The Oxford Handbook of Quantitative Methods, Two-Volume Set

Paperback | March 1, 2014

EditorTodd Little

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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 aone-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 chaptersassociated 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.

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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 aone-stop source for learning and reviewing curren...

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.

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Format:PaperbackDimensions:1328 pages, 10 × 7 × 0.68 inPublished:March 1, 2014Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199370176

ISBN - 13:9780199370177

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Table of Contents

VOLUME 11. Todd D. Little: Introduction2. Brian D. Haig: The Philosophy of Quantitative Methods3. Ralph L. Rosnow and Robert Rosenthal: Quantitative Methods and Ethics4. Keith F. Widaman, Dawnte R. Early, and Rand D. Conger: Special Populations5. James Jaccard: Theory Construction, Model Building, and Model Selection6. Lisa L. Harlow: Teaching Quantitative Psychology7. R. P. McDonald: Modern Test Theory8. R. J. de Ayala: The IRT Tradition and its Applications9. Paul E. Spector: Survey Design and Measure Development10. Neal M. Kingston and Laura B. Kramer: High Stakes Test Construction and Test Use11. Ken Kelley: Effect Size and Sample Size Planning12. Kelly Hallberg, Coady Wing, Vivian Wong, and Thomas D. Cook: Experimental Design for Causal Inference: Clinical Trials and Regression Discontinuity Designs13. Peter M. Steiner and David Cook: Matching and Propensity Scores14. Trisha Van Zandt and James T. Townsend: Designs for and Analyses of Response Time Experiments15. Jamie M. Ostrov and Emily J. Hart: Observational Methods16. David E. Bard, Joseph L. Rodgers, and Keith E. Muller: A Primer of Epidemiologic Methods, Concepts, and Analysis with Examples and More Advanced Applications within Psychology17. Aurelio Jose Figueredo, Sally Gayle Olderbak, Gabriel Lee Schlomer, Rafael Antonio Garcia, and Pedro Sofio Abril Wolf: Program Evaluation: Principles, Procedures, and Practices18. Ke-Hai Yuan and Christof Schuster: Overview of Statistical Estimation Methods19. David M. Erceg-Hurn, Rand R. Wilcox, and Harvey J. Keselman: Robust Statistical Estimation20. David Kaplan and Sarah Depaoli: Bayesian Statistical Methods21. Daniel R. Cavagnaro, Jay I. Myung, and Mark A. Pitt: Mathematical Modeling22. P. E. Johnson: What Would Happen If...? Monte Carlo Analysis in Academic ResearchVOLUME 21. Todd Little: Introduction2. Bruce Thompson: Overview of Traditional/Classical Statistical Approaches3. Stefany Coxe, Stephen G. West, and Leona S. Aiken: Generalized Linear models4. Carol M. Woods: Categorical Methods5. Alexander von Eye, Eun-Young Mun, Patrick Mair, and Stefan von Weber: Configural Frequency Analysis6. Trent D. Buskirk, Lisa M. Willoughby, and Terry T. Tomazic: Nonparametric Statistical Techniques7. Michael J. Greenacre: Correspondence Analysis8. Luc Anselin, Alan T. Murray, and Sergio J. Rey: Spatial Analysis9. Larry R. Price: Analysis of Imaging Data10. Sarah E. Medland: Quantitative Analysis of Genes11. Gabriella A.M. Blokland, Miriam A. Mosing, Karin J.H. Verweij, and Sarah E. Medland: Twin Studies and Behavior Genetics12. Cody S. Ding: Multidimensional Scaling13. Timothy A. Brown: Latent Variable Measurement Models14. Joop J. Hox: Multilevel Regression and Multilevel Structural Equation Modeling15. John J. McArdle and Kelly M. Kadlec: Structural Equation Models16. David P. MacKinnon, Yasemin Kisbu-Sakarya, and Amanda C. Gottschall: Developments in Mediation Analysis17. Herbert W. Marsh, Kit-Tai Hau, Zhonglin Wen, Benjamin Nagengast, and Alexandre J.S. Morin: Moderation18. Wei Wu, James P. Selig, and Todd D. Little: Longitudinal Data Analysis19. P. R. Deboeck: Dynamical Systems and Models of Continuous Time20. Theodore A. Walls: Intensive Longitudinal Data21. Nilam Ram, Annette Brose, and Peter C. M. Molenaar: Dynamic Factor Analysis: Modeling Person-specific Process22. William W.S. Wei: Time Series Analysis23. Trond Peterson: Analyzing Event History Data24. Andre A. Rupp: Clustering and Classification25. Kathryn E. Masyn: Latent Class Analysis and Finite Mixture Modeling26. Theodore P. Beauchaine: Taxometrics27. Amanda N. Baraldi and Craig K. Enders: Missing Data Methods28. M. Brent Donnellan and Richard E. Lucas: Secondary Data Analysis29. Carolin Strobl: Data Mining30. Noel A. Card and Deborah M. Casper: Meta-analysis and Quantitative Research Synthesis31. Lihshing Leigh Wang, Amber S. Watts, Rawni A. Anderson, and Todd D. Little: Common Fallacies in Quantitative Research Methodology