Best practices in quantitative methods

Introduction

Jason W. Osborne<br />Part I: Best Practices in Measurement

Jason Osborne<br />Chapter 1: The New Stats: Attitudes for the Twenty-First Century

Fiona Fidler & Geoff Cumming<br />Chapter 2: Using Criterion-Referenced Assessments for Setting Standards and Making Decisions: Some Conceptual & Technical Issues

Thomas Kellow & Victor Willson<br />Chapter 3: Best Practices in Inter-rater Reliability: Assumptions and Implications of three common approaches

Steve Stemler<br />Chapter 4: An Introduction to Rasch Measurement

Cherdsak Iramaneerat, Everett V. Smith, Jr., & Richard M. Smith<br />Chapter 5: Applications of the Multi-Faceted Rasch Model

Edward W. Wolfe & Lidia Dobria<br />Chapter 6: Best Practices in Exploratory Factor Analysis

Jason W. Osborne, Anna B. Costello, & J. Thomas Kellow<br />Part II: Selected Best Practices in Research Design

Jason W. Osborne<br />Chapter 7: A Rational Foundation for Scientific Decisions: The Case for the Probability of Replication Statistic

Peter R. Killeen<br />Chapter 8: Best Practices in Mixed Methods Research

Jessica T. DeCuir-Gunby<br />Chapter 9: Designing a Rigorous Small Sample Study

Naomi Jeffery Petersen<br />Chapter10: Replication in Field Studies

William D. Schafer<br />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<br />Chapter 12: Fixed and Mixed Effects Models in Meta-Analysis

Spyros Konstantopoulos<br />Part III: Best Practices in Data Cleaning and the Basics of Data Analysis

Jason W. Osborne<br />Chapter 13: Best Practices in Data Transformations: The Overlooked Effect of Minimum Values

Jason W. Osborne<br />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<br />Chapter 15: How to Deal With Missing Data

Jason C. Cole<br />Chapter16: Is Disattenuation of Effects a Best Practice?

Jason W. Osborne<br />Chapter 17: Computing and Interpreting Effect Sizes, Confidence Intervals, & Confidence Intervals for Effect Sizes

Bruce Thompson<br />Chapter 18: Robust Methods for Detecting Associations

Rand R. Wilcox<br />Part IV: Best Practices of Quantitative Methods

Jason W. Osborne<br />Chapter 19: Resampling: A Conceptual and Procedural Introduction

Chong Ho Yu<br />Chapter 20: Creating Valid Prediction Equations in Multiple Regression: Shrinkage, Double Cross-Validation, and Confidence Intervals around Predictions

Jason W. Osborne<br />Chapter 21: Using Poisson Regression to Analyze Count Data

E. Michael Nussbaum, Sherif Elsadat, & Ahmed H. Khago<br />Chapter 22: Testing the Assumptions of Analysis of Variance

Yanyan Sheng<br />Chapter 23: Best Practices in ANOVA

David Howell<br />Chapter24: Logistic Regression in the Social Sciences

Jason E. King<br />Chapter 25: Bringing balance and accuracy to odds ratios

Jason W. Osborne<br />Chapter 26: Advanced Topics in Logistic Regression: Polytomous Response Variables

Carolyn J. Anderson & Leslie Rutkowski<br />Chapter 27: Enhancing Accuracy in Research Using Regression Mixture Analysis

Cody S. Ding<br />Chapter 28: Mediation, Moderation, and the Study of Individual Differences

A. Alexander Beaujean<br />Part V: Best Advanced Practices in Quantitative Methods

Jason W. Osborne<br />Chapter 29: Hierarchical Linear Modeling: What it is and when Researchers should use it

Jason W. Osborne<br />Chapter 30: Analysis of longitudinal data: Advantages of Hierarchical Linear Modeling and growth curve analysis over repeated measures ANOVA

Frans E.S. Tan<br />Chapter 31: Analysis of Moderator Effects in Meta-Analysis

Wolfgang Viechtbauer<br />Chapter 32: Best Practices in Structural Equation Modeling

Ralph O. Mueller & Gregory R. Hancock<br />Chapter 33: Introduction to Bayesian Modeling for Social Sciences

Gianluca Baio & Marta Blangiardo<br />Chapter 34: Using R for Data Analysis: A Best Practice for Research

Ken Kelley, Keke Lai, & Po-Ju Wu<br />Best Practices in Quasi-Experimental Designs: Matching Methods for Causal Inference

Elizabeth A. Stuart & Donald B. Rubin<br /><br /><br /><br /><br />