The Reviewer’s Guide to Quantitative Methods in the Social Sciences

  • Price: $69.95 $62.96
  • Paperback: 432 pages
  • Also available in Hardback and e-Book
  • Published: February 2010
  • ISBN: 978-0-415-96508-8
  • Publisher: Routledge

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The Reviewer’s Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its thirty-one 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 book updates readers on each technique’s key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author’s reference for preparing sound research manuscripts and proposals. Key features include:

Thirty-one chapters cover virtually all of the popular classic and emerging quantitative techniques , thus helping reviewers to evaluate a manuscript’s methodological approach and it’s data analysis. In addition, the volume serves as an indispensable reference tool for those designing their own research.

For ease of use, all chapters follow the same structure:

  • the opening page of each chapter defines and explains the purpose of that statistical method

  • the next one or two pages provide a table listing various criteria that should be considered when evaluating and applying that methodological approach to data analysis

  • the remainder of each chapter contains numbered sections corresponding to the numbered criteria listed in the opening table. Each section explains the role and importance of that particular criterion.

Chapters are written by methodological and applied scholars who are expert in the particular quantitative method being reviewed.

Table of Contents

1. Analysis of Variance: Between-Groups Designs, Alan J. Klockars (University of Washington)

2. Analysis of Variance: Repeated Measures Designs, Lisa M. Lix (University of Saskatchewan) & H.J. Keselman (University of Manitoba)

3. Canonical Correlation Analysis, Xitao Fan & Timothy R. Konold (University of Virginia)

4. Cluster Analysis, Dena Pastor (James Madison University)

5. Correlation and Other Measures of Association, Jason W. Osborne (North Carolina State University)

6. Discriminant Analysis, Carl J. Huberty (University of Georgia)

7. Effect Sizes and Confidence Intervals, Geoff Cumming & Fiona Fidler (La Trobe University)

8. Factor Analysis: Exploratory and Confirmatory, Deborah L. Bandalos (University of Georgia) & Sara J. Finney (James Madison University)

9. Generalizability Theory, Amy Hendrickson (The College Board) & Ping Yin (American College Testing)

10. Hierarchical Linear Modeling, D. Betsy McCoach (University of Connecticut)

11. Interrater Reliability,William T. Hoyt (University of Wisconsin)

12. Item Response Theory, R.J. De Ayala (University of Nebraska)

13. Latent Class Analysis, Karen M. Samuelsen (University of Georgia) & C. Mitchell Dayton (University of Maryland)

14. Latent Growth Curve Models, Kristopher J. Preacher (University of Kansas)

15. Latent Transition Analysis, David Rindskopf (City University of New York)

16. Latent Variable Mixture Models, Gitta Lubke (University of Notre Dame)

17. Logistic Regression, Ann A. O’Connell (The Ohio State University) & K. Rivet Amico (University of Connecticut)

18. Log-Linear Analysis, Ronald C. Serlin (University of Wisconsin) & Michael A. Seaman (University of South Carolina)

19. Meta-Analysis, S. Natasha Beretvas (University of Texas)

20. Multidimensional Scaling, Mark L. Davison (University of Minnesota), Cody S. Ding (University of Missouri), & Se-Kang Kim (Fordham University)

21. Multiple Regression, Ken Kelley & Scott E. Maxwell (University of Notre Dame)

22. Multitrait-Multimethod Analysis, Keith F. Widaman (University of California at Davis)

23. Multivariate Analysis of Variance, Stephen Olejnik (University of Georgia)

24. Power Analysis, Kevin R. Murphy (Pennsylvania State University)

25. Reliability and Validity of Instruments, Thomas R. Knapp (University of Rochester and Ohio State University) & Ralph O. Mueller (University of Hartford)

26. Research Design, Sharon A. Dannels (George Washington University)

27. Single-Subject Design and Analysis, Andrew L. Egel (University of Maryland) & Christine H. Barthold (University of Delaware)

28. Structural Equation Modeling, Ralph O. Mueller (University of Hartford) & Gregory R. Hancock (University of Maryland)

29. Structural Equation Modeling: Multisample Covariance and Mean Structures, Richard G. Lomax (Ohio State University)

30. Survey Sampling, Administration, and Analysis, Laura M. Stapleton (University of Maryland, Baltimore County)

31. Survival Analysis, Paul D. Allison (University of Pennsylvania)

Author Biography

Gregory R. Hancock is Professor and Chair of the Department of Measurement, Statistics, and Evaluation at the U. of Maryland and is Director of their Center for Integrated Latent Variable Research.

Ralph O. Mueller is Professor and Dean of the College of Education, Nursing and Health Professions at the University of Hartford.

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