Paperback : £63.07
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.
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.
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
Gabriëlla 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
André 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
psychological
methodology, 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 benefit
of students of psychology as well as practicing psychologists."
-Sonu Chandiram, Biz India
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