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Full Description
Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods in Psychology is the complete tool box to deliver the most valid and generalizable answers to today's complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences.
Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.
Contents
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
