Using an HLM Growth Curve Analysis to Determine the Impact of the Milwaukee Mathematics Partnership on Increasing Student Achievement in Mathematics
For one cohort of students considered, it was found that 35% of the variability in initial mathematics achievement test scores could be explained by school level differences. This implies that approximately 65% of variability in initial mathematics achievement test scores could be explained by student level differences. Moreover, for the younger cohort of students it was found that the majority of variability (90.2%) found in individual student learning rates could be explained by between school differences. This finding was less pronounced for the older cohort of students as only 58.7% of the variability found in individual learning rates could be explained by school level differences for this cohort.
One of the variables created from the online survey, math focus, was found to be a statistically significant school level predictor of initial mathematics achievement scores for students, as well as student learning rates, for both cohorts of students. This variable assessed how focused a school was on trying to improve student achievement in mathematics. For the younger cohort of students, attending a school with a math focus score that was one point higher would be expected to result in an initial mathematics achievement score that was 15.3 points higher. Moreover, these students would be expected to gain an average of 6.7 scale score points more over time. For the older cohort of students, attending a school with a math focus score that was one point higher would be expected to results in an initial mathematics achievement scores that is 18.1 points higher. Moreover, these students would also be expected to gain an average of 6.7 scale score points more over time. Using math focus as a school level predictor explained approximately 4.3% and 9.5% of the between school variability that existed among individual students in the younger and older cohorts, respectively, in terms of their initial mathematics achievement test scores. Moreover, using math focus as a school level predictor explained approximately 10.1% and 8.3% of the between school variability that existed among individual students in the younger and older cohorts, respectively, in terms of their growth in mathematics achievement test scores. Interestingly a slightly higher percentage of between school variability that existed between individual student learning rates was explained by using math focus as a school level predictor for the older cohort of students. On the other hand, a higher percentage of between school variability that existed among initial mathematics achievement tests scores for students was explained by using math focus as a school level predictor for the younger cohort of students.
