data in developmental and behavioral sciences. Quantitative and developmental experts describe best practices for modeling interdependent data that stem from interactions within families, relationships, and peer groups, for example. Complex models for analyzing longitudinal data, such as growth curves and time series, are also presented. Many contributors are innovators of the techniques and all are able to clearly explain the methodologies and their practical problems, including issues of measurement, missing data, power and sample size, and the specific limitations of each method. Featuring a balance between analytic strategies and applications, the book addresses: · The Actor-Partner Interdependence Model for analyzing influence between two individuals · The Intraclass Correlational Approach for analyzing distinguishable roles (parentchild) or exchangeable (same-sex) dyadic data · The Social Relations Model for analyzing group interdependency · Social Network Analysis approaches for relationships between individuals. This book is intended for graduate students and researchers across the developmental, social, behavioral, and educational sciences. It is an excellent research guide and a valuable resource for advanced methods courses. CONTENTS

N.A. Card, T.D. Little, J.P. Selig, Modeling Dyadic and Interdependent Data in Developmental Research: An Introduction. B. Laursen, D. Popp, W.J. Burk, M. Kerr, H. Stattin, Incorporating Interdependence into Developmental Research: Examples from the Study of Homophily and Homogeneity. W.L. Cook, Application of the Social Relations Model Formulas to Developmental Research. A.H.N. Cillessen, C. Borch, Analyzing Social Networks in Adolescence. N. Ram, A.B. Pedersen, Dyadic Models Emerging from the Longitudinal Structural Equation Modeling Tradition: Parallels with Ecological Models of Interspecific Interactions. E. Ferrer, K.F. Widaman, Multilevel Structural Equation Models for Contextual Factors with Inter-group Differences. P. Sadler, E. Woody, It Takes Two: A Dyadic, SEM-based Perspective on Personality Development. D.A. Kashy, M.B. Donnellan, Comparing MLM and SEM Approaches to Analyzing Developmental Dyadic Data: Growth Curve Models of Hostility in Families. J.P. Selig, K.A. McNamara, N.A. Card, T.D. Little, Techniques for Modeling Dependency in Interchangeable Dyads. T.E. Malloy, A.H.N. Cillessen, Variance Component Analysis of Generalized and Dyadic Peer Perceptions in Adolescence. N.A. Card, T.D. Little, J.P. Selig, Using the Bivariate Social Relations Model to Study Dyadic Relationships: Early Adolescents' Perceptions of Friends' Aggression and Prosocial Behavior. S.J.T. Branje, C. Finkenauer, W.H.J. Meeus, Modeling Interdependence Using the Social Relations Model: The Investment Model in Family Relationships. J. Templin, Methods for Detecting Subgroups in Social Networks. T.A. Kindermann, Can We Use Causal Inferences about the Influence of Children's Naturally-existing Social Networks on Their School Motivation? B.J.H. Zijlstra, R. Veenstra, M.A.J. Van Duijn, An Application of the Multilevel Model for Binary Network Data on Bully-Victim Relationships. C.F. Bond, Jr., D. Cross, Beyond the Dyad: Prospects for Social Development. D.A. Kenny, Thinking about the Developmental Course of Relationships.

Applied Data Analytic Techniques for Turning Points Research

Patricia Cohen (Ed.) Columbia University, New York State Psychiatric Institute, USA

Series: Multivariate Applications

"The volume will ... make a superb contribution." - Lisa L. Harlow, University of Rhode Island This innovative volume demonstrates the use of a range of statistical approaches that examine "turning points" (a change in direction, magnitude, or meaning) in real data. Analytic techniques are illustrated with real longitudinal data from a variety of fields. As such the book will appeal to a variety of researchers, including: · Developmental researchers interested in identifying factors precipitating turning points at various life stages · Medical or substance abuse researchers looking for turning points in disease or recovery · Social researchers interested in estimating the effects of life experiences on subsequent behavioral changes · Interpersonal behavior researchers looking to identify turning points in relationships · Brain researchers needing to discriminate the onset of an experimentally produced process in a participant. The book opens with the goals and theoretical considerations in defining turning points. An overview of the methods presented in subsequent chapters is then provided. Chapter goals include discriminating "local" from long-term effects, identifying variables altering the connection between trajectories at different life stages, locating non-normative turning points, coping with practical distributional problems in trajectory analyses, and changes in the meaning and connections between variables in the transition to adulthood. From an applied perspective, the book explores such topics as antisocial/aggressive trajectories at different life stages, the impact of imprisonment on criminal behavior, family contact trajectories in the transition to adulthood, sustained effects of substance abuse, alternative models of bereavement, and identifying brain changes associated with the onset of a new brain process. This volume is ideal for advanced students and researchers interested in identifying significant change in data in a variety of fields including psychology, medicine, education, political science, criminology, and sociology. CONTENTS

