Table Of Content1. Analysis of Variance: Between-Groups DesignsAlan J. Klockars 2. Analysis of Variance: Repeated Measures DesignsLisa M. Lix and H.J. Keselman 3. Canonical Correlation Analysis Xitao Fan & Timothy R. Konold 4. Cluster AnalysisDena Pastor 5. Correlation and Other Measures of AssociationJason 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 TheoryAmy Hendrickson and Ping Yin 10. Hierarchical Linear ModelingD. Betsy McCoach 11. Interrater Reliability William T. Hoyt 12. Item Response TheoryR.J. De Ayala 13.Latent Class AnalysisKaren M. Samuelsen and C. Mitchell Dayton 14.Latent Growth Curve ModelsKristopher J. Preacher 15. Latent Transition AnalysisDavid Rindskopf 16.Latent Variable Mixture ModelsGitta 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-AnalysisS. Natasha Beretvas 20.Multidimensional ScalingMark L. Davison, Cody S. Ding and Se-Kang Kim 21.Multiple Regression Ken Kelley and Scott E. Maxwell 22. Multitrait-Multimethod AnalysisKeith F. Widaman 23. Multivariate Analysis of VarianceStephen Olejnik 24. Power AnalysisKevin R. Murphy 25. Reliability and Validity of InstrumentsThomas R. Knapp and Ralph O. Mueller 26. Research DesignSharon A. Dannels 27. Single-Subject Design and Analysis Andrew L. Egel and Christine H. Barthold 28. Structural Equation ModelingRalph O. Mueller and Gregory R. Hancock 29. Structural Equation Modeling: Multisample Covariance and Mean StructuresRichard G. Lomax 30. Survey Sampling, Administration, and AnalysisLaura M. Stapleton 31. Survival Analysis Paul D. Allison lt;/EM> 12. Item Response TheoryR.J. De Ayala 13.Latent Class AnalysisKaren M. Samuelsen and C. Mitchell Dayton 14.Latent Growth Curve ModelsKristopher J. Preacher 15. Latent Transition AnalysisDavid Rindskopf 16.Latent Variable Mixture ModelsGitta 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-AnalysisS. Natasha Beretvas 20.Multidimensional ScalingMark L. Davison, Cody S. Ding and Se-Kang Kim 21.Multiple Regression Ken Kelley and Scott E. Maxwell 22. Multitrait-Multimethod AnalysisKeith F. Widaman 23. Multivariate Analysis of VarianceStephen Olejnik 24. Power AnalysisKevin R. Murphy 25. Reliability and Validity of InstrumentsThomas R. Knapp and Ralph O. Mueller 26. Research DesignSharon A. Dannels 27. Single-Subject Design and Analysis Andrew L. Egel and Christine H. Barthold 28. Structural Equation ModelingRalph O. Mueller and Gregory R. Hancock 29. Structural Equation Modeling: Multisample Covariance and Mean StructuresRichard G. Lomax 30. Survey Sampling, Administration, and AnalysisLaura M. Stapleton 31. Survival Analysis Paul D. Allisonsp; 23. Multivariate Analysis of VarianceStephen Olejnik 24. Power AnalysisKevin R. Murphy 25. Reliability and Validity of InstrumentsThomas R. Knapp and Ralph O. Mueller 26. Research DesignSharon A. Dannels 27. Single-Subject Design and Analysis Andrew L. Egel and Christine H. Barthold 28. Structural Equation ModelingRalph O. Mueller and Gregory R. Hancock 29. Structural Equation Modeling: Multisample Covariance and Mean StructuresRichard G. Lomax 30. Survey Sampling, Administration, and AnalysisLaura M. Stapleton 31. Survival Analysis Paul D. Allison
SynopsisThe Reviewer's Guide is designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its uniquely structured chapters address traditional and emerging quantitative methods of data analysis., The Reviewer's Guide to Quantitative Methods in the Social Sciencesis 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. me 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., 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.