David Kaplan received his Ph.D. in Education from UCLA in 1987. He is now a Professor of Education and (by courtesy) Psychology at the University of Delaware. His research interests are in the development and application of statistical models to problems in educational evaluation and policy analysis. His current program of research concerns the development of dynamic latent continuous and categorical variable models for studying the diffusion of educational innovations.

• PrefaceAcknowledgmentsSection I: ScalingChapter 1: Dual Scaling - Shizuhiko NishisatoChapter 2: Multidimensional Scaling and Unfolding of Symmetric and Asymmetric Proximity Relations - Willem J. Heiser and Frank M.T.A. BusingChapter 3: Principal Components Analysis With Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data - Jacqueline J. Muelman, Anita J. Van der Kooij, and Willem J. HeiserSection II: Testing and MeasurementChapter 4: Responsible Modeling of Measurement Data for Appropriate Inferences: Important Advances in Reliability and Validity Theory - Bruno D. Zumbo and Andre A. RuppChapter 5: Test Modeling - Ratna Nandakumar and Terry AckermanChapter 6: Differential Item Functioning Analysis: Detecting DIF Items and Testing DIF Hypotheses - Louis A. Roussos and William StoutChapter 7: Understanding Computerized Adaptive Testing: from Robbins-Monro to Lord and Beyond - Hua-Hua ChangSection III: Models for Categorical DataChapter 8: Trends in Categorical Data Analysis: New, Semi-New, and Recycled Ideas - David RindskopfChapter 9: Ordinal Regression Models - Valen E. Johnson and James H. AlbertChapter 10: Latent Class Models - Jay Magidson and Jeroen K. VermuntChapter 11: Discrete-Time Survival Analysis - John B. Willett and Judith D. SingerSection IV: Models for Multilevel DataChapter 12: An Introduction to Growth Modeling - Donald HedeckerChapter 13: Multilevel Models for School Effectiveness Research - Russell W. Rumberger and Gregory J. PalardyChapter 14: The Use of Hierarchical Models in Analyzing Data from Experiments and Quasi-Experiments Conducted in Field Settings - Michael SeltzerChapter 15: Meta-Analysis - Spyros Konstantopoulos and Larry V. HedgesSection V: Models for Latent VariablesChapter 16: Determining the Number of Factors in Exploratory and Confirmatory Factor Analysis - Rick H. Hoyle and Jamieson L. DuvallChapter 17: Experimental, Quasi-Experimental, and Nonexperimental Design and Analysis with Latent Variables - Gregory R. HancockChapter 18: Applying Dynamic Factor Analysis in Behavioral and Social Science Research - John R. Nesselroade and Peter C. M. MolenaarChapter 19: Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data - Bengt MuthenSection VI: Foundational IssuesChapter 20: Probabalistic Modeling with Bayesian Networks - Richard E. Neapolitan and Scott MorrisChapter 21: The Null Ritual: What You Always Wanted to Know About Significance Testing but Were Afraid to Ask - Gerd Gigerenzer, Stefan Krauss, and Oliver VitouchChapter 22: On Exogeneity - David KaplanChapter 23: Objectivity in Science and Structural Equation Modeling - Stanley A. MulaikChapter 24: Causal Inference - Peter Spirtes, Richard Scheines, Clark Glymour, Thomas Richardson, and Christopher MeekIndex

“The 24 chapters in this Handbook span a wide range of topics, presenting the latest quantitative developments in scaling theory, measurement, categorical data analysis, multilevel models, latent variable models, and foundational issues. Each chapter reviews the historical context for the topic and then describes current work, including illustrative examples where appropriate. The level of presentation throughout the book is detailed enough to convey genuine understanding without overwhelming the reader with technical material. Ample references are given for readers who wish to pursue topics in more detail. The book will appeal to both researchers who wish to update their knowledge of specific quantitative methods, and students who wish to have an integrated survey of state-of- the-art quantitative methods.”