P. Cohen, Turning Points: Theoretical Considerations, Research Designs, and a Preview of the Book. D.S. Nagin, T. Barker, E. Lacourse, R.E. Tremblay, The Interrelationship of Temporally Distinct Risk Markers and the Transition From Childhood Physical Aggression to Adolescent Violent Delinquency. C. Wimer, R.J. Sampson, J.H. Laub, Estimating Time-varying Causes and Outcomes, with Application to Incarceration and Crime. D. Rindskopf, J.R.Sneed, Turning Points in Family Contact During Emerging Adulthood. A.M. Hussong, P.J. Curran, T.E. Moffitt, A. Caspi, Testing Turning Points Using Latent Growth Curve Models: Competing Models of Substance Abuse and Desistance in Young Adulthood. J.J. McArdle, L. Wang, Modeling Agebased Turning Points in Longitudinal Life-span Growth Curves of Cognition. C.T. Burke, P.E. Shrout, N. Bolger, Bereavement as a Potential Turning Point: Modeling Between-person Variability in Adjustment to Conjugal Loss. M. Lindquist, T.D. Wager, Application of Change-point Theory to Modeling State-related Activity in fMRI. H. Chen, P. Cohen, K. Gordon, Using an Econometric Model of Change Points to Locate Turning Points in Individual Time Series. P. Cohen, K. Gordon, S. Kasen, H. Chen, Developmental Structural Change in the Maturity of Role Assumption.

Introduction to Statistical Mediation Analysis

David MacKinnon Arizona State University, USA

Series: Multivariate Applications

"Dave MacKinnon's book will be a welcome addition to the field. The topic of statistical mediation is important for researchers who want to examine models more complex than simple prediction." - Lisa L. Harlow, University of Rhode Island "Overall, I found these chapters to be uniformly excellent. The text was well written, nicely organized, and technically rigorous while remaining broadly accessible." - Patrick Curran, University of North Carolina, Chapel Hill This volume introduces the statistical, methodological, and conceptual aspects of mediation analysis. Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout. Single-mediator, multilevel, and longitudinal models are reviewed. The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations. Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions). The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, Mplus and CALIS, and a program to simulate the model. The notation used is consistent with existing literature on mediation in psychology. The book opens with a review of the types of research questions the mediation model addresses. Part 2 describes the estimation of mediation effects, including assumptions, statistical tests, and the construction of confidence limits. Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described. The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions. Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology. Some exposure to a graduate-level research methods or statistics course is assumed. The overview of mediation analysis and the guidelines for conducting a mediation analysis will be appreciated by all readers. CONTENTS

Preface. Introduction. 1. Applications of the Mediation Model. 2. Single Mediator Model. 3. Single Mediator Model Details. 4. Multiple Mediator Model. 5. Path Analysis Mediation Models. 6. Latent Variable Mediation Models. 7. Longitudinal Mediation Models. 8. Multilevel Mediation Models. 9. Mediation and Moderation. 10. Mediation in Categorical Data Analysis. 11. Computer Intensive Methods for Mediation Models. 12. Causal Inference for Mediation Models. 13. Additional Approaches to Identifying Mediating Variables. Conclusions and Future Directions. Appendices: Answers to Odd-numbered Exercises. Notation.

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Multilevel Analysis of Individuals and Cultures

Fons J.R. van de Vijver, Tilburg University, The Netherlands; Dianne A. van Hemert, University of Amsterdam, The Netherlands; Ype H. Poortinga, Emeritus Professor, Tilburg University, The Netherlands and University of Leuven, Belgium (Eds.)

"This book will have broad appeal to many scholars. ... Cross-cultural psychology is a hot topic and this book would be a great asset for educators in psychology, sociology, education, and cultural psychology. ... This is a terrific book with many strengths." - Todd Little, University of Kansas In this book, top specialists address theoretical, methodological, and empirical multilevel models as they relate to the analysis of individual and cultural data. Divided into four parts, the book opens with the basic conceptual and theoretical issues in multilevel research, including the fallacies of such research. Part 2 describes the methodological aspects of multilevel research, including data-analytic and structural equation modeling techniques. Applications and models from various research areas, including control, values, organizational behavior, social beliefs, well-being, personality, response styles, school performance, family, and acculturation, are explored in Part 3. This section also deals with validity issues in aggregation models. The book concludes with an overview of the kinds of questions addressed in multilevel models and highlights the theoretical and methodological issues yet to be explored. This book is intended for researchers and advanced students in psychology, sociology, social work, marriage and family therapy, public health, anthropology, education, economics, political science, and cultural and ethnic studies who study the relationship between behavior and culture. CONTENTS

