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Accolades:

Suggested reading list Jan 2009 International Initiative for Impact Evaluation.

"'Best Practices in Quantitative Methods', edited by Jason Osborne, is a recent collection of chapters by authorities on many aspects of quantitative methods, many of which are directly applicable to impact evaluation."

 

 

 

 

 

 



Table of Contents

(if you know of a best practice not represented here, email me and perhaps we can get it in the next edition! If you have an idea for a great exercise or example of a topic presented in a particular chapter, email me and I'll include it and credit you!)

Chapter #

Title

Chapter resources:

     
Introduction

Using Best Practices is a Moral and Ethical Oblication, Jason W. Osborne

  
1.

The New Stats: Attitudes for the 21st Century, Fiona Fidler and Geoff Cumming

 Chapter introduction
     
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
 Chapter 02 introduction
3.
Best Practices in Interrater Reliability:  Three common approaches, Steve E. Stemler and Jessica Tsai
 Data for Chapter 03 examples
4.
An Introduction to Rasch Measurement, Cherdsak Iramaneerat, Everett V. Smith Jr., and Richard M. Smith
 Tools for Chapter 04 examples
5.
Applications of the Multifaceted Rasch Model, Edward W. Wolfe and Lidia Dobria
 Chapter 05 introduction and link to example application of MFRM
6.
Best Practices in Exploratory Factor Analysis, Jason W. Osborne, Anna B. Costello, and J. Thomas Kellow
 Chapter 06 introduction and information
     
Part II: Selected Best Practices in Research Design
7.
Replication Statistics as a replacement for significance testing:  Best practices in scientific decision-making, Peter R. Killeen
 Introducing p(rep) and some tools for calculating it
8.   
Mixed Methods Research in the Social Sciences, Jessica T. DeCuir-Gunby
 Chapter 08 introduction
9.
Designing a Rigorous Small Sample Study, Naomi Jeffery Petersen
 Chapter 09 introduction
10.
Replication in Field Study Design, William D. Shafer
 Chapter 10 introduction
11.
Best practices in quasi-experimental designs:  Why matched subjects designs are superior to ANCOVA, Elizabeth A. Stuart and Donald B. Rubin
 Chapter 11 datasets and notes
12.
An introduction to Meta-Analysis, Spyros Konstantopoulos
 Chapter 12 introduction
     
Part III: Best Practices in Data Cleaning and the Basics of Data Analysis
13.
Best Practices in Data Transformations: The Overlooked Effect of Minimum Values, Jason W. Osborne
 Chapter 13 overview
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
 Chapter 14 further exploration
15.
How to Deal With Missing Data: Conceptual Overview and Details for Implementing Two Modern Methods, Jason C. Cole
 Chapter 15 introduction
16.
Is Disattenuation of Effects a Best Practice? Jason W. Osborne
 Chapter 16 introduction
17.
Computing and Interpreting Effect Sizes, Confidence Intervals, and Confidence Intervals for Effect Sizes, Bruce Thompson
 Chapter 17 resources
18.
Robust Methods for Detecting and Describing Associations, Rand R. Wilcox
 Chapter 18 software resources
     
Part IV: Best Practices in Quantitative Methods
19. Resampling: A Conceptual and Procedural Introduction, Chong Ho Yu Chapter 19 data files and resources
20. Creating Valid Prediction Equations in Multiple Regression: Shrinkage, Double Cross-Validation, and Confidence Intervals Around Predictions, Jason W. Osborne Chapter 20 data and exercises for cross-validation and etc.
21. Best Practices in Analyzing Count Data: Poisson Regression, E. Michael Nussbaum, Sherif Elsadat, and Ahmed H. Khago Chapter 21 introduction and data sets
22. Testing the Assumptions of Analysis of Variance, Yanyan Sheng Chapter 22 data sets and activities
23. Best Practices in the Analysis of Variance, David Howell Chapter 23 overview and links to data
24. Logistic Regression in the Social Sciences, Jason E. King Chapter 24 overview and data links
25. Bringing Balance and Technical Accuracy to Reporting Odds Ratios and the Results of Logistic Regression Analyses, Jason W. Osborne Chapter 25 summary and data table
26. Multinomial Logistic Regression Models, Carolyn J. Anderson and Leslie Rutkowski Chapter 26 data sets and activities
27. Enhancing Accuracy in Research Using Regression Mixture Analysis, Cody S. Ding Chapter 27 overview and description of data
28. Mediation, Moderation, and the Study of Individual Differences, A. Alexander Beaujean Chapter 28 Data description and data set
     
Part V: Best Advanced Practices in Quantitative Methods
29. A Brief Introduction to Hierarchical Linear Modeling, Jason W. Osborne Chapter 29 overview (data coming)
30. Best Practices in Analysis of Longitudinal Data: A Multilevel Approach, Frans E. S. Tan Chapter 30 data sets
31. Analysis of Moderator Effects in Meta-Analysis, Wolfgang Viechtbauer Chapter 31 overview and data
32. Best Practices in Structural Equation Modeling, Ralph O. Mueller and Gregory R. Hancock Chapter 32 overview and data summary
33. Introduction to Bayesian Modeling for the Social Sciences, Gianluca Biao and Marta Blangiardo Chapter 33 overview
34. Using R for Data Analysis: A Best Practice for Research, Ken Kelley, Keke Lai, and Po-Ju Wu Chapter 34 datasets and notes