The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.…mehr
The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.
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. HisWeb site is atwww.udel.edu/dkaplan.
Inhaltsangabe
Preface Acknowledgments Section I: Scaling Chapter 1: Dual Scaling - Shizuhiko Nishisato Chapter 2: Multidimensional Scaling and Unfolding of Symmetric and Asymmetric Proximity Relations - Willem J. Heiser and Frank M.T.A. Busing Chapter 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. Heiser Section II: Testing and Measurement Chapter 4: Responsible Modeling of Measurement Data for Appropriate Inferences: Important Advances in Reliability and Validity Theory - Bruno D. Zumbo and Andre A. Rupp Chapter 5: Test Modeling - Ratna Nandakumar and Terry Ackerman Chapter 6: Differential Item Functioning Analysis: Detecting DIF Items and Testing DIF Hypotheses - Louis A. Roussos and William Stout Chapter 7: Understanding Computerized Adaptive Testing: from Robbins-Monro to Lord and Beyond - Hua-Hua Chang Section III: Models for Categorical Data Chapter 8: Trends in Categorical Data Analysis: New, Semi-New, and Recycled Ideas - David Rindskopf Chapter 9: Ordinal Regression Models - Valen E. Johnson and James H. Albert Chapter 10: Latent Class Models - Jay Magidson and Jeroen K. Vermunt Chapter 11: Discrete-Time Survival Analysis - John B. Willett and Judith D. Singer Section IV: Models for Multilevel Data Chapter 12: An Introduction to Growth Modeling - Donald Hedecker Chapter 13: Multilevel Models for School Effectiveness Research - Russell W. Rumberger and Gregory J. Palardy Chapter 14: The Use of Hierarchical Models in Analyzing Data from Experiments and Quasi-Experiments Conducted in Field Settings - Michael Seltzer Chapter 15: Meta-Analysis - Spyros Konstantopoulos and Larry V. Hedges Section V: Models for Latent Variables Chapter 16: Determining the Number of Factors in Exploratory and Confirmatory Factor Analysis - Rick H. Hoyle and Jamieson L. Duvall Chapter 17: Experimental, Quasi-Experimental, and Nonexperimental Design and Analysis with Latent Variables - Gregory R. Hancock Chapter 18: Applying Dynamic Factor Analysis in Behavioral and Social Science Research - John R. Nesselroade and Peter C. M. Molenaar Chapter 19: Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data - Bengt Muthen Section VI: Foundational Issues Chapter 20: Probabalistic Modeling with Bayesian Networks - Richard E. Neapolitan and Scott Morris Chapter 21: The Null Ritual: What You Always Wanted to Know About Significance Testing but Were Afraid to Ask - Gerd Gigerenzer, Stefan Krauss, and Oliver Vitouch Chapter 22: On Exogeneity - David Kaplan Chapter 23: Objectivity in Science and Structural Equation Modeling - Stanley A. Mulaik Chapter 24: Causal Inference - Peter Spirtes, Richard Scheines, Clark Glymour, Thomas Richardson, and Christopher Meek Index
Preface Acknowledgments Section I: Scaling Chapter 1: Dual Scaling - Shizuhiko Nishisato Chapter 2: Multidimensional Scaling and Unfolding of Symmetric and Asymmetric Proximity Relations - Willem J. Heiser and Frank M.T.A. Busing Chapter 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. Heiser Section II: Testing and Measurement Chapter 4: Responsible Modeling of Measurement Data for Appropriate Inferences: Important Advances in Reliability and Validity Theory - Bruno D. Zumbo and Andre A. Rupp Chapter 5: Test Modeling - Ratna Nandakumar and Terry Ackerman Chapter 6: Differential Item Functioning Analysis: Detecting DIF Items and Testing DIF Hypotheses - Louis A. Roussos and William Stout Chapter 7: Understanding Computerized Adaptive Testing: from Robbins-Monro to Lord and Beyond - Hua-Hua Chang Section III: Models for Categorical Data Chapter 8: Trends in Categorical Data Analysis: New, Semi-New, and Recycled Ideas - David Rindskopf Chapter 9: Ordinal Regression Models - Valen E. Johnson and James H. Albert Chapter 10: Latent Class Models - Jay Magidson and Jeroen K. Vermunt Chapter 11: Discrete-Time Survival Analysis - John B. Willett and Judith D. Singer Section IV: Models for Multilevel Data Chapter 12: An Introduction to Growth Modeling - Donald Hedecker Chapter 13: Multilevel Models for School Effectiveness Research - Russell W. Rumberger and Gregory J. Palardy Chapter 14: The Use of Hierarchical Models in Analyzing Data from Experiments and Quasi-Experiments Conducted in Field Settings - Michael Seltzer Chapter 15: Meta-Analysis - Spyros Konstantopoulos and Larry V. Hedges Section V: Models for Latent Variables Chapter 16: Determining the Number of Factors in Exploratory and Confirmatory Factor Analysis - Rick H. Hoyle and Jamieson L. Duvall Chapter 17: Experimental, Quasi-Experimental, and Nonexperimental Design and Analysis with Latent Variables - Gregory R. Hancock Chapter 18: Applying Dynamic Factor Analysis in Behavioral and Social Science Research - John R. Nesselroade and Peter C. M. Molenaar Chapter 19: Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data - Bengt Muthen Section VI: Foundational Issues Chapter 20: Probabalistic Modeling with Bayesian Networks - Richard E. Neapolitan and Scott Morris Chapter 21: The Null Ritual: What You Always Wanted to Know About Significance Testing but Were Afraid to Ask - Gerd Gigerenzer, Stefan Krauss, and Oliver Vitouch Chapter 22: On Exogeneity - David Kaplan Chapter 23: Objectivity in Science and Structural Equation Modeling - Stanley A. Mulaik Chapter 24: Causal Inference - Peter Spirtes, Richard Scheines, Clark Glymour, Thomas Richardson, and Christopher Meek Index
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