What is a large effect size for partial eta squared?

Hello, I'm a seasoned expert in statistical analysis and research methodology. When it comes to interpreting effect sizes, especially in the context of ANOVA and regression analyses, understanding the nuances of different measures is crucial for accurate interpretation of results. Effect size is a measure of the strength or magnitude of the difference or relationship between variables in a study. It's an important concept because it provides a standardized way to quantify the practical significance of findings, beyond the mere statistical significance indicated by p-values. In the context of ANOVA, partial eta squared is a common effect size measure that represents the proportion of total variance in the dependent variable that is explained by a particular independent variable, after controlling for other variables in the model. It's particularly useful in the analysis of factorial designs where the effect of one factor is considered while holding the effects of other factors constant. Now, when we talk about what constitutes a "large" effect size for partial eta squared, it's important to reference established benchmarks. Unlike the suggestion provided, which references the square of a Pearson correlation (R-squared), the benchmarks for eta squared are typically smaller because it's a measure of variance explained in the context of a specific independent variable in a model, not the total variance explained by all predictors combined. The benchmarks for eta squared are often cited as follows: - Small: Around 0.01 - Medium: Around 0.06 - Large: Around 0.14 These benchmarks are not absolute and can vary depending on the field of study and the context of the research question. However, they provide a general guideline for interpreting the magnitude of effects. It's also worth noting that the choice of what constitutes a large effect size can depend on the practical significance in a given field. For instance, in a field where even small changes can have substantial real-world implications, an effect size considered medium might be seen as large. In summary, while the suggestion to use the square of a Pearson correlation for effect sizes in partial eta squared might provide a starting point, it does not align with the more commonly accepted benchmarks within the statistical community. A large effect size for partial eta squared is generally considered to be around 0.14 or higher, which indicates a meaningful proportion of variance in the dependent variable is accounted for by the independent variable in question. Now, let's proceed with the translation into Chinese.

Suggestion : Use the square of a Pearson correlation for effect sizes for partial -- 2 (R-squared in a multiple regression) giving 0.01 (small), 0.09 (medium) and 0.25 (large) which are intuitively larger values than eta-squared.Dec 12, 2017