The Oxford Handbook of Quantitative Methods in Psychology, Vol. 1 Foundations

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. 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, Dawnt? 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 Jos? 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

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.