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Best Practices in Quantitative Methods

Best Practices in Quantitative Methods

November 2007 | 608 pages | SAGE Publications, Inc
The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically.  Editor Jason W. Osborne designed 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.

Jason W. Osborne
Jason Osborne
Part I: Best Practices in Measurement
Fiona Fidler & Geoff Cumming
Chapter 1: The New Stats: Attitudes for the Twenty-First Century
Thomas Kellow & Victor Willson
Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues
Steve Stemler
Chapter 3: Best Practices in Inter-rater Reliability: Assumptions and Implications of three common approaches
Cherdsak Iramaneerat, Everett V. Smith, Jr., & Richard M. Smith
Chapter 4: An Introduction to Rasch Measurement
Edward W. Wolfe & Lidia Dobria
Chapter 5: Applications of the Multi-Faceted Rasch Model
Jason W. Osborne, Anna B. Costello, & J. Thomas Kellow
Chapter 6: Best Practices in Exploratory Factor Analysis
Jason W. Osborne
Part II: Selected Best Practices in Research Design
Peter R. Killeen
Chapter 7: A Rational Foundation for Scientific Decisions: The Case for the Probability of Replication Statistic
Jessica T. DeCuir-Gunby
Chapter 8: Best Practices in Mixed Methods Research
Naomi Jeffery Petersen
Chapter 9: Designing a Rigorous Small Sample Study
William D. Schafer
Chapter10: Replication in Field Studies
Elizabeth A. Stuart & Donald B. Rubin
Chapter 11: Best practices in ANCOVA may mean not using ANCOVA: Why paired subjects designs are a better choice
Spyros Konstantopoulos
Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis
Jason W. Osborne
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 & Amy Overbay
Chapter 14: Best Practices in Data Cleaning: How Outliers can increase error rates and decrease the quality and precision of your results
Jason C. Cole
Chapter 15: How to Deal With Missing Data
Jason W. Osborne
Chapter16: Is Disattenuation of Effects a Best Practice?
Bruce Thompson
Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, & Confidence Intervals for Effect Sizes
Rand R. Wilcox
Chapter 18: Robust Methods for Detecting Associations
Jason W. Osborne
Part IV: Best Practices of Quantitative Methods
Chong Ho Yu
Chapter 19: Resampling: A Conceptual and Procedural Introduction
Jason W. Osborne
Chapter 20: Creating Valid Prediction Equations in Multiple Regression: Shrinkage, Double Cross-Validation, and Confidence Intervals around Predictions
E. Michael Nussbaum, Sherif Elsadat, & Ahmed H. Khago
Chapter 21: Using Poisson Regression to Analyze Count Data
Yanyan Sheng
Chapter 22: Testing the Assumptions of Analysis of Variance
David Howell
Chapter 23: Best Practices in ANOVA
Jason E. King
Chapter24: Logistic Regression in the Social Sciences
Jason W. Osborne
Chapter 25: Bringing balance and accuracy to odds ratios
Carolyn J. Anderson & Leslie Rutkowski
Chapter 26: Advanced Topics in Logistic Regression: Polytomous Response Variables
Cody S. Ding
Chapter 27: Enhancing Accuracy in Research Using Regression Mixture Analysis
A. Alexander Beaujean
Chapter 28: Mediation, Moderation, and the Study of Individual Differences
Jason W. Osborne
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
Frans E.S. Tan
Chapter 30: Analysis of longitudinal data: Advantages of Hierarchical Linear Modeling and growth curve analysis over repeated measures ANOVA
Wolfgang Viechtbauer
Chapter 31: Analysis of Moderator Effects in Meta-Analysis
Ralph O. Mueller & Gregory R. Hancock
Chapter 32: Best Practices in Structural Equation Modeling
Gianluca Baio & Marta Blangiardo
Chapter 33: Introduction to Bayesian Modeling for Social Sciences
Ken Kelley, Keke Lai, & Po-Ju Wu
Chapter 34: Using R for Data Analysis: A Best Practice for Research
Elizabeth A. Stuart & Donald B. Rubin
Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference
Key features
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 "best" 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.

Sample Materials & Chapters

Chapter 7

Chapter 11

Chapter 32

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