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Best Practices in Quantitative Methods

Best Practices in Quantitative Methods

January 2008 | 608 pages | SAGE Publications, Inc
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 ôbestö 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.

Jason W. Osborne
Cherdsak Iramaneerat, Everett V. Smith, Jr., & Richard M. Smith
Chapter 1: The New Stats: Attitudes for the Twenty-First Century
Part I: Best Practices in Measurement
Thomas Kellow & Victor Willson
Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues
Jason Osborne
Part I: Best Practices in Measurement
Gianluca Baio & Marta Blangiardo
Chapter 3: Estimating Inter-Rater Reliability: Assumptions and Implications of Three Common Approaches
Jason W. Osborne
Chapter 4: An Introduction to Rasch Measurement
Peter R. Killeen
Chapter 5: Applications of the Multi-Faceted Rasch Model
Jason W. Osborne
Chapter 6: Best Practices in Exploratory Factor Analysis
Part II: Selected Best Practices in Research Design
Jason W. Osborne
Chapter 7: Replication Statistics
Wolfgang Viechtbauer
Chapter 8: Mixed Methods Research in the Social Sciences
Bruce Thompson
Chapter 9: Designing a Rigorous Small Sample Study
Elizabeth A. Stuart & Donald B. Rubin
Chapter 11: Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference
Ken Kelley, Keke Lai, & Po-Ju Wu
Chapter 12: An Introduction to Meta-Analysis
Spyros Konstantopoulos
Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis
Part III: Best Practices in Data Cleaning and the Basics of Data Analysis
Edward W. Wolfe & Lidia Dobria
Chapter 14: Best Practices in Data Cleaning: How Outliers and "Fringeliers" Can Increase Error Rates and Decrease the Quality and Precision of Your Results
Naomi Jeffery Petersen
Chapter 15: How to Deal with Missing Data: Conceptual Overview and Details for Implementing Two Modern Methods
Jason C. Cole
Chapter 16: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual and Technical Issues
Elizabeth A. Stuart & Donald B. Rubin
Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes
Jason W. Osborne
Chapter 18: Robust Methods for Detecting and Describing Associations
Part IV: Best Practices of Quantitative Methods
Jason W. Osborne
Chapter 19: Resampling: A Conceptual and Procedural Introduction
Rand R. Wilcox
Chapter 21: Advanced Topics in Power Analysis
Jason W. Osborne & Amy Overbay
Chapter 21: Best Practices in Analyzing Count Data: Poisson Regression
Chong Ho Yu
Chapter 22: Testing the Assumptions of Analysis of Variance
Jason W. Osborne
Chapter 23: Best Practices in the Analysis of Variance
E. Michael Nussbaum, Sherif Elsadat, & Ahmed H. Khago
Chapter 24: Binary Logistic Regression
Yanyan Sheng
Chapter 26: Multinomial Logistic Regression
David Howell
Chapter 27: Mediation, Moderation, and the Study of Individual Differences
A. Alexander Beaujean
Chapter 28: Mediation, Moderation, and the Study of Individual Differences
Part V: Best Advanced Practices in Quantitative Methods
Carolyn J. Anderson & Leslie Rutkowski
Chapter 30: Hierarchical Linear Modeling: What It is and When Researchers Should Use It
Cody S. Ding
Chapter 30: Best Practices in Analysis of Longitudinal Data: A Multilevel Approach
Jason W. Osborne
Chapter 32: Best Practices in Structural Equation Modeling
Jason W. Osborne
Chapter 33: Introduction to Bayesian Modeling for the Social Sciences
Frans E.S. Tan
Chapter 35: Measuring Accuracy in Psychological Research
Ralph O. Mueller & Gregory R. Hancock
Chapter 37: Ethical Implications for Best Practices in Quantitative Methods
Jason W. Osborne, Anna B. Costello, & J. Thomas Kellow
(Dropped) Chapter 4: Best Practices in Graphically Displaying Data
Jessica T. DeCuir-Gunby
(Dropped) Chapter 7: Choosing a Demoninator
William D. Schafer
(Dropped) Chapter 9: Four Assumptions of Multiple Regression You Should ALWAYS Check
Jason E. King
(Dropped) Chapter 28: An Introduction to Item Response Theory
Steve Stemler
(Dropped) A Framework for Model Building in Social Science Research

Sample Materials & Chapters

Chapter 7

Chapter 11

Chapter 32

For instructors

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ISBN: 9781412940658

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