Best Practices in Quantitative Methods

Best Practices in Quantitative Methods

By: Jason Osborne (author)Hardback

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Best Practices in Quantitative Methods follows the tradition of 'handbooks' in that it calls on the top researchers in the field to share with us what they know. In this case, the focus of the chapters is on best practices for the vast field of quantitative methods. The volume provides readers with the most effective, evidence-based ways to use and analyze quantitative methods and quantitative data across the social and behavioral sciences and education .The text is divided into three main sections: Basics of Best Practices, in which a comprehensive review of basic statistic and methodological practices is covered, including core statistical methods and critical data analysis issues such as power, effect sizes, and assumptions; Advanced Best Practices, leading with logistic regression, and moving through IRT, Rasch Measurement, HLM, Meta-Analysis, and the inimitable area of Sampling; and The Implications of Best Practices, including a discussion of the ethical implications of quantitative analysis. 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-rangning examples along with any empirical evidence to show why certain techniques are better. The book encourages best practices in three very distinct ways: 1) Some chapters will describe important implicit knowledge to readers. For example, one of the most common data transformations is the square root transformation. Statistics and quantitative methods are filled with examples of these seemingly mundane aspects of research life that makes a substantial difference. Chapters in this book gather the important details, make them accessible to readers, and demonstrate why it is important to pay attention to these details. 2) Other chapters compare and contrast analytic techniques to give readers information they need to decide the best way to analyze particular data. For example, exploratory factor analysis has up to eight extraction methods, several rotation options, multiple ways to decide how many factors you have, and it is often the case that the options are not clearly described or discussed. Some of the chapters will examine instances where there are multiple options for doing things, and make recommendations as to what the obesto choice (or choices, as what is best often depends on the circumstances) are.3 ) Finally, there are always new procedures being developed and disseminated. Many times (not all) newer procedures represent improvements over old procedures. Some chapters will 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.This book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource to go to for practical and sound advice from leading experts in quantitative methods.

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About Author

Jason W. Osborne is Associate Provost and Dean of the Graduate School at Clemson University in Clemson, South Carolina. He is also Professor of Applied Statistics in the Department of Mathematical Sciences, with a secondary appointment in Public Health Science. He teaches and publishes on "best practices" in quantitative and applied research methods. He has served as evaluator or consultant on projects in public education (K-12), instructional technology, higher education, nursing and health care, medicine and medical training, epidemiology, business and marketing, and jury selection in death penalty cases. He served as founding editor of Frontiers in Quantitative Psychology and Measurement and has been on the editorial boards of several other journals (such as Practical Assessment, Research, and Evaluation). Jason also publishes on identification with academics (how a student's self-concept impacts motivation to succeed in academics) and on issues related to social justice and diversity (such as Stereotype Threat). He is the very proud father of three, and holds the rank of third degree black belt in Songahm Tae Kwon Do. The rest is subject to change without notice (as Anne McCaffrey wrote in her bio).


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

Product Details

  • publication date: 15/01/2008
  • ISBN13: 9781412940658
  • Format: Hardback
  • Number Of Pages: 608
  • ID: 9781412940658
  • weight: 1197
  • ISBN10: 1412940656

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