Preface. Part 1. Conceptual Issues. F.J.R. van de Vijver, D.A. van Hemert, Y.H. Poortinga, Conceptual Issues in Multilevel Models. J. Adamopoulos, On the Entanglement of Culture and Individual Behavior. Part 2. Methodological Issues. J.R.J. Fontaine, Traditional and Multilevel Approaches in Cross-cultural Research: An Integration of Methodological Frameworks. J.P. Selig, N.A. Card, T.D. Little, Latent Variable Structural Equation Modeling in Cross-cultural Research: Multigroup and Multilevel Approaches. Part 3. Multilevel Models and Applications. S. Yamaguchi, T. Okumura, H.F. Chua, H. Morio, J.F. Yates, Levels of Control Across Cultures: Conceptual and Empirical Analysis. D. Oyserman, A.K. Uskul, Individualism and Collectivism: Societal-level Processes with Implications for Individual-level and Society-level Outcomes. R. Fischer, Multilevel Approaches in Organizational Settings: Opportunities, Challenges and Implications for Cross-cultural Research. K. Leung, M.H. Bond, Psycho-logic and Eco-logic: Insights from Social Axiom Dimensions. R.E. Lucas, E. Diener, Can We Learn About National Differences in Happiness From Individual Responses? A Multilevel Approach. R.R. McCrae, A. Terracciano, The Fivefactor Model and Its Correlates in Individuals and Cultures. P.B. Smith, R. Fischer, Acquiescence, Extreme Response Bias and Culture: A Multilevel Analysis. P. Stanat, O. Ladtke, Multilevel Issues in International Large-scale Assessment Studies on Student Performance. K. Mylonas, V. Pavlopoulos, J. Georgas, Multilevel Structure Analysis for Family-related Constructs. B. Nauck, Acculturation. Part 4. Integration. D.A. van Hemert, F.J.R. van de Vijver, Y.H. Poortinga, Multilevel Models of Individuals and Cultures: Current State and Outlook.

Modeling Contextual Effects in Longitudinal Studies

Todd D. Little, University of Kansas, USA; James A. Bovaird, University of Nebraska, USA; Noel A. Card, University of Arizona, USA (Eds.)

Modeling Contextual Effects in Longitudinal Studies reviews the challenges and alternative approaches to modeling how individuals change across time, and provides methodologies and data analytic strategies for behavioral and social science researchers. This accessible guide provides concrete, clear examples of how contextual factors can be included in most research studies. Each chapter can be understood independently, allowing readers to first focus on areas most relevant to their work. The opening chapter demonstrates the various ways contextual factors are represented ­ as covariates, predictors, outcomes, moderators, mediators, or mediated effects. Succeeding chapters review "best practice" techniques for treating missing data, making model comparisons, and scaling across developmental age ranges. Other chapters focus on specific statistical techniques such as multilevel modeling and multiple-group and multilevel SEM, and how to incorporate tests of mediation, moderation, and moderated mediation. Critical measurement and theoretical issues are discussed, particularly how age can be represented and the ways in which context can be conceptualized. The final chapter provides a compelling call to include contextual factors in theorizing and research. This book will appeal to researchers and advanced students conducting developmental, social, clinical, or educational research, as well as those in related areas such as psychology and linguistics. CONTENTS

Preface. N.A. Card, T.D. Little, J.A. Bovaird, Modeling Ecological and Contextual Effects in Longitudinal Studies of Human Development. S.M. Hofer, L. Hoffman, Statistical Analysis with Incomplete Data: A Developmental Perspective. K.J. Preacher, L. Cai, R.C. MacCullum, Alternatives to Traditional Model Comparison Strategies for Covariance Structure Models. S.E. Embretson, Impact of Measurement Scale in Modeling Developmental Processes and Ecological Factors. P.J. Curran, M.C. Edwards, R.J. Wirth, A.M. Hussong, L. Chassin, The Incorporation of Categorical Measurement Models in the Analysis of Individual Growth. T.D. Little, N.A. Card, D.W. Slegers, E.C. Ledford, Representing Contextual Effects in Multiple-group MACS Models. J.A. Bovaird, Multilevel Structural Equation Models for Contextual Factors. D. Hedeker, R.J. Mermelstein, Mixed-effects Regression Models with Heterogeneous Variance: Analyzing Ecological Momentary Assessment (EMA) Data of Smoking. T.D. Little, N.A. Card, J.A. Bovaird, K.J. Preacher, C.S. Crandel, Structural Equation Modeling of Mediation and Moderation with Contextual Factors. D.B. Flora, S.T. Khoo, L. Chassin, Moderating Effects of a Risk Factor: Modeling Longitudinal Moderated Mediation in the Development of Adolescent Heavy Drinking. D.J. Bauer, M.J. Shanahan, Modeling Complex Interactions: Person-centered and Variablecentered Approaches. N. Bolger, P.E. Shrout, Accounting for Statistical Dependency in Longitudinal Data on Dyads. S.M. Boker, J.-P. Laurenceau, Coupled Dynamics and Mutually Adaptive Context. N. Ram, J.R. Nesselroade, Modeling Intraindividual and Intracontextual Change: Rendering Developmental Contextualism Operational. J.L. Rodgers, The Shape of Things to Come: Using Developmental Curves From Adolescent Smoking and Drinking Reports to Diagnose the Type of Social Process that Generated the Curves. K.J. Grimm, J.J. McArdle, A Dynamic Structural Analysis of the Impacts of Context on Shifts in Lifespan Development. K.F. Widaman, Intrauterine Environment Affects Infant and Child Intellectual Outcomes: Environment as Direct Effect. H. Jelicic, C. Theokas, E. Phelps, R.M. Lerner, Conceptualizing and Measuring the Context within Person Context Models of Human Development: Implications for Theory, Research, and Application.

