The Reviewer’s Guide to Quantitative Methods in the Social Sciences (2^nd Ed.)

The Reviewer?s Guide to Quantitative Methods in the Social Sciences provides evaluators of research manuscripts and proposals in the social and behavioral sciences with the resources they need to read, understand, and assess quantitative work. 35 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The second edition of this valuable resource updates readers on each technique?s key principles, appropriate usage, underlying assumptions and limitations, providing reviewers with the information they need to offer constructive commentary on works they evaluate. Written by methodological and applied scholars, this volume is also an indispensable author?s reference for preparing sound research manuscripts and proposals.

1. Analysis of Variance: Between-Groups Designs Robert A. Cribbie and Alan J. Klockars 2. Analysis of Variance: Repeated Measures Designs Lisa M. Lix and H. J. Keselman 3. Canonical Correlation Analysis Xitao Fan and Timothy R. Konold 4. Cluster Analysis Dena A. Pastor and Monica K. Erbacher 5. Correlation and Other Measures of Association Jill L. Adelson, Jason W. Osborne, and Brittany F. Crawford 6. Effect Sizes and Confidence Intervals Fiona Fidler and Geoff Cumming 7. Event History and Survival Analysis Paul D. Allison 8. Factor Analysis: Exploratory and Confirmatory Deborah L. Bandalos and Sara J. Finney 9. Generalizability Theory Amy Hendrickson and Ping Yin 10. Interrater Reliability and Agreement William T. Hoyt 11. Item Response Theory and Rasch Modeling R.J. De Ayala 12. Latent Class Analysis Karen M. Samuelsen and C. Mitchell Dayton 13. Latent Growth Curve Models Kristopher J. Preacher 14. Latent Transition Analysis David Rindskopf 15. Latent Variable Mixture Models Gitta Lubke 16. Logistic Regression and Extensions Ann A. O’Connell and K. Rivet Amico 17. Log-Linear Analysis Ronald C. Serlin and Michael A. Seaman 18. Mediation and Moderation Paul E. Jose 19. Meta-Analysis S. Natasha Beretvas 20. Monte Carlo Simulation Methods Daniel McNeish, Stephanie Lane, and Patrick Curran 21. Multidimensional Scaling Cody S. Ding and Se-Kang Kim 22. Multilevel Modeling D. Betsy McCoach 23. Multiple Regression Ken Kelley and Scott E. Maxwell 24. Multitrait-Multimethod Analysis Keith F. Widaman 25. Multivariate Analysis of Variance Keenan A. Pituch 26. Nonparametric Statistics Michael A. Seaman 27. Power Analysis Kevin R. Murphy 28. Propensity Scores and Matching Methods Elizabeth A. Smart 29. Reliability and Validity Ralph O. Mueller 30. Research Design Sharon Anderson Dannels 31. Single-Subject Design and Analysis Andrew L. Egel, Christine H. Barthold, Jennifer Lee Kuou, and Fayez S. Maajeeny 32. Social Network Analysis Tracy Sweet 33. Structural Equation Modeling Ralph O. Mueller and Gregory R. Hancock 34. Structural Equation Modeling: Multisample Covariance and Mean Structures Richard G. Lomax 35. Survey Sampling, Administration, and Analysis Laura M. Stapleton

Gregory R. Hancock is Professor, Distinguished Scholar-Teacher, and Director of the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology at the University of Maryland, as well as Director of their Center for Integrated Latent Variable Research.

Laura M. Stapleton is Professor in the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology at the University of Maryland.

Ralph O. Mueller is Vice Chancellor for Academic Affairs and Provost at Purdue University Northwest.