What is eta mean?

Eta-Squared (η²) Explained

Eta-squared (η²) is a measure of effect size in analysis of variance (ANOVA). It estimates the proportion of variance in the dependent variable that is explained by an independent variable or factor. It is calculated as the ratio of the sum of squares between groups (SS_between) to the total sum of squares (SS_total).

• Formula: η² = SS_between / SS_total

• Interpretation: Eta-squared values range from 0 to 1. A higher value indicates a larger proportion of the variance in the dependent variable is explained by the independent variable. For example, an eta-squared of 0.25 means that 25% of the variance in the dependent variable is explained by the independent variable.

• Advantages: Simple to calculate and understand. Provides a standardized measure of effect size that can be compared across studies.

• Disadvantages: Can overestimate the population effect size, especially with small sample sizes. Can be inflated if there are many factors in the model. Is not directly comparable to other effect size measures such as <a href="https://www.wikiwhat.page/kavramlar/Cohen's%20d">Cohen's d</a>.

• Guidelines for interpretation (Cohen's guidelines for effect size, which are general and need to be considered within the context of the specific field of research):

  • Small effect: η² = 0.01
  • Medium effect: η² = 0.06
  • Large effect: η² = 0.14

• Usage: Commonly used in ANOVA and similar statistical tests to quantify the practical significance of the findings. It is a <a href="https://www.wikiwhat.page/kavramlar/descriptive%20statistic">descriptive statistic</a>, providing information about the sample data.

• Alternatives: Other effect size measures, such as omega-squared (ω²) and epsilon-squared (ε²), are less biased estimators of the population effect size. Omega-squared is generally preferred as it provides a less biased estimate. Partial eta-squared is used to report effect size in factorial designs and mixed designs.