Wilcoxon-signed rank vs Kruskal-Wallis?

I am rather stressed out about my methodology. For my PhD I ran a year long literacy intervention (small scale longitudinal study) that was supposed to test the rate of change in individual students' literacy skills over the year. I chose an individual rate of change per student because I cannot compare students with different socio-economic backgrounds and from different schools etc.

My sample was roughly 90 (3 classess = small-scale). The students had to write multiple writing drafts throughout the year-long intervention so that I could track their changing literacy scores. I numbered the literacy assignments from 1 to 10 and then at the end when I did the analysis I looked at the literacy scores pre-intervention and post-intervention (assignments 1 and 10) for an overview of whether students developed better literacy skills. But the strength of my study (or so I thought, and my supervisors) was in the process/based analysis. I compared the literacy scores for assignment 10 & 9; 9 & 8; 8 & 7 etc to get a detailed analysis of what was happening during the intervention. I wanted to know if there was a better literacy score between assignment 10 & 9; a better score between 9 & 8 etc to see if progress was being made, each one building on the one before. I wanted more than just a pre and post score because often things happen during the year that impact on teaching and learning and we wanted to see if they impacted on development, where in the year to get an idea of what aspect of the syllabus was not working. So I used a Wilcoxon signed rank test and now that I am trying to publish I am being told it was the wrong test to use. Out of 4 reviewers, 3 are happy with the Wilcoxon but one said it should have been Kruskal-Wallis. I am being told that because I looked at pairwise comparisons over a year, and not just a once off pre and post, I have inflated the error? I am confused and would love some help if possible.

In my opinion, the Wilcoxon signed rank tests are fine. It's not the only way you could analyse this data, but it's ok. You need to present nine test p-values in your manuscript, one for each increment (2 vs 1, 3 vs 2, ..., 10 vs 9.).

Your data is not in any sense a one-way anova so the Kruskal-Wallis test is inappropriate. A Kruska-Wallis test would assume that all observations are independent, whereas repeat observations on the same student are related.

The Wilcoxon signed rank test correctly accounts for the fact that observations are paired by student by making a pairwise comparisons.