Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative Training Program, Director of the Undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental Training program.

"This is a unique, well-researched [book] uses many real-life examples to help you the student or practitioner understand concepts in concrete terms. 105 specialists in various areas have contributed to the second volume (with its focus on statistical analysis) of this unique book on quantitative methods in psychology and other sciences, writing or co-writing its 31 chapters. This is a unique, well-researched and well-organized book on psychologicalmethodology, and Dr. Little has done an excellent job in providing proper guidelines to authors on the scope and purpose of the articles, as well as analyzing, and expertly presenting the material for the benefitof students of psychology as well as practicing psychologists." -Sonu Chandiram, Biz India

• 1. Introduction ; Todd Little ; 2. Overview of Traditional/Classical Statistical Approaches ; Bruce Thompson ; 3. Generalized Linear Models ; Stefany Coxe, Stephen G. West, and Leona S. Aiken ; 4. Categorical Methods ; Carol M. Woods ; 5. Configural Frequency Analysis ; Alexander von Eye, Eun-Young Mun, Patrick Mair, and Stefan von Weber ; 6. Nonparametric Statistical Techniques ; Trent D. Buskirk, Lisa M. Willoughby, and Terry T. Tomazic ; 7. Correspondence Analysis ; Michael J. Greenacre ; 8. Spatial Analysis ; Luc Anselin, Alan T. Murray, and Sergio J. Rey ; 9. Analysis of Imaging Data ; Larry R. Price ; 10. Quantitative Analysis of Genes ; Sarah E. Medland ; 11. Twin Studies and Behavior Genetics ; Gabriella A.M. Blokland, Miriam A. Mosing, Karin J.H. Verweij, and Sarah E. Medland ; 12. Multidimensional Scaling ; Cody S. Ding ; 13. Latent Variable Measurement Models ; Timothy A. Brown ; 14. Multilevel Regression and Multilevel Structural Equation Modeling ; Joop J. Hox ; 15. Structural Equation Models ; John J. McArdle and Kelly M. Kadlec ; 16. Developments in Mediation Analysis ; David P. MacKinnon, Yasemin Kisbu-Sakarya, and Amanda C. Gottschall ; 17. Moderation ; Herbert W. Marsh, Kit-Tai Hau, Zhonglin Wen, Benjamin Nagengast, and Alexandre J.S. Morin ; 18. Longitudinal Data Analysis ; Wei Wu, James P. Selig, and Todd D. Little ; 19. Dynamical Systems and Models of Continuous Time ; Deboeck, P. R. ; 20. Intensive Longitudinal Data ; Theodore A. Walls ; 21. Dynamic Factor Analysis: Modeling Person-specific Process ; Nilam Ram, Annette Brose, and Peter C. M. Molenaar ; 22. Time Series Analysis ; William W.S. Wei ; 23. Analyzing Event History Data ; Trond Peterson ; 24. Clustering and Classification ; Andre A. Rupp ; 25. Latent Class Analysis and Finite Mixture Modeling ; Katherine E. Masyn ; 26. Taxometrics ; Theodore P. Beauchaine ; 27. Missing Data Methods ; Amanda N. Baraldi and Craig K. Enders ; 28. Secondary Data Analysis ; M. Brent Donnellan and Richard E. Lucas ; 29. Data Mining ; Carolin Strobl ; 30. Meta-analysis and Quantitative Research Synthesis ; Noel A. Card and Deborah M. Casper ; 31. Common Fallacies in Quantitative Research Methodology ; Lihshing Leigh Wang, Amber S. Watts, Rawni A. Anderson, and Todd D. Little

Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative Training Program, Director of the Undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental Training program.

"This is a unique, well-researched [book] uses many real-life examples to help you the student or practitioner understand concepts in concrete terms. 105 specialists in various areas have contributed to the second volume (with its focus on statistical analysis) of this unique book on quantitative methods in psychology and other sciences, writing or co-writing its 31 chapters. This is a unique, well-researched and well-organized book on psychologicalmethodology, and Dr. Little has done an excellent job in providing proper guidelines to authors on the scope and purpose of the articles, as well as analyzing, and expertly presenting the material for the benefitof students of psychology as well as practicing psychologists." -Sonu Chandiram, Biz India