Factor Analysis at 100

Historical Developments and Future Directions Robert Cudeck, Ohio State University, USA Robert C. MacCallum, University of North Carolina at Chapel Hill, USA (Eds.)

This book provides a retrospective look at major developments as well as a prospective view of future directions in factor analysis. In so doing, it demonstrates how and why factor analysis is considered to be one of the methodological pillars of behavioral research. Featuring an outstanding collection of contributors, this volume offers unique insights on factor analysis and its related methods. The book reviews some of the extensions of factor analysis to such techniques as latent growth curve models, models for categorical data, and structural equation models. Intended for graduate students and researchers in the behavioral, social, health, and biological sciences who use this technique in their research, a basic knowledge of factor analysis is required and a working knowledge of linear algebra is helpful. CONTENTS

Preface. D. Bartholomew, Three Faces of Factor Analysis. L. Jones, Remembering L.L. Thurstone. R.D. Bock, Rethinking Thurstone. K. Jöreskog, Factor Analysis and Its Extensions. K. Bollen, On the Origins of the Latent Curve Model in the Factor Analysis and Growth Curve Traditions. J. McArdle, Factor Analysis of Longitudinal Repeated Measures Data. R. Millsap, W. Meredith, Factorial Invariance: Historical Trends and New Problems. R. MacCallum, M.W. Browne, L. Cai, Factor Analysis Models as Approximations. K.F. Widaman, Common Factors vs. Components: Principals and Principles, Errors, and Misconceptions. J. Horn, Understanding Human Intelligence: Where Have We Come Since Spearman? J.R. Nesselroade, Factoring at the Individual Level: Some Matters for the Second Century of Factor Analysis. M.W. Browne, G. Zhang, Developments in the Factor Analysis of Individual Time Series. I. Moustaki, Factor Analysis and Latent Structure of Categorical and Metric Data. R. Jennrich, Rotation Methods, Algorithms, and Standard Errors. M. Wall, Y. Amemiya, A Review of Nonlinear Factor Analysis and Nonlinear Structural Equation Modeling.

Event History Analysis With Stata

Hans-Peter Blossfeld, Otto-Friedrich Universität Bamberg, Germany; Katrin Golsch, University of Cologne, Germany; Götz Rohwer, RuhrUniversität Bochum, Germany

Event History Analysis With Stata provides an introduction to event history modeling techniques using Stata (version 9), a widely used statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. The authors illustrate the entire research path required in the application of eventhistory analysis, from the initial problems of recording event-oriented data, to data organization, to applications using the software, to the interpretation of results. The book also demonstrates, through example, how to implement hypotheses tests and how to choose the right model. The strengths and limitations of various techniques are emphasized in each example, along with an introduction to the model, details on how to input data, and the related Stata commands. Each application is accompanied by a brief explanation of the underlying statistical concept. Readers are offered the unique opportunity to easily run and modify all of the book's application examples on a computer, by visiting the author's website at Examples include survival rates of patients in medical studies; unemployment periods in economic studies; and the time it takes a criminal to break the law after his release in a criminological study. This book supplements Event History Analysis, by Blossfeld et al. (1988) and Techniques of Event History Modeling, by Blossfeld and Rohwer (2001), extending their coverage of practical applications and statistical theory. Intended for researchers in a variety of fields such as statistics, economics, psychology, sociology, and political science, Event History Analysis With Stata also serves as a text, in combination with the authors' other two books, for courses on event history analysis. CONTENTS

Preface. Introduction. 1. Event History Data Structures. 2. Nonparametric Descriptive Methods. 3. Exponential Transition Rate Models. 4. Piecewise Constant Exponential Models. 5. Exponential Models With Time-dependent Covariates. 6. Parametric Models of Time-dependence. 7. Methods to Check Parametric Assumptions. 8. Semiparametric Transition Rate Models. 9. Problems of Model Specification.

