OXFORD UNIVERSITY PRESS

The Oxford Handbook of Quantitative Methods

ISBN : 9780199370177

参考価格(税込): 
¥19,250
著者: 
Todd Little
ページ
1328 ページ
フォーマット
Multiple Copy Pack
サイズ
182 x 254 mm
刊行日
2014年05月
シリーズ
Oxford Library of Psychology
メール送信
印刷

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.

目次: 

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

著者について: 

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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