David Kaplan received his Ph.D. in Education from UCLA in 1987. He is now a Professor of Education and (by courtesy) Psychology at the University of Delaware. His research interests are in the development and application of statistical models to problems in educational evaluation and policy analysis. His current program of research concerns the development of dynamic latent continuous and categorical variable models for studying the diffusion of educational innovations.

• PrefaceAcknowledgmentsSection I: ScalingChapter 1: Dual Scaling - Shizuhiko NishisatoChapter 2: Multidimensional Scaling and Unfolding of Symmetric and Asymmetric Proximity Relations - Willem J. Heiser and Frank M.T.A. BusingChapter 3: Principal Components Analysis With Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data - Jacqueline J. Muelman, Anita J. Van der Kooij, and Willem J. HeiserSection II: Testing and MeasurementChapter 4: Responsible Modeling of Measurement Data for Appropriate Inferences: Important Advances in Reliability and Validity Theory - Bruno D. Zumbo and Andre A. RuppChapter 5: Test Modeling - Ratna Nandakumar and Terry AckermanChapter 6: Differential Item Functioning Analysis: Detecting DIF Items and Testing DIF Hypotheses - Louis A. Roussos and William StoutChapter 7: Understanding Computerized Adaptive Testing: from Robbins-Monro to Lord and Beyond - Hua-Hua ChangSection III: Models for Categorical DataChapter 8: Trends in Categorical Data Analysis: New, Semi-New, and Recycled Ideas - David RindskopfChapter 9: Ordinal Regression Models - Valen E. Johnson and James H. AlbertChapter 10: Latent Class Models - Jay Magidson and Jeroen K. VermuntChapter 11: Discrete-Time Survival Analysis - John B. Willett and Judith D. SingerSection IV: Models for Multilevel DataChapter 12: An Introduction to Growth Modeling - Donald HedeckerChapter 13: Multilevel Models for School Effectiveness Research - Russell W. Rumberger and Gregory J. PalardyChapter 14: The Use of Hierarchical Models in Analyzing Data from Experiments and Quasi-Experiments Conducted in Field Settings - Michael SeltzerChapter 15: Meta-Analysis - Spyros Konstantopoulos and Larry V. HedgesSection V: Models for Latent VariablesChapter 16: Determining the Number of Factors in Exploratory and Confirmatory Factor Analysis - Rick H. Hoyle and Jamieson L. DuvallChapter 17: Experimental, Quasi-Experimental, and Nonexperimental Design and Analysis with Latent Variables - Gregory R. HancockChapter 18: Applying Dynamic Factor Analysis in Behavioral and Social Science Research - John R. Nesselroade and Peter C. M. MolenaarChapter 19: Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data - Bengt MuthenSection VI: Foundational IssuesChapter 20: Probabalistic Modeling with Bayesian Networks - Richard E. Neapolitan and Scott MorrisChapter 21: The Null Ritual: What You Always Wanted to Know About Significance Testing but Were Afraid to Ask - Gerd Gigerenzer, Stefan Krauss, and Oliver VitouchChapter 22: On Exogeneity - David KaplanChapter 23: Objectivity in Science and Structural Equation Modeling - Stanley A. MulaikChapter 24: Causal Inference - Peter Spirtes, Richard Scheines, Clark Glymour, Thomas Richardson, and Christopher MeekIndex

“The 24 chapters in this Handbook span a wide range of topics, presenting the latest quantitative developments in scaling theory, measurement, categorical data analysis, multilevel models, latent variable models, and foundational issues. Each chapter reviews the historical context for the topic and then describes current work, including illustrative examples where appropriate. The level of presentation throughout the book is detailed enough to convey genuine understanding without overwhelming the reader with technical material. Ample references are given for readers who wish to pursue topics in more detail. The book will appeal to both researchers who wish to update their knowledge of specific quantitative methods, and students who wish to have an integrated survey of state-of- the-art quantitative methods.”

