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The Oxford Handbook of Quantitative Methods in Psychology, Volume 2

The Oxford Handbook of Quantitative Methods in Psychology, Volume 2

The Oxford Handbook of Quantitative Methods in Psychology, Volume 2

The Oxford Handbook of Quantitative Methods in Psychology, Volume 2

Overview

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.

Product Details

ISBN-13: 9780199370160
Publisher: Oxford University Press
Publication date: 03/01/2014
Series: Oxford Library of Psychology
Pages: 784
Product dimensions: 7.00(w) x 10.00(h) x 1.50(d)

About the Author

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

Table of 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
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
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