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Invitation to Authors

Psychology Press is the leading international publisher of textbooks and handbooks in Research Methods & Statistics. If you are planning to write a textbook, handbook or monograph, we would like to hear from you. Visit

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Longitudinal Models in the Behavioral and Related Sciences

Kees van Montfort, Free University Amsterdam, The Netherlands; Johan Oud, Radbound University Nijmegen, The Netherlands; Albert Satorra, Universitat Pompeu Fabra, Barcelona, Spain (Eds.)

Series: European Association for Methodology

This volume reviews longitudinal models and analysis procedures for use in the behavioral and social sciences. Written by distinguished experts in the field, the book presents the most current approaches and theories, and the technical problems that may be encountered along the way. Readers will find new ideas about the use of longitudinal analysis in solving problems that arise due to the specific nature of the research design and the data available. Longitudinal Models in the Behavioral and Related Sciences opens with the latest theoretical developments. In particular, the book addresses situations that arise due to the categorical nature of the data, issues related to state space modeling, and potential problems that may arise from network analysis and/or growth-curve data. The focus of part two is on the application of longitudinal modeling in a variety of disciplines. The book features applications such as heterogeneity on the patterns of a firm's profit, on house prices, and on delinquent behavior; non-linearity in growth in assessing cognitive aging; measurement error issues in longitudinal research; and distance association for the analysis of change. Part two clearly demonstrates the caution that should be taken when applying longitudinal modeling as well as in the interpretation of the results. This volume is ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience. CONTENTS

Preface. Part 1. Theoretical Developments. A. Mooijaart, K. van Montfort, Latent Markov Models for Categorical Variables and Time-dependent Covariates. J. Oud, Comparison of Four Procedures to Estimate the Damped Linear Differential Oscillator for Panel Data. T. Snijders, C. Steglich, M. Schweinberger, Modeling the Coevolution of Networks and Behavior. H. Singer, Stochastic Differential Equation Models with Sampled Data. S.-M. Chow, Factor Score and Parameter Estimations in Nonlinear Dynamical Systems Models. J. Vermunt, Growth Models for Categorical Response Variables: Standard, Latent-class, and Hybrid Approaches. J.J. McArdle, Dynamic Structural Equation Modeling in Longitudinal Experimental Studies. S. Blozis, A Second-order Structured Latent Curve Model for Longitudinal Data. Part 2. Applications. J.-C. Bou, A. Satorra, Patterns of Persistence of Abnormal Returns: A Finite Mixture Distribution Approach. J. Reinecke, The Development of Deviant and Delinquent Behavior of Adolescents: Applications of Latent Class Growth Curves and Growth Mixtures Models. K.J. Grimm, J.J. McArdle, F. Hamagami, Nonlinear Growth Mixture Models in Research on Cognitive Aging. U. Engel, A. Gattig, J. Simonson, Longitudinal Multilevel Modelling: A Comparison of Growth Curve Models and Structural Equation Modelling Using Panel Data From Germany. E. Schlueter, E. Davidov, P. Schmidt, Applying Autoregressive Cross-lagged and Latent Growth Curve Models to a Three-wave Panel Study. I. Visser, V. Schmittmann, M.E.J. Raijmakers, Markov Process Models for Discrimination Learning. M. de Rooij, The Use of Covariates in Distance Association Models for the Analysis of Change. A. Scherpenzeel, W. Saris, Multitrait-Multimethod Models for Longitudinal Research. N. Longford, I. McCarthy, G. Dowse, Patterns of House-price Inflation in New Zealand.

An Introduction to Latent Variable Growth Curve Modeling

Concepts, Issues, and Applications Second Edition Terry E. Duncan, Susan C. Duncan, Lisa A. Strycker, Oregon Research Institute, Eugene, USA

Series: Quantitative Methodology

"An excellent mid-level text on latent growth modeling. ... [The graphics] are arguably better than those in any other book on any aspect of SEM. They are crisp and clear, and the inclusion of the theoretical weights is greatly helpful in understanding the graphics." - Lee Sechrest, University of Arizona "I initially learned growth modeling from this book, and I am still a big fan. It allowed me to jump in and get my feet wet with this flexible analytic method ... The text really is just as its title suggests ­ an introduction ­ and an excellent one at that." - James A. Bovaird, University of Kansas 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, on growth modeling with ordered categorical variables, on growth mixture modeling, and on pooled interrupted time series LGM approaches. Completely reorganized, 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. CONTENTS