David Kaplan received his Ph.D. in Education from UCLA in 1987. He is now a Professor of Education and (by courtesy) Psychology at the University of Delaware. His research interests are in the development and application of statistical models to problems in educational evaluation and policy analysis. His current program of research concerns the development of dynamic latent continuous and categorical variable models for studying the diffusion of educational innovations.

• PrefaceAcknowledgmentsSection I: ScalingChapter 1: Dual Scaling - Shizuhiko NishisatoChapter 2: Multidimensional Scaling and Unfolding of Symmetric and Asymmetric Proximity Relations - Willem J. Heiser and Frank M.T.A. BusingChapter 3: Principal Components Analysis With Nonlinear Optimal Scaling Transformations for Ordinal and Nominal Data - Jacqueline J. Muelman, Anita J. Van der Kooij, and Willem J. HeiserSection II: Testing and MeasurementChapter 4: Responsible Modeling of Measurement Data for Appropriate Inferences: Important Advances in Reliability and Validity Theory - Bruno D. Zumbo and Andre A. RuppChapter 5: Test Modeling - Ratna Nandakumar and Terry AckermanChapter 6: Differential Item Functioning Analysis: Detecting DIF Items and Testing DIF Hypotheses - Louis A. Roussos and William StoutChapter 7: Understanding Computerized Adaptive Testing: from Robbins-Monro to Lord and Beyond - Hua-Hua ChangSection III: Models for Categorical DataChapter 8: Trends in Categorical Data Analysis: New, Semi-New, and Recycled Ideas - David RindskopfChapter 9: Ordinal Regression Models - Valen E. Johnson and James H. AlbertChapter 10: Latent Class Models - Jay Magidson and Jeroen K. VermuntChapter 11: Discrete-Time Survival Analysis - John B. Willett and Judith D. SingerSection IV: Models for Multilevel DataChapter 12: An Introduction to Growth Modeling - Donald HedeckerChapter 13: Multilevel Models for School Effectiveness Research - Russell W. Rumberger and Gregory J. PalardyChapter 14: The Use of Hierarchical Models in Analyzing Data from Experiments and Quasi-Experiments Conducted in Field Settings - Michael SeltzerChapter 15: Meta-Analysis - Spyros Konstantopoulos and Larry V. HedgesSection V: Models for Latent VariablesChapter 16: Determining the Number of Factors in Exploratory and Confirmatory Factor Analysis - Rick H. Hoyle and Jamieson L. DuvallChapter 17: Experimental, Quasi-Experimental, and Nonexperimental Design and Analysis with Latent Variables - Gregory R. HancockChapter 18: Applying Dynamic Factor Analysis in Behavioral and Social Science Research - John R. Nesselroade and Peter C. M. MolenaarChapter 19: Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data - Bengt MuthenSection VI: Foundational IssuesChapter 20: Probabalistic Modeling with Bayesian Networks - Richard E. Neapolitan and Scott MorrisChapter 21: The Null Ritual: What You Always Wanted to Know About Significance Testing but Were Afraid to Ask - Gerd Gigerenzer, Stefan Krauss, and Oliver VitouchChapter 22: On Exogeneity - David KaplanChapter 23: Objectivity in Science and Structural Equation Modeling - Stanley A. MulaikChapter 24: Causal Inference - Peter Spirtes, Richard Scheines, Clark Glymour, Thomas Richardson, and Christopher MeekIndex

“The 24 chapters in this Handbook span a wide range of topics, presenting the latest quantitative developments in scaling theory, measurement, categorical data analysis, multilevel models, latent variable models, and foundational issues. Each chapter reviews the historical context for the topic and then describes current work, including illustrative examples where appropriate. The level of presentation throughout the book is detailed enough to convey genuine understanding without overwhelming the reader with technical material. Ample references are given for readers who wish to pursue topics in more detail. The book will appeal to both researchers who wish to update their knowledge of specific quantitative methods, and students who wish to have an integrated survey of state-of- the-art quantitative methods.”