Measuring Data About Students

¶ … large elementary school regarding the average number of days per week each math teacher spends in a collaborative teaching community. The average ranges from 0 to 7 days per week. You have also obtained the pre-test and post-test scores in math administered in those same classrooms in the beginning and end of the semester, and you have calculated a score that shows learning during the semester by subtracting the pre-test score from the post-test score for each student.

You are interested in examining any potential relationship between the average number of days of teacher collaborative activities and student learning (i.e., post-test minus pre-test).

How can you best handle the data so that you can perform group comparisons to examine the relationship between the two variables? Why is this technique the best for handling the data? The scenario outlined in the case is suitable to attempt to identify the relationship between the average number of days that a math teacher spends in a collaborative teaching community and the effects on student test scores that ensues. This experiment could not be considered strictly experiemental for a couple of different reasons.

One reason is that the groups were not divided into populations that were randomly assigned. Instead, the data was collected in a traditional classroom and there was no random assignment that dictated which group would be the control group and which would be the experimental group. However, the data collected still can be used quantitatively to attempt to identify a potential correlation between the groups that were divided through the traditional classroom assignment methods.

Thus, the study could be considered quasi-experimental at best since the study did not utilize any form of random selection. However, the study utizes a form of manipulation of the variables, despite the fact that these do meet the criteria of experimental conditions, since there is manipulation of the data, the study does qualify for more than the non-experimental design study. Furthermore, the study would be explanatory because the manipulation of the variable would allow more than a simple descripted interpretation of the results. The independent.