Eta squared is expressed as sum of squares between groups (SS_effect) divided by the total sum of squares of the dependent variable (SS_total), ${\mathit{\eta }}^{\mathrm{2}}=\frac{{\mathit{\text{SS}}}_{\mathit{\text{effect}}}}{{\mathit{\text{SS}}}_{\mathit{\text{total}}}}$. The quantity is the same with the usual r squared (R²) which we use as a measure of degree that a model explains the data. The estimate of η² value was calculated as $\mathrm{0.341}\left(=\frac{{\mathit{\text{SS}}}_{\mathit{\text{effect}}}}{{\mathit{\text{SS}}}_{\mathit{\text{total}}}}=\frac{\mathit{1996.998}}{\mathit{5863.715}}\right)$, which means the ANOVA model using the MATERIAL independent variable explained 34.1% of variability in the dependent variable (Table 2). Eta squared can be converted into Cohen's f and vice versa as follows: $\mathit{f}=\sqrt{{\mathit{\eta }}^{\mathrm{2}}/\left(\mathrm{1}-{\mathit{\eta }}^{\mathrm{2}}\right)}$ or η² = f ² / (1 + f ²).

The effect size of partial Eta square measure is preferred to Eta squared in a two-way factorial design. The main reason is that when other independent variables are included in the model, η² value becomes smaller compared to the original value, therefore it cannot represent an effect size in multivariate situation. The partial Eta squared is expressed as SS_effect divided by the sum of SS_effect and SS_error, ${\mathit{\eta }}_{\mathit{p}}^{\mathrm{2}}=\frac{{\mathit{\text{SS}}}_{\mathit{\text{effect}}}}{{\mathit{\text{SS}}}_{\mathit{\text{effect}}}+{\mathit{\text{SS}}}_{\mathit{\text{error}}}}$. Partial Eta squared measure can be obtained by selecting 'estimates of effect size' on the option window during performing two-way ANOVA by selecting successive procedures of Analysis - General Linear Model - Univariate in IBM SPSS statistical package version 23.0 (IBM Corp., Armonk, NY, USA). As appeared in Table 3, the estimates of ${\mathit{\eta }}_{\mathit{p}}^{\mathrm{2}}$ values for MATERIAL, LIGHT, and the interaction term were calculated as 0.469, 0.015, and 0.410, respectively. Partial Eta squared can be converted into Cohen's f for a specific term using the formula, $\mathit{f}=\sqrt{{\mathit{\eta }}_{\mathit{p}}^{\mathrm{2}}/\left(\mathrm{1}-{\mathit{\eta }}_{\mathit{p}}^{\mathrm{2}}\right)}$

The Omega squared measure was suggested to correct the biasedness of Eta squared measure. The Eta squared was slightly biased because the calculation procedure was made purely based on statistics from the sample without any adjustment considering population measure. Omega-squared is calculated as ${\mathit{\omega }}^{\mathrm{2}}=\frac{{\mathit{\text{SS}}}_{\mathit{\text{effect}}}-{\mathit{\text{df}}}_{\mathit{\text{effect}}}\mathit{*}{\mathit{\text{MS}}}_{\mathit{\text{error}}}}{{\mathit{\text{SS}}}_{\mathit{\text{total}}}-{\mathit{\text{MS}}}_{\mathit{\text{error}}}}$, where SS_total and MS_residual represent total sum of squares and mean square of error, respectively, and df_effect is degrees of freedom of the effect. An Omega square has slightly lower value and generally is considered more accurate compared to an Eta squared (Table 4). Omega squared can be approximately converted into Cohen's f using the formula, $\mathit{f}\approx \sqrt{{\mathit{\omega }}^{\mathrm{2}}/\left(\mathrm{1}-{\mathit{\omega }}^{\mathrm{2}}\right)}$.

Cohen (1988) suggested interpretation of effect sizes expressed as η² or ω²: small effect, η² or ω² = 0.01; medium effect, η² or ω² = 0.06; large effect, η² or ω² = 0.14 for the behavioral science.1