DU Report Retention A Dillard Specific Regression Model


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DU Report Retention A Dillard Specific Regression Model

  1. 1. 2011Retention: A Dillard Specific Regression Model wkirkland Dillard University 9/26/2011
  2. 2. Abstract 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 forthe entering 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 theamount of unmet financial aid need. It is also negatively related to retention but in apositive way. As the amount declines retention increases. The second best predictor isacademic performance, or first semester grade point average. Thus, the evidence showsthat unmet financial needs play an equal or greater role as academic performance inpredicting retention. 2
  3. 3. Every fall semester, thousands of recent high school graduates flock to collegecampuses around the country only to fail to re-enter the following year. During thesummer of 2010 senior administrators at Dillard asked various offices to identify