Best Practices in Quantitative Methods

Description

The contributors toBest Practices in Quantitative Methodsenvision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. EditorJason W. Osbornedesigned this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers:The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques:The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice--or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques:The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience:Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods. Using Best Practices Is a Moral and Ethical Obligation Jason W. Osborne 1.  The New Stats: Attitudes for the 21st Century Fiona Fidler and Geoff Cumming Part I: Best Practices in Measurement 2.  Setting Standards and Establishing Cut Scores on Criterion-Referenced Assessments: Some Technical and Practical Considerations J. Thomas Kellow and Victor L. Willson 3.  Best Practices in Interrater Reliability: Three Common Approaches Steven E. Stemler and Jessica Tsai 4.  An Introduction to Rasch Measurement Cherdsak Iramaneerat, Everett V. Smith Jr., and Richard M. Smith 5.  Applications of the Multifaceted Rasch Model Edward W. Wolfe and Lidia Dobria 6.  Best Practices in Exploratory Factor Analysis Jason W. Osborne, Anna B. Costello, and J. Thomas Kellow Part II: Selected Best Practices in Research Design 7.  Replication Statistics Peter R. Killeen 8.  Mixed Methods Research in the Social Sciences Jessica T. DeCuir-Gunby 9.  Designing a Rigorous Small Sample Study Naomi Jeffery Petersen 10.  Replicated Field Study Design William D. Schafer 11.  Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference Elizabeth A. Stuart and Donald B. Rubin 12.  An Introduction to Meta-Analysis Spyros Konstantopoulos Part III: Best Practices in Data Cleaning and the Basics of Data Analysis 13.  Best Practices in Data Transformation: The Overlooked Effect of Minimum Values Jason W. Osborne 14.  Best Practices in Data Cleaning: How Outliers and “Fringeliers” Can Increase Error Rates and Decrease the Quality and Precision of Your Results Jason W. Osborne and Amy Overbay 15.  How to Deal With Missing Data: Conceptual Overview and Details for Implementing Two Modern Methods Jason C. Cole 16.  Is Disattenuation of Effects a Best Practice? Jason W. Osborne 17.  Computing and Interpreting Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes Bruce Thompson 18.  Robust Methods for Detecting and Describing Associations Rand R. Wilcox Part IV: Best Practices in Quantitative Methods 19.  Resampling: A Conceptual and Procedural Introduction Chong Ho Yu 20.  Creating Valid Prediction Equations in Multiple Regression: Shrinkage, Double Cross-Validation, and Confidence Intervals Around Predictions Jason W. Osborne 21.  Best Practices in Analyzing Count Data: Poisson Regression E. Michael Nussbaum, Sherif Elsadat, and Ahmed H. Khago 22.  Testing the Assumptions of Analysis of Variance Yanyan Sheng 23.  Best Practices in the Analysis of Variance David Howell 24.  Binary Logistic Regression Jason E. King 25.  Bringing Balance and Technical Accuracy to Reporting Odds Ratios and the Results of Logistic Regression Analyses Jason W. Osborne 26.  Multinomial Logistic Regression Carolyn J. Anderson and Leslie Rutkowski 27.  Enhancing Accuracy in Research Using Regression Mixture Analysis Cody S. Ding 28.  Mediation, Moderation, and the Study of Individual Differences A. Alexander Beaujean Part V: Best Advanced Practices in Quantitative Methods 29.  A Brief Introduction to Hierarchical Linear Modeling Jason W. Osborne 30.  Best Practices in Analysis of Longitudinal Data: A Multilevel Approach Frans E. S. Tan 31.  Analysis of Moderator Effects in Meta-Analysis Wolfgang Viechtbauer 32.  Best Practices in Structural Equation Modeling Ralph O. Mueller and Gregory R. Hancock 33.  Introduction to Bayesian Modeling for the Social Sciences Gianluca Baio and Marta Blangiardo 34.  Using R for Data Analysis: A Best Practice for Research Ken Kelley, Keke Lai, and Po-Ju Wu Index About the Editor About the Contributors