Preface. Introduction. 1. Specification of the LGM. 2. LGM, Repeated Measures ANOVA, and the Mixed Linear Model. 3. Multivariate Representations of Growth and Development. 4. Analyzing Growth in Multiple Populations. 5. Accelerated Designs. 6. Multilevel Longitudinal Approaches. 7. Growth Mixture Modeling. 8. Piecewise and Pooled Interruped Time Series LGMs. 9. Latent Growth Curve Modeling With Categorical Variables. 10. Missing Data Models. 11. Latent Variable Framework for LGM Power Estimation. 12. Testing Interaction Effects in LGMs. Summary.

Structural Equation Modeling With EQS

Basic Concepts, Applications, and Programming Second Edition Barbara Byrne University of Ottawa, Canada

Series: Multivariate Applications

"Byrne's most recent book on SEM, a second edition to her book using the EQS program, continues to live up to the high expectations fostered by the previous books in being simple, nontechnical, and based on real-world applications. ... Recommend this as the first resource for those using EQS in their modeling endeavors." - PsycCRITIQUES Readers who want a less mathematical alternative to the EQS manual will find exactly what they're looking for in this practical text. Written specifically for those with little to no knowledge of structural equation modeling (SEM) or EQS, the author's goal is to provide a non-mathematical introduction to the basic concepts of SEM by applying these principles to EQS, Version 6.1. The book clearly demonstrates a wide variety of SEM/EQS applications that include confirmatory factor analytic and full latent variable models. Written in a user-friendly style, the author "walks" the reader through the varied steps involved in the process of testing SEM models: model specification and estimation, assessment of model fit, EQS output, and interpretation of findings. Each of the book's applications is accompanied by a statement of the hypothesis being tested, a schematic representation of the model, explanations of the EQS input and output files, tips on how to use the pull-down menus, and the data file upon which the application is based. The book carefully works through applications starting with relatively simple single group analyses, through to more advanced applications, such as a multi-group, latent growth curve, and multilevel modeling. The second edition features: · Many new applications that include a latent growth curve model, a multilevel model, a second-order model based on categorical data, a missing data multigroup model based on the EM algorithm, and the testing for latent mean differences related to a higher-order model · A CD enclosed with the book that includes all application data · Vignettes illustrating procedural and/or data management tasks · Description of how to build models both interactively using the BUILD-EQ interface and graphically using the EQS Diagrammer. CONTENTS

Preface. Part 1. Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part 2. Single-group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part 3. Multiple-group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-order CFA Model). Application 8: Testing for Latent Mean Differences (Second-order CFA Model). Part 4. Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-level Variance: The Multilevel Model.

A First Course in Structural Equation Modeling

Second Edition Tenko Raykov, Michigan State University, USA George A. Marcoulides, University of California, Riverside, USA

Praise for the First Edition:

"The book is ideal for individuals interested in learning the basics about SEM, evaluating research that employs SEM, and perhaps even performing SEM in subsequent research." - Measurement in Physical Education and Exercise Science "Raykov and Marcoulides have added to the SEM library a text that does a serviceable job of introducing the student or researcher to the basic fundamentals of modeling." - Structural Equation Modeling In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the Second Edition include: · Review of latent change (growth) analysis models at an introductory level · Coverage of the popular Mplus program · Updated examples of LISREL and EQS · A CD that contains all of the text's LISREL, EQS, and Mplus examples. A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful. CONTENTS

Preface. 1. Fundamentals of Structural Equation Modeling. 2. Getting to Know the EQS, LISREL, and Mplus Programs. 3. Path Analysis. 4. Confirmatory Factor Analysis. 5. Structural Regression Models. 6. Latent Change Analysis. Epilogue.

March 2006: 6x9: 248pp Hb with CD: 978-0-8058-5587-6:9 5 . 8 4 £ $79.95 Pb with CD: 978-0-8058-5588-3:5 0 . 1 2 £ $34.50 · 60-day examination copy available

October 2006: 6x9: 464pp Hb: 978-0-8058-5913-3:9 5 . 2 8 £ $135.00 Pb: 978-0-8058-6168-6:9 5 . 7 2 £ $45.00 · 60-day examination copy available

May 2006: 6x9: 272pp Hb with CD: 978-0-8058-5546-3:9 5 . 5 4 £ $75.00 Pb with CD: 978-0-8058-5547-0:5 0 . 4 2 £ $39.95 · 60-day examination copy available

February 2006: 6x9: 456pp Hb with CD: 978-0-8058-4125-1:0 . 5 £ $99.95 Pb with CD: 978-0-8058-4126-8:5 0 . 3 2 £ $49.95 · 60-day examination copy available

