I have a dataset of 100 students that have undertaken a specialized training program and measure their proficiency (0 to 100). It is measured at a number of points in time in a number of different ways Test (A, B, C, D, E, F). For each student, there is the initial evaluation, 2 months later evaluation and the final evaluation 3 months later. I want to determine if there is significant improvement but am confused on what statistical tests to conduct. I want to compare month 3 to 1 as well as month 1, 2 and 3.
For example. I could use matched pairs and check month 3 vs. month 1 and see if there is a significant difference between the 100 students. I could also calculate month 3 - month 1 and see if that is significantly different than 0? I also have a few categorical factors I would like to see if they impact the proficiency gains.
Currently, the data is one row per student and columns for variables/factors (categories and efficiency test for initial, 2 months later and 3 month and this is repeated for each of the 6 tests (A thru F). Is there an easy way of completing the analysis without running the matched pairs individually? I’ve heard of longitudinal analysis and think that could help in seeing if the performance changes over time from month 1 to 2 to 3.
JMP PRO 18
