COMPARISON OF SOME EFFECT SIZE MEASURES IN SIMPLE AND MULTIPLE LINEAR REGRESSION MODELS
Abstract
It is very important that the results of the statistical analysis are understandable. Therefore, while reporting the results of statistical analysis, some effect size measures should be given along with the P-value. In this study, some effect size measure Eta-Squared, Epsilon Squared and Omega-Squared were compared in terms of their performance (bias) in simple and multiple linear regression models. Results of simulation runs showed that the Epsilon and Omega-Squared estimates were quite unbiased when compared to Eta-Squared. Thus, it could be concluded that reporting Epsilon or Omega Squared is more appropriate to evaluate the practical significance along with P-values in simple and multiple linear regression models.
Keywords
practical significance, eta-squared, epsilon-squared, omega-squared, regression analysis, simulation
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