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A Beginner's Guide to Structural Equation Modeling

Second Edition Randall E. Schumacker, University of Alabama, USA Richard G. Lomax, Ohio State University, USA

The second edition features: · A CD with all of the book's Amos, EQS, and LISREL programs and data sets · New chapters on importing data issues related to data editing and on how to report research · An updated introduction to matrix notation and programs that illustrate how to compute these calculations · Many more computer program examples and chapter exercises · Increased coverage of factors that affect correlation, the 4-step approach to SEM and hypothesis testing, significance, power, and sample size issues. This edition's expanded use of applications make this book ideal for advanced students and researchers in psychology, education, business, health care, political science, sociology, and biology. A basic understanding of correlation is assumed and an understanding of the matrices used in SEM models is encouraged. June 2004: 6x9: 304pp Hb with CD: 978-0-8058-4017-9:5 0 . 3 7 £ $135.00 Pb with CD: 978-0-8058-4018-6:9 5 . 3 £ $59.95 · 60-day examination copy available BESTSELLER!

Multilevel Analysis

Techniques and Applications Joop Hox Utrecht University, The Netherlands

Series: Quantitative Methodology

This book is an introduction to multilevel analysis for applied researchers, featuring models for hierarchical or nested data. This book presents two types of model: the multilevel regression and multilevel covariance structures models. Despite the book being an introduction, it includes a discussion of many extensions and special applications. As an introduction, it will be useable in courses in a variety of fields, such as psychology, education, sociology, and business. The various extensions and special applications make it useful to researchers who work in applied or theoretical research, and to methodologists who have to consult with these researchers. The basic models and examples are discussed in non-technical terms; the emphasis is on understanding the methodological and statistical issues involved in using these models. The book will be useful as an introduction and as a standard reference for a large variety of applications. April 2002: 6x9: 320pp Hb: 978-0-8058-3218-1:5 0 . 5 £ $99.95 Pb: 978-0-8058-3219-8:5 0 . 4 2 £ $45.00 · 60-day examination copy available BESTSELLER!

Structural Equation Modeling With LISREL, PRELIS, and SIMPLIS

Basic Concepts, Applications, and Programming Barbara Byrne University of Ottawa, Canada

Series: Multivariate Applications

This book shows how easily various features of LISREL 8 and PRELIS 2 can be implemented in addressing research questions that lend themselves to SEM. Its purpose is threefold: firstly, to present a nonmathematical introduction to basic concepts associated with SEM; secondly, to demonstrate basic applications of SEM using both the DOS and Windows versions of LISREL 8, as well as both the LISREL and SIMPLIS lexicons; and thirdly, to highlight particular features of the LISREL 8 and PRELIS 2 progams that address important caveats related to SEM analyses. This book is intended neither as a text on the topic of SEM, nor as a comprehensive review of the many statistical functions available in the LISREL 8 and PRELIS 2 programs. Rather, the intent is to provide a practical guide to SEM using the LISREL approach. As such, the reader is "walked through" a diversity of SEM applications that include both factor analytic and full latent variable models, as well as a variety of data management procedures. March 1998: 6x9: 432pp Hb: 978-0-8058-2924-2:9 5 . 7 2 £ $49.95 · 60-day examination copy available

Also of Interest

An Introduction to Applied Multivariate Analysis

Tenko Raykov, Michigan State University, USA George Marcoulides, University of California, Riverside, USA

This comprehensive new text introduces readers to the most commonly used multivariate techniques at an introductory, nontechnical level. By focusing on the fundamentals, readers are better prepared for more advanced applied pursuits, particularly on topics that are most critical to the behavioral, social, and educational sciences. Analogies between the already familiar univariate statistics and multivariate statistics are emphasized throughout. The authors examine in detail how each multivariate technique can be implemented using SPSS and SAS and Mplus in the book's later chapters. Important assumptions are discussed along the way along with tips for how to deal with pitfalls the reader may encounter. Mathematical formulas are used only for definitional meaning rather than as proofs. The book is distinguished by its use of latent variable modeling to address multivariate questions specific to behavioral and social scientists including missing data analysis and longitudinal data modeling. Ideal for graduate and advanced undergraduate students in the behavioral, social, and educational sciences, this book will also appeal to researchers in these disciplines who have limited familiarity with multivariate statistics. Recommended prerequisites include an introductory statistics course with exposure to regression analysis and some familiarity with SPSS and SAS.

