Best practices in quantitative methods. (2007)

• Record Type: Book
• Further Information: Note: Edited by Jason W. Osborne.
• Other Names: Osborne, Jason W
• Contents: Introduction - Jason W. Osborne; Part I: Best Practices in Measurement - Jason Osborne; Chapter 1: The New Stats: Attitudes for the Twenty-First Century - Fiona Fidler & Geoff Cumming; Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues - Thomas Kellow & Victor Willson; Chapter 3: Best Practices in Inter-rater Reliability: Assumptions and Implications of three common approaches - Steve Stemler; Chapter 4: An Introduction to Rasch Measurement - Cherdsak Iramaneerat, Everett V. Smith, Jr., & Richard M. Smith; Chapter 5: Applications of the Multi-Faceted Rasch Model - Edward W. Wolfe & Lidia Dobria; Chapter 6: Best Practices in Exploratory Factor Analysis - Jason W. Osborne, Anna B. Costello, & J. Thomas Kellow; Part II: Selected Best Practices in Research Design - Jason W. Osborne; Chapter 7: A Rational Foundation for Scientific Decisions: The Case for the Probability of Replication Statistic - Peter R. Killeen; Chapter 8: Best Practices in Mixed Methods Research - Jessica T. DeCuir-Gunby; Chapter 9: Designing a Rigorous Small Sample Study - Naomi Jeffery Petersen; Chapter10: Replication in Field Studies - William D. Schafer; Chapter 11: Best practices in ANCOVA may mean not using ANCOVA: Why paired subjects designs are a better choice - Elizabeth A. Stuart & Donald B. Rubin; Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis - Spyros Konstantopoulos; Part III: Best Practices in Data Cleaning and the Basics of Data Analysis - Jason W. Osborne; Chapter 13: Best Practices in Data Transformations: The Overlooked Effect of Minimum Values - Jason W. Osborne; Chapter 14: Best Practices in Data Cleaning: How Outliers can increase error rates and decrease the quality and precision of your results - Jason W. Osborne & Amy Overbay; Chapter 15: How to Deal With Missing Data - Jason C. Cole; Chapter16: Is Disattenuation of Effects a Best Practice? - Jason W. Osborne; Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, & Confidence Intervals for Effect Sizes - Bruce Thompson; Chapter 18: Robust Methods for Detecting Associations - Rand R. Wilcox; Part IV: Best Practices of Quantitative Methods - Jason W. Osborne; Chapter 19: Resampling: A Conceptual and Procedural Introduction - Chong Ho Yu; Chapter 20: Creating Valid Prediction Equations in Multiple Regression: Shrinkage, Double Cross-Validation, and Confidence Intervals around Predictions - Jason W. Osborne; Chapter 21: Using Poisson Regression to Analyze Count Data - E. Michael Nussbaum, Sherif Elsadat, & Ahmed H. Khago; Chapter 22: Testing the Assumptions of Analysis of Variance - Yanyan Sheng; Chapter 23: Best Practices in ANOVA - David Howell; Chapter24: Logistic Regression in the Social Sciences - Jason E. King; Chapter 25: Bringing balance and accuracy to odds ratios - Jason W. Osborne; Chapter 26: Advanced Topics in Logistic Regression: Polytomous Response Variables - Carolyn J. Anderson & Leslie Rutkowski; Chapter 27: Enhancing Accuracy in Research Using Regression Mixture Analysis - Cody S. Ding; Chapter 28: Mediation, Moderation, and the Study of Individual Differences - A. Alexander Beaujean; Part V: Best Advanced Practices in Quantitative Methods - Jason W. Osborne; Chapter 29: Hierarchical Linear Modeling: What it is and when Researchers should use it - Jason W. Osborne; Chapter 30: Analysis of longitudinal data: Advantages of Hierarchical Linear Modeling and growth curve analysis over repeated measures ANOVA - Frans E.S. Tan; Chapter 31: Analysis of Moderator Effects in Meta-Analysis - Wolfgang Viechtbauer; Chapter 32: Best Practices in Structural Equation Modeling - Ralph O. Mueller & Gregory R. Hancock; Chapter 33: Introduction to Bayesian Modeling for Social Sciences - Gianluca Baio & Marta Blangiardo; Chapter 34: Using R for Data Analysis: A Best Practice for Research - Ken Kelley, Keke Lai, & Po-Ju Wu; Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference - Elizabeth A. Stuart & Donald B. Rubin; ; ; ; ;
• Publisher Details: Thousand Oaks : SAGE Publications, Inc
• Publication Date: 2007
• Extent: 1 online resource (608 pages)
• Subjects: 001.4/2; Social sciences -- Research -- Methodology; Social sciences -- Mathematical models
• Languages: English
• ISBNs: 9781483333052; 1483333051
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.