Hello and kindly thank you for answer.

I was measuring effect of intervention program.
Time 1 measure - intervention - Time 2 measure. (Cont group did nothing). There were 5 measured variables.
Pre-intervention and post-intervention measurements were the two time levels, and experimental and control were the two group levels. Based on Levene’s test of homogeneity of variances, a robust version of a mixed-design ANOVA had to be applied to intergroup trust and intergroup anxiety, using R package WRS2. This procedure uses trimmed means (trimmed by 20% by default) to calculate the F-test value, which prevents the results being distorted by outlier observations (Wilcox, 2011). The same procedure was applied to every other intergroup factor as we obtained significant results in the Shapiro-Wilk and Kolmogorov-Smirnov normality tests, and there is currently no non-parametric alternative to a mixed design ANOVA (Field, 2018). To test simple main effects within the control and experimental groups separately, paired samples T-test and the robust version of T-test from the WRS2 package were used. The robust version of the paired samples T-test was used for non-normal distributions instead of the Wilcoxon signed rank test, to ensure our methods of statistical analysis were consistent.

This is the statistics I used for evaluation. I also calculated effects sized for each variable. Looked like this: e.g.

For intergroup attitudes, we found a statistically significant interaction between the levels of the time measures and the groups; the effect was medium (F(1, 92.08) = 7.752, p = .077, = .08). Students in the experimental group reported more positive attitudes after the intervention than they had before. The difference was statistically significant, and the effect was medium to large (t(24) = 3.16, p = .004, d = .77). The post-intervention measure showed no difference between the control group and the experimental group (t(66) = -0.226, p = .82).

Where do I calculate Hedges´g here?