The Oxford Handbook of Quantitative Methods, Two-Volume Set
Published on 29. May 2014
Other
Multiple copy pack
1328 pages
978-0-19-937017-7 (ISBN)
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.
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.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Target group
College/higher education
Dimensions
Height: 254 mm
Width: 182 mm
Thickness: 59 mm
Weight
2260 gr
ISBN-13
978-0-19-937017-7 (9780199370177)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
VOLUME 1 ; 1. Introduction ; Todd D. Little ; 2. The Philosophy of Quantitative Methods ; Brian D. Haig ; 3. Quantitative Methods and Ethics ; Ralph L. Rosnow and Robert Rosenthal ; 4. Special Populations ; Keith F. Widaman, Dawnte R. Early, and Rand D. Conger ; 5. Theory Construction, Model Building, and Model Selection ; James Jaccard ; 6. Teaching Quantitative Psychology ; Lisa L. Harlow ; 7. Modern Test Theory ; R. P. McDonald ; 8. The IRT Tradition and its Applications ; R.J. de Ayala ; 9. Survey Design and Measure Development ; Paul E. Spector ; 10. High Stakes Test Construction and Test Use ; Neal M. Kingston and Laura B. Kramer ; 11. Effect Size and Sample Size Planning ; Ken Kelley ; 12. Experimental Design for Causal Inference: Clinical Trials and Regression Discontinuity Designs ; Kelly Hallberg, Coady Wing, Vivian Wong, and Thomas D. Cook ; 13. Matching and Propensity Scores ; Peter M. Steiner and David Cook ; 14. Designs for and Analyses of Response Time Experiments ; Trisha Van Zandt and James T. Townsend ; 15. Observational Methods ; Jamie M. Ostrov and Emily J. Hart ; 16. A Primer of Epidemiologic Methods, Concepts, and Analysis with Examples and More Advanced Applications within Psychology ; David E. Bard, Joseph L. Rodgers, and Keith E. Muller ; 17. Program Evaluation: Principles, Procedures, and Practices ; Aurelio Jose Figueredo, Sally Gayle Olderbak, Gabriel Lee Schlomer, Rafael Antonio Garcia, and Pedro Sofio Abril Wolf ; 18. Overview of Statistical Estimation Methods ; Ke-Hai Yuan and Christof Schuster ; 19. Robust Statistical Estimation ; David M. Erceg-Hurn, Rand R. Wilcox, and Harvey J. Keselman ; 20. Bayesian Statistical Methods ; David Kaplan and Sarah Depaoli ; 21. Mathematical Modeling ; Daniel R. Cavagnaro, Jay I. Myung, and Mark A. Pitt ; 22. What Would Happen If...? Monte Carlo Analysis in Academic Research ; P. E. Johnson ; VOLUME 2 ; 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 ; P. R. Deboeck ; 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