DU 2011-2012 First Semester Retention Monitoring Report

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DU 2011-2012 First Semester Retention Monitoring Report

  1. 1. 2012 Dillard Mid Year Retention Monitoring Report Incorporating a Repeat Measurement Approach The predictors of retention are time sensitive; their effects differ by magnitude over the course of first year matriculation. wkirkland Dillard University 3/9/2012
  2. 2. In 2011, the Office of Institutional Research, using the 2010 first-time freshmen cohort,developed for the first time a Dillard specific regression model to identify variables that predictfirst year to second year retention at the institution. The model yielded substantial informationabout factors influencing student retention at Dillard. The original study produced the followingabstract: Declining student retention has been the subject of serious discussion amongdecision-makers at Dillard University during the past two years. The most commonexplanation suggests the cause for the low rate centers around the issue of studentacademic preparation, especially the academic profile of admitted first-time freshmen.This study analyzes the impact of nine independent variables in predicting retention for theentering freshmen cohort group of Fall 2010. Despite expectations that academicpreparation would be a predictor, little evidence is found that standardized test score(ACT) and/or high school grade point average (HSGPA) have a positive influence onretention. The opposite is true for ACT composite score; it is negatively related toretention. HSGPA has no influence. The most potent predictor of retention is the amountof unmet financial aid need. It is also negatively related to retention but in a positive way.As the amount declines retention increases. The second best predictor is academicperformance, or first semester grade point average. Thus, the evidence shows that unmetfinancial needs play an equal or greater role as academic performance in predictingretention. Now, for the first time the model is being applied not only to assess the 2011 cohort butits retention from the first to second semester period. The purpose of this analysis is to providegreater clarity to Dillard policymakers about the relationship between retention predictors andthe first year matriculation cycle. Repeated Measurement Approach What the original study did not address is how long after matriculation starts do theeffects of the predictors begin to influence retention? Since the release of the results of theoriginal study the office has embarked on developing a continuing process to monitor retention atdifferent stages of new freshmen matriculation. Instead of limiting analyses of cohort retention 2
  3. 3. from first year to second year, the office is applying the model from the first semester to secondsemester as well. By implementing a repeated measurement design approach it is hoped that theoffice will be able to tease out the effect of each variable at different points in time and perhapsbetter understand how quickly each one influences retention over the course of the first yearmatriculation cycle. It may be that some variables play a greater role in influencing retention atdifferent points in time. The advantage of knowing this is that it may allow policymakers todevelop more complex strategies that take into account the timing of each variable’s effect as ameans of addressing retention issues. 2011 Cohort Analysis The analysis provides evidence that the predictive power of the variables identified in theoriginal study appear to be time sensitive. While the original analysis found that unmet financialaid need amount (beta weight -.368) and first semester GPA (beta weight .324) contributedequally to predicting first to second year retention, results from the first to second semesteranalysis for the Fall 2011 cohort show a much different pattern. The influence of unmetfinancial aid amount (beta weight -.581) is three times greater than first semester GPA (betaweight .173) in predicting retention during this period. Also, although first semester GPA is stilla predictor, its influence is a little more than half of what it was in predicting first to second yearretention. The influence of third predictor variable, ACT score, is similar in both magnitude anddirection during both periods (beta weight -.131 to -.176). However, it failed to reach statisticalsignificance during the early stage. In essence, the nature of the relationship between the most potent predictors and retentionidentified in the original analysis of the 2010 cohort group are sustained in the 2011 cohortgroup. In addition, the direction of the relationships also remained the same, unmet financial aid 3
  4. 4. amount and ACT score remain negatively related to retention, and first semester GPA ispositively related to retention. Retroactive Analysis of 2010 Cohort The results gleaned from the 2011 cohort analysis triggered an interest in retroactivelyapplying the model to the 2010 cohort group during the first to second semester period. Thefindings support the notion that the influence of the predictor variables vary according to time.Among the 2010 cohort the influence of unmet financial aid need amount (beta weight -.702) isfive times greater than first semester GPA (beta weight .119) during the early period. Yet theycontribute almost equally in predicting retention for the second year. This suggests thatacademic performance is less likely to influence retention during the early period than it will at alater point for this group. The results also buttress the reliability of the model as it predicts that unmet financial aidneed amount is an extremely strong predictor during the early stages of matriculation for bothcohorts (beta weight -.702 and -.581). Based on these findings it is expected that the influence ofunmet need will probably wane for the 2011 cohort in the later part of matriculation while theinfluence of first semester GPA will wax stronger. The analysis reveals one constant - that is -the influence of ACT score appears not to be sensitive to time sequence. In both scenarios, ACTscore is negatively related to retention. In addition its influence seems to remain constant nomatter the period (beta weight -.131, -.150 and -.176). After assessing these results a third stage analysis was performed on the 2010 cohort toassess the effect of each variable during the period between the second semester of the first yearand the beginning of the first semester of the second year. The results show that first semesterGPA is much stronger during this period (beta weight .346) compared to (beta weight .119) 4
  5. 5. during the earlier period. Additionally, the influence of unmet need (beta weight -.168) is fourtimes less (beta weight -.702) during this period than during the earlier period. The influence ofACT score remains consistent no matter the period (beta weight -.176,) year to year, (beta weight-.150) first to second semester, and (beta weight and -.160) second semester to second year. Inaddition, both unmet financial aid need amount and ACT score failed to reach statisticalsignificance during this period. In conclusion, these analyses have explicated different levels of influence at differenttimes for predictor variables in the model. During the first year, various factors effect retentionat different times during the matriculation cycle. That being said, attrition at Dillard is stronglydriven by unmet financial need during the early stage of freshmen matriculation and by academicperformance during the latter stage of the matriculation. This knowledge may aid policymakerin developing intervention strategies that are time sensitive. Once such strategies have beenimplemented the next logical step would be to develop a reliable intervention analysismethodology to monitor the effects of those strategies. 5

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