In this test we analyze repeated-measures (RM) data. For example, when 10 students are graded by three teachers the grades could look like:
| T1 | T2 | T3 | |
| Jack | 80 | 78 | 75 |
| Jason | 71 | 83 | 84 |
| Joe | 70 | 80 | 79 |
| Jim | 84 | 83 | 80 |
| John | 78 | 86 | 79 |
| Jesse | 76 | 81 | 76 |
| Joel | 82 | 81 | 83 |
| Jonas | 65 | 73 | 83 |
| Joshua | 77 | 73 | 83 |
| Julius | 77 | 74 | 81 |
H0 for this test is that students get the same mean marks from all three teachers.
To run the test execute the command:
StatsANOVA2RMTest/T=1 data0
The results of the test are displayed in the table:
| SS | DF | MS | F | Fc | Conclusion | |
| Total | 1415.37 | 29 | ||||
| Subjects | 355.367 | 9 | ||||
| Within_Subjects | 1060 | 20 | ||||
| Groups | 532.867 | 2 | 49.9 | |||
| Remainder | 527.133 | 18 | 21.1963 | |||
| Test H0 | 2.35418 | 3.55456 | 1 |
Since the F statistic is less than the critical value we can't reject H0. To get the P-value for this test you can execute:
Print 1-StatsFCDF(2.35418,2,18)
The result, 0.123532 is clearly larger than our default significance 0.05.
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