• 1. Introduction ; Todd Little ; 2. Overview of Traditional/Classical Statistical Approaches ; Bruce Thompson ; 3. Generalized Linear Models ; Stefany Coxe, Stephen G. West, and Leona S. Aiken ; 4. Categorical Methods ; Carol M. Woods ; 5. Configural Frequency Analysis ; Alexander von Eye, Eun-Young Mun, Patrick Mair, and Stefan von Weber ; 6. Nonparametric Statistical Techniques ; Trent D. Buskirk, Lisa M. Willoughby, and Terry T. Tomazic ; 7. Correspondence Analysis ; Michael J. Greenacre ; 8. Spatial Analysis ; Luc Anselin, Alan T. Murray, and Sergio J. Rey ; 9. Analysis of Imaging Data ; Larry R. Price ; 10. Quantitative Analysis of Genes ; Sarah E. Medland ; 11. Twin Studies and Behavior Genetics ; Gabriella A.M. Blokland, Miriam A. Mosing, Karin J.H. Verweij, and Sarah E. Medland ; 12. Multidimensional Scaling ; Cody S. Ding ; 13. Latent Variable Measurement Models ; Timothy A. Brown ; 14. Multilevel Regression and Multilevel Structural Equation Modeling ; Joop J. Hox ; 15. Structural Equation Models ; John J. McArdle and Kelly M. Kadlec ; 16. Developments in Mediation Analysis ; David P. MacKinnon, Yasemin Kisbu-Sakarya, and Amanda C. Gottschall ; 17. Moderation ; Herbert W. Marsh, Kit-Tai Hau, Zhonglin Wen, Benjamin Nagengast, and Alexandre J.S. Morin ; 18. Longitudinal Data Analysis ; Wei Wu, James P. Selig, and Todd D. Little ; 19. Dynamical Systems and Models of Continuous Time ; Deboeck, P. R. ; 20. Intensive Longitudinal Data ; Theodore A. Walls ; 21. Dynamic Factor Analysis: Modeling Person-specific Process ; Nilam Ram, Annette Brose, and Peter C. M. Molenaar ; 22. Time Series Analysis ; William W.S. Wei ; 23. Analyzing Event History Data ; Trond Peterson ; 24. Clustering and Classification ; Andre A. Rupp ; 25. Latent Class Analysis and Finite Mixture Modeling ; Katherine E. Masyn ; 26. Taxometrics ; Theodore P. Beauchaine ; 27. Missing Data Methods ; Amanda N. Baraldi and Craig K. Enders ; 28. Secondary Data Analysis ; M. Brent Donnellan and Richard E. Lucas ; 29. Data Mining ; Carolin Strobl ; 30. Meta-analysis and Quantitative Research Synthesis ; Noel A. Card and Deborah M. Casper ; 31. Common Fallacies in Quantitative Research Methodology ; Lihshing Leigh Wang, Amber S. Watts, Rawni A. Anderson, and Todd D. Little

Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative Training Program, Director of the Undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental Training program.

"This is a unique, well-researched [book] uses many real-life examples to help you the student or practitioner understand concepts in concrete terms. 105 specialists in various areas have contributed to the second volume (with its focus on statistical analysis) of this unique book on quantitative methods in psychology and other sciences, writing or co-writing its 31 chapters. This is a unique, well-researched and well-organized book on psychologicalmethodology, and Dr. Little has done an excellent job in providing proper guidelines to authors on the scope and purpose of the articles, as well as analyzing, and expertly presenting the material for the benefitof students of psychology as well as practicing psychologists." -Sonu Chandiram, Biz India

• 1. Introduction ; Todd Little ; 2. Overview of Traditional/Classical Statistical Approaches ; Bruce Thompson ; 3. Generalized Linear Models ; Stefany Coxe, Stephen G. West, and Leona S. Aiken ; 4. Categorical Methods ; Carol M. Woods ; 5. Configural Frequency Analysis ; Alexander von Eye, Eun-Young Mun, Patrick Mair, and Stefan von Weber ; 6. Nonparametric Statistical Techniques ; Trent D. Buskirk, Lisa M. Willoughby, and Terry T. Tomazic ; 7. Correspondence Analysis ; Michael J. Greenacre ; 8. Spatial Analysis ; Luc Anselin, Alan T. Murray, and Sergio J. Rey ; 9. Analysis of Imaging Data ; Larry R. Price ; 10. Quantitative Analysis of Genes ; Sarah E. Medland ; 11. Twin Studies and Behavior Genetics ; Gabriella A.M. Blokland, Miriam A. Mosing, Karin J.H. Verweij, and Sarah E. Medland ; 12. Multidimensional Scaling ; Cody S. Ding ; 13. Latent Variable Measurement Models ; Timothy A. Brown ; 14. Multilevel Regression and Multilevel Structural Equation Modeling ; Joop J. Hox ; 15. Structural Equation Models ; John J. McArdle and Kelly M. Kadlec ; 16. Developments in Mediation Analysis ; David P. MacKinnon, Yasemin Kisbu-Sakarya, and Amanda C. Gottschall ; 17. Moderation ; Herbert W. Marsh, Kit-Tai Hau, Zhonglin Wen, Benjamin Nagengast, and Alexandre J.S. Morin ; 18. Longitudinal Data Analysis ; Wei Wu, James P. Selig, and Todd D. Little ; 19. Dynamical Systems and Models of Continuous Time ; Deboeck, P. R. ; 20. Intensive Longitudinal Data ; Theodore A. Walls ; 21. Dynamic Factor Analysis: Modeling Person-specific Process ; Nilam Ram, Annette Brose, and Peter C. M. Molenaar ; 22. Time Series Analysis ; William W.S. Wei ; 23. Analyzing Event History Data ; Trond Peterson ; 24. Clustering and Classification ; Andre A. Rupp ; 25. Latent Class Analysis and Finite Mixture Modeling ; Katherine E. Masyn ; 26. Taxometrics ; Theodore P. Beauchaine ; 27. Missing Data Methods ; Amanda N. Baraldi and Craig K. Enders ; 28. Secondary Data Analysis ; M. Brent Donnellan and Richard E. Lucas ; 29. Data Mining ; Carolin Strobl ; 30. Meta-analysis and Quantitative Research Synthesis ; Noel A. Card and Deborah M. Casper ; 31. Common Fallacies in Quantitative Research Methodology ; Lihshing Leigh Wang, Amber S. Watts, Rawni A. Anderson, and Todd D. Little