Researchers are increasingly encouraged to report effect size statistics in their studies. However, many struggle with choosing the appropriate measure, especially in repeated measures designs. Generalized eta squared (η_G²) is a recommended effect size statistic for such designs, as it provides comparable estimates across between-subjects and within-subjects designs. Unlike traditional eta squared (η²) and partial eta squared (η_P²), η_G² accounts for both manipulated and measured factors, ensuring comparability across different study designs. It is easily computed from standard statistical software outputs, such as SPSS, and is recommended for routine reporting in research. η_G² differs from η² and η_P² in its denominator, which includes variance due to individual differences and excludes variance from manipulated factors. This makes it more accurate for comparing effects across studies with varying designs. While η_P² is often used in factorial designs, it can be problematic when comparing effects across between-subjects and within-subjects designs. η_G² addresses this issue by including all sources of variance that involve measured factors in the denominator, while excluding those involving only manipulated factors. Despite its advantages, η_G² is not without limitations. It assumes a traditional univariate ANOVA approach and may not be suitable for multivariate or multilevel designs. Additionally, it does not account for overestimation of effect sizes, which can occur in small samples. However, it is generally preferred over η² and η_P² for repeated measures designs due to its comparability across studies. In summary, researchers are encouraged to report η_G² when using ANOVAs, particularly in repeated measures designs. This statistic provides a more accurate and comparable measure of effect size, facilitating meaningful comparisons across studies. While other effect size measures may be useful in specific contexts, η_G² is the recommended choice for repeated measures designs. Its computation is straightforward, and it aligns with common guidelines for interpreting effect sizes in psychological research.Researchers are increasingly encouraged to report effect size statistics in their studies. However, many struggle with choosing the appropriate measure, especially in repeated measures designs. Generalized eta squared (η_G²) is a recommended effect size statistic for such designs, as it provides comparable estimates across between-subjects and within-subjects designs. Unlike traditional eta squared (η²) and partial eta squared (η_P²), η_G² accounts for both manipulated and measured factors, ensuring comparability across different study designs. It is easily computed from standard statistical software outputs, such as SPSS, and is recommended for routine reporting in research. η_G² differs from η² and η_P² in its denominator, which includes variance due to individual differences and excludes variance from manipulated factors. This makes it more accurate for comparing effects across studies with varying designs. While η_P² is often used in factorial designs, it can be problematic when comparing effects across between-subjects and within-subjects designs. η_G² addresses this issue by including all sources of variance that involve measured factors in the denominator, while excluding those involving only manipulated factors. Despite its advantages, η_G² is not without limitations. It assumes a traditional univariate ANOVA approach and may not be suitable for multivariate or multilevel designs. Additionally, it does not account for overestimation of effect sizes, which can occur in small samples. However, it is generally preferred over η² and η_P² for repeated measures designs due to its comparability across studies. In summary, researchers are encouraged to report η_G² when using ANOVAs, particularly in repeated measures designs. This statistic provides a more accurate and comparable measure of effect size, facilitating meaningful comparisons across studies. While other effect size measures may be useful in specific contexts, η_G² is the recommended choice for repeated measures designs. Its computation is straightforward, and it aligns with common guidelines for interpreting effect sizes in psychological research.