by user39531
Last Updated January 14, 2018 08:19 AM

After conducting a one way repeated measures ANOVA, I obtained the following value for **eta squared=0.2861** (28.61%) and for **Cohen's d =1.25**

Cohen's guideline for effect's size: Small: 0.01; Medium: 0.059; Large: 0.138;

I understand that 28.61% of the independent var influenced the dependent var. How do I interpret my results?

1) **Can we deduce that it is a large effect size by referring to eta-squared and cohen's d?**

2) **I have manually calculated the output by referring to my descriptive IBM SPSS statistics output.Kindly advise if there is a way to obtain cohen's d and eta-squared directly in the IBM SPSS output.**

Thanks

I think what complicates the analysis of the effect size(s) here is the repeated measures (or within-subjects) design. Because of this, the calculation of the effect sizes might be different from simple one-way ANOVA. This article compares different effect sizes for within- and between-subject designs. At the end of the article you will see that, it suggests, for within-subjects designs, "effect sizes that control for intra-subjects variability ($\eta^2_p$ and $\omega^2_p$), or that take the correlation between measurements into account (Cohen's *d*_{z})" (Lakens, 2013).

So, it might be better to take into consideration repeated measures design in your estimation of the effect size. But if we assume that your calculations are correct, Cohen's *d* (1.25) indicates a large effect size (Gamst et.al., 2008, p.44). However, the interpretation of the eta-squared depends on the context of your research.

I am not an SPSS user but as far as I understand SPSS already reports partial eta-squared ($\eta^2_p$). Here is an example of how to do ANOVA with repeated measures using SPSS (it also shows how to get effect size).

Gamst, G., Meyers, L. S., & Guarino, A. J. (2008). *Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS*. Cambridge: Cambridge University Press.

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. *Frontiers in Psychology*, 4. http://doi.org/10.3389/fpsyg.2013.00863

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