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Is there a statistical test (both parametric and non-parametric) to test whether there is a difference between 2 Groups of students (in ALC and Traditional classroom) in their differences of two scores (midterm exam and final exam)?

In other words, each student from a group (ALC or Traditional) have taken Midterm exam and Final exam (get two scores for each). I find the difference in midterm and final exam scores for each student and then find the mean of these differences for each group separately. Then I want to test whether there is a difference in the mean difference between each group of students.

I have ordinal data (ranking from 0 to 100 percent).

I know this is a paired type of test and am thinking of a paired ANOVA. Or could it be Repeated Measures of ANOVA or Friedman test because the subjects (students) are in both midterm and final exams.

Any suggestions?

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If test scores are nearly normal, then consider using the ANOVA model explained in the Wikipedia article linked in @Dave's Comment.

If there is sufficient departure from normality that you want to use nonparametric tests, then you can do it in two steps:

(1) For each class $i=1,2,$ find differences $D_{ij}$ of Final minus Midterm for each student $j.$ Then, for each class, use a Wilcoxon signed rank test to see whether the mean difference $\bar D_i$ is significantly different from $0.$

(2) Use a 2-sample Wilcoxon rank sum test to compare $\bar D_1$ and $\bar D_2.$

Suppose the vector d1 of (Final - Minterm) differences $D_{1j}$ for students $j = 1, 2 \dots, n_1 = 30$ in Class 1. Similarly, suppose d2 has differences for the $n_2 = 40$ students in Class 2.

set.seed(2020)
d1 = round(rbeta(30, 5, 2)*15,3)
summary(d1)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.679   8.508  10.361  10.017  11.490  14.908 
stripchart(d1, pch="|")
  abline(v=median(d1), col="red")

enter image description here

d2 = round(rbeta(40, 7, 2)*15,3)
summary(d2)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  8.577  10.583  11.669  11.716  12.938  14.423 
stripchart(d2, pch="|")
  abline(v=median(d2), col="red")

enter image description here

It seems that students in Class 1 had score improvements with a median of about 10.4 points from midterm to final, and that students in Class 2 the median improvement was slightly greater 11.7 points.

Improvements in both classes were significantly greater than $0.$ Both P-values are near 0, so improvements are highly significant.

wilcox.test(d1)

        Wilcoxon signed rank test

data:  d1
V = 465, p-value = 1.863e-09
alternative hypothesis: true location is not equal to 0

wilcox.test(d2)

        Wilcoxon signed rank test

data:  d2
V = 820, p-value = 1.819e-12
alternative hypothesis: true location is not equal to 0

Now the question is whether amounts of improvement in the two classes were significantly different. Based on an approximate and informal nonparametric test, notches in the boxes of the boxplots typically do not overlap if there is a significant difference in locations. The dotted green line shows that notches may overlap, but only very slightly.

boxplot(d1, d2, notch=T, col="skyblue2")
  abline(h = 11.05, col="green3", lwd=2, lty="dotted")

enter image description here

A printout from R of a formal 2-sample nonparametric Wilcoxon rank sum test follows, showing significance at the 0.3% level. Students in Class 2 seem to have learned a little more between midterm and final than did students in Class 1. Finally, it is worthwhile considering whether difference of 1.3 points in median class improvements is of any practical importance.

wilcox.test(d1,d2)

        Wilcoxon rank sum test

data:  d1 and d2
W = 352, p-value = 0.002913
alternative hypothesis: 
   true location shift is not equal to 0
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If I understand you correctly you have both within-groups (mid-term and final exam) and between-groups (ALC or traditional) data, but you only want to carry out an analysis on the mean differences of the mid-term and final exam between the two different groups.

The non-parametric between-groups test is the Mann-Whitney U test (see here).

An alternative could be to use a mixed-anova if your data meets the assumptions (see them listed here). This would have the added benefit of allowing you to explore interactions. However, you typically need continuous data for this - though have been in stats lectures where it has been suggested that it could be used with ordinal data.

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