2003/01/01 by Stephen Olejnik, James Algina · 3 citations
Psychology · Decision Sciences · Computer Science · Mathematics · #Behavioral and Psychological Studies #Psychometric Methodologies and Testing #Advanced Statistical Modeling Techniques #Statistics #Omega #Mean squared error #Variance (accounting) #Mathematics #Measure (data warehouse) #Econometrics #Analysis of variance #Statistical analysis #Statistical hypothesis testing #Computer science #Data mining #Economics #Physics
paper · doi:10.1037/1082-989x.8.4.434
openalex publication_date 2003/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variety of research designs.