My study is about the total number of times a speaking activity was done over a period of 6 weeks and the improvement of oral fluency scores of each student based on the number of times the activity was done. (So it's about the effect of this activity on the final fluency scores).
In the picture, each row represents one student; the 1st column has the number of times each student did the activity, and the 2nd column has the student's improvement score of oral fluency calculated by the number of words per minute. (The picture has my true data.)
I previously tried to make 3 subgroups of the independent variable: students who did the activity 0 times, or 10-19 times, or 20-29 times and performed a Kruskal Wallis test on the data (because the data did not meet the assumptions for ANOVA). But I got an alpha score of 0.057 which is over 0.05 and means that it is not statistically significant.
So I am now trying to find a new way to organize my data and analyze it in a different (better) way in case it could lead to a statistically significant result.
Any help would be greatly appreciated. This is my first time doing this (statistics altogether) so simple words / explanations would also be very helpful! Thank you in advance.
Maybe you could try to show if there is a significant correlation between the number of time the student did the task and the improvement ? Given your data don't seems to show a normal distribution, I did a Spearman correlation test (Spearman's rank correlation rho; Alternative :two.sided), and it shows a significant strong to moderate correlation (p=0.0132, r=0.5436).
If you use the software R, you can try it by testing this line of data :
m=matrix(c(29,6,27,11.8,22,5.31,22,17.13,21,18.02,19,16.725,16,3.94,14,14.29,13,1.89,12,-1.03,10,4.7,0,1,0,1.83,0,6.72,0,2.57,0,-2.07,0,4.8,0,6,0,-2.61,0,8.2),20,2, byrow=TRUE) cor.test(m[,1], m[,2], method="spearman")
I'm really not an expert in statistics, so I might be totally wrong, but it could be a way to present your datas.
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