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The Oxford Handbook of Quantitative Methods, Volume 1

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 ...

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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.

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Product Details

  • ISBN-13: 9780199370153
  • Publisher: Oxford University Press, USA
  • Publication date: 3/1/2014
  • Series: Oxford Library of Psychology Series
  • Pages: 544
  • Product dimensions: 7.00 (w) x 9.90 (h) x 1.10 (d)

Meet 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 at the University of Kansas.

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Table of Contents

1. Introduction
Todd 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, Dawnté 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 José 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

23. Network Analysis: A definitional Guide to Important Concepts
Harold D. Green, Jr. and Stanley Wasserman

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