Hi there, sorry for the delay in releasing your post - it was caught in the spam filter for some reason. A couple of thoughts:

1) ANOVA (and other regression models) do not assume that the marginal ("overall") distribution of the dependent variable is normal (link). ANOVA does assume that the distribution of the DV is normalwithineach group. That said, testing this with a Shapiro-Wilk test is virtually pointless: If the sample is small, the normality assumption matters, but the Shapiro-Wilk test will have poor power to detect violations of normality; if the sample is large, the Shapiro-Wilk test will have good power, but the normality assumption probably won't matter (due to the central limit theorem).

2) A non-significant Levene's test statistic indicatesa lack of evidence to reject a null hypothesis that the variances are equal. It doesn't necessarily indicate that the variances are homogenous; a non-significant result might just be due to low power.

3) A Kruskal-Wallis test is a non-parametric alternative to ANOVA, but it tests a completely different null hypothesis (that the meanranksare equal across the populations). That might not be what you're interested in testing. If all you're worried about is normality, a simpler alternative would be to use ANOVA, but apply bootstrapping or a permutation test to calculate confidence intervals or p values.

Hope that helps!