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121008 Reddell Ccs Analysis
 

121008 Reddell Ccs Analysis

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analysis of of a new policy for community colleges regarding credit cost rates.

analysis of of a new policy for community colleges regarding credit cost rates.

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    121008 Reddell Ccs Analysis 121008 Reddell Ccs Analysis Presentation Transcript

    • Enrollment trends and analyses related to tuition payment changes Dan Ruddell, Dec 2008 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group) 06/07/09
      • Prior to the Fall quarter of 2005 all students paid the same tuition if their credit load was between 10 and 18 credits.
      • Fall 2005: a new policy required students to pay variable amounts for credits in this range
      • The following analysis identifies if this change had an effect on the number of credits taken across the Community Colleges of Spokane system and at each institution.
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • Outliers were removed from the data set.
        • Several students had credit loads of greater than 25. This number was above two standard deviations from the mean credit load (~15) so they were dropped from the analysis.
      • Students enrolling at less than 10 credits were dropped from the analysis.
      • Comparisons before and after the policy change were done using the entire CCS System and also by each institution.
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • Visual inspection of the data indicate that the distributions are not normal (expected because of 10 credit cut off point) but very similar.
      • Note – the number of students taking credits at this level increased by over 1900 students.
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • An independent samples two tailed t-test was chosen to analyze any existing differences between credit enrollment
        • Independent samples was selected because the groups being compared are not identical.
        • A two tailed test was chosen because there was no hypothesized direction of change.
      • A Levene’s test was run to analyze the variances of each group.
        • It was significant – so the t-test was adjusted to account for this
      • Following this, a Chi-Square analysis was performed between the groups.
        • This was ran to confirm that the differences observed according to the t-test were not due to deviation from a normal distribution
      • Pearson product moment correlations (r) were performed at each comparison to see how closely the data relates.
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • The t-test indicated a significant drop in numbers of credits enrolled.
        • t (45763) = 12.973, p < .000 (adjusted for non-equal variances)
      • However, the means only changed by ~ 1/3 of a credit.
        • Mean credits – pre = 15.08
        • Mean credits – post = 14.72
      • The chi-square test resulted in a significant decrease as well (at the p < .000 level).
      • However the two groups were significantly correlated (r = -.060, p < .000)
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
    • IEL credit distributions SCC credit distributions SFCC credit distributions Pre Post 06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
    • 06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • Levene’s tests for equality of variances were performed on all comparisons
        • SCC and SFCC tests were significant at p < .05
        • T-test adjustments for these two were performed
      • All differences were significant
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group) Test t df P < IEL – pre post 2.903 2236 .005 SCC – pre post 11.108 23357 .000 SFCC – pre post 6.259 20562 .000
      • Because of non normal distributions non-parametric tests were performed (chi square).
      • A test of the correlation between times was also performed.
      • All results are significant
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group) Test Chi-square df p < r p < IEL 62.806 30 .000 -.061 .005 SCC 469.6 34 .000 -.073 .000 SFCC 108.7 16 .000 -.044 .000
      • All differences tested showed a statistically significant decrease in the number of credits enrolled in after the change.
      • However, the differences are always less than 1 credit and given rounding conventions – this would mean no actual change.
        • As a result – the meaningfulness of these results is in question.
      • Correlations for all groups were significant
        • This means that the groups are correlated with each other on a level that is likely not due to chance.
        • This lends support to the notion that the differences are a statistical one and not a real one.
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • These results should be tempered by the fact that other meaningful data was missing.
        • Total revenue – if this was available we could conclude what effects this had on actual revenue
        • External factors – other factors could be resulting in enrollment changes (environmental, personal, etc.)
          • We do not know why enrollment trends may be changing.
          • That said – the enrollments for each institution consistently increased
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)
      • Based purely on the data given, the result is that the policy change has resulted in a significant decrease in the amount of credit being taken at each institution and for all institutions.
      • However, due to the large sample sizes these stats are somewhat biased in favor of finding significant differences when one really isn’t there (Type 1 error).
      • Not enough data is present to conclude if this was an effective policy change.
        • The most important data would be revenue generated by tuition.
      06/07/09 Prepared by Ryan Sain, Ph.D. (Three Worthies Consulting Group)