March 2008: 6x9: 496pp Hb: 978-0-8058-6375-8:0 . 0 5 £ $89.95 · 60-day examination copy available

Latent Variable Models

An Introduction to Factor, Path, and Structural Equation Analysis Fourth Edition John C. Loehlin University of Texas, Austin, USA

This book introduces multiple latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: · A data CD that features the correlation and covariance matrices used in the exercises · New sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams · Reorganization of chapters 3-7 enhance the flow of the book and its flexibility for teaching. Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful. January 2004: 6x9: 336pp Hb: 978-0-8058-4909-7:5 0 . 1 6 £ $110.00 Pb: 978-0-8058-4910-3:5 0 . 6 2 £ $45.00 · 60-day examination copy available

Structural Equation Modeling With AMOS

due in Fall 2009!

New edition

Approaching Multivariate Analysis

An Introduction for Psychology John Todman, University of Dundee, UK Pat Dugard, Statistical Consultant, UK

This book provides an introduction to a range of advanced statistical techniques that are used in psychology, in an accessible, nonmathematical way. The authors take a practical approach with an emphasis on helping the reader to gain an understanding of what each method is for, what it does, and how it does it. The book covers ANOVA, MANOVA, ANCOVA, multiple regression and partial correlation, factor analysis, cluster analysis and multidimensional scaling, path analysis, and mediation and moderation analysis, discriminant analysis, logistic regression and loglinear analysis. Instructions on how to do the analyses and how to make sense of the results are fully illustrated with dialogue boxes and output tables from SPSS, and details of how to interpret and report the output are also provided. This book aims to get students started, and prepares them to approach more comprehensive treatments with confidence. This makes it an ideal text for psychology students, and for students and academic staff in disciplines other than psychology that use multivariate methods.

Basic Concepts, Applications, and Programming Barbara Byrne University of Ottawa, Canada

Series: Multivariate Applications

This book illustrates the ease with which AMOS 4.0 can be used to address research questions that lend themselves to structural equation modeling (SEM). This goal is achieved by, firstly, presenting a nonmathematical introduction to the basic concepts and applications of structural equation modeling; secondly, demonstrating basic applications of SEM using AMOS 4.0; and thirdly, highlighting features of AMOS 4.0 that address important caveats related to SEM analyses. Written in a "user-friendly" style, the author "walks" the reader through ten SEM applications, from model specification to estimation to the assessment and interpretation of the output. Each of the book's applications is accompanied by: · A statement of the hypothesis being tested · A schematic representation of the model under study · The use and function of a wide variety of icons and pull-down menus · A full explanation of related AMOS Graphic input models and output files · A model input file based on AMOS BASIC · The published reference from which each application was drawn. April 2001: 6x9: 352pp Hb: 978-0-8058-3322-5:9 5 . 9 4 £ $89.95 Pb: 978-0-8058-4104-6:5 0 . 7 2 £ $49.95 · 60-day examination copy available


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Structural Equation Modeling


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Structural Equation Modeling publishes manuscripts from all academic disciplines with an interest in structural equation modeling. These include, but are not limited to, psychology, sociology, educational research, political science, economics, management, and business/marketing. The journal contains theoretical and applied articles, a teachers' corner, book and software reviews, and advertising. Theoretical articles address new developments and examine current practices. Applied articles deal with both exploratory and confirmatory models. The teachers' corner provides instructional modules on aspects of structural equation modeling. The book and software reviews afford an opportunity to examine new modeling information and techniques. Advertising alerts readers to new products. For all submissions, send four (4) manuscript copies to Dr. George A. Marcoulides, Editor, GSOE, 1207 Sproul Hall, University of California, Riverside, Riverside CA 92521. See full `Instructions for Authors' at the website below. RECENT ARTICLES Variance Estimation Using Replication Methods in Structural Equation Modeling With Complex Sample Data: Laura M. Stapleton Is Parceling Really Necessary? A Comparison of Results From Item Parceling and Categorical Variable Methodology: Deborah L. Bandalos An Expansion of the Trait-State-Occasion Model: Accounting for Shared Method Variance: Beth LaGrange, David A. Cole A Bayesian Approach for Analyzing Hierarchical Data With Missing Outcomes Through Structural Equation Models: Xin-Yuan Song, Sik-Yum Lee A Comparison of Four Estimators of a Population Measure of Model Fit in Covariance Structure Analysis: Wei Zhang Investigating Experimental Effects Within the Framework of Structural Equation Modeling: An Example With Effects on Both Error Scores and Reaction Times: Karl Schweizer SUBSCRIPTION RATES 2008 (VOLUME 15) Institutional (Print & Online): £432/$720/576 Institutional (Online only): £410/$684/547 Personal (Print & Online): £36/$60/48 Full details, further subscription information, notes for authors, submission procedures and full online contents available at:

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Factor Analysis, Structural Equation, Multilevel & Longitudinal Modeling

New Titles and Key Backlist 2008 ­ 2009

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