2007/01/01 by Ken Kelley · 3 citations
Decision Sciences · Computer Science · Mathematics · #Meta-analysis and systematic reviews #Data Analysis with R #Psychometric Methodologies and Testing #Confidence interval #Robust confidence intervals #CDF-based nonparametric confidence interval #Confidence distribution #Statistics #Credible interval #Confidence and prediction bands #Computer science #Mathematics
paper · doi:10.18637/jss.v020.i08
openalex publication_date 2007/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
The behavioral, educational, and social sciences are undergoing a paradigmatic shift in methodology, from disciplines that focus on the dichotomous outcome of null hypothesis significance tests to disciplines that report and interpret effect sizes and their corresponding confidence intervals. Due to the arbitrariness of many measurement instruments used in the behavioral, educational, and social sciences, some of the most widely reported effect sizes are standardized. Although forming confidence intervals for standardized effect sizes can be very beneficial, such confidence interval procedures are generally difficult to implement because they depend on noncentral t, F, and x<sup>2</sup> distributions. At present, no main-stream statistical package provides exact confidence intervals for standardized effects without the use of specialized programming scripts. Methods for the Behavioral, Educational, and Social Sciences (MBESS) is an R package that has routines for calculating confidence intervals for noncentral t, F, and x<sup>2</sup> distributions, which are then used in the calculation of exact confidence intervals for standardized effect sizes by using the confidence interval transformation and inversion principles. The present article discusses the way in which confidence intervals are formed for standardized effect sizes and illustrates how such confidence intervals can be easily formed using MBESS in R.