The reviewer's guide to quantitative methods in the social sciences
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書誌事項
The reviewer's guide to quantitative methods in the social sciences
Routledge, 2010
- : hbk
- : pbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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:
The chapters cover virtually all of the popular classic and emerging quantitative techniques, thus helping reviewers to evaluate a manuscript's methodological approach and its 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.
目次
1. Analysis of Variance: Between-Groups Designs Alan J. Klockars 2. Analysis of Variance: Repeated Measures Designs Lisa M. Lix and H.J. Keselman 3. Canonical Correlation Analysis Xitao Fan & Timothy R. Konold 4. Cluster Analysis Dena Pastor 5. Correlation and Other Measures of Association Jason W. Osborne 6. Discriminant Analysis Carl J. Huberty 7. Effect Sizes and Confidence Intervals Geoff Cumming and Fiona Fidler 8. Factor Analysis: Exploratory and Confirmatory Deborah L. Bandalos and Sara J. Finney 9. Generalizability Theory Amy Hendrickson and Ping Yin 10. Hierarchical Linear Modeling D. Betsy McCoach 11. Interrater Reliability William T. Hoyt 12. Item Response Theory R.J. De Ayala 13. Latent Class Analysis Karen M. Samuelsen and C. Mitchell Dayton 14. Latent Growth Curve Models Kristopher J. Preacher 15. Latent Transition Analysis David Rindskopf 16. Latent Variable Mixture Models Gitta Lubke 17. Logistic Regression Ann A. O'Connell and K. Rivet Amico 18. Log-Linear Analysis, Ronald C. Serlin and Michael A. Seaman 19. Meta-Analysis S. Natasha Beretvas 20. Multidimensional Scaling Mark L. Davison, Cody S. Ding and Se-Kang Kim 21. Multiple Regression Ken Kelley and Scott E. Maxwell 22. Multitrait-Multimethod Analysis Keith F. Widaman 23. Multivariate Analysis of Variance Stephen Olejnik 24. Power Analysis Kevin R. Murphy 25. Reliability and Validity of Instruments Thomas R. Knapp and Ralph O. Mueller 26. Research Design Sharon A. Dannels 27. Single-Subject Design and Analysis Andrew L. Egel and Christine H. Barthold 28. Structural Equation Modeling Ralph O. Mueller and Gregory R. Hancock 29. Structural Equation Modeling: Multisample Covariance and Mean Structures Richard G. Lomax 30. Survey Sampling, Administration, and Analysis Laura M. Stapleton 31. Survival Analysis Paul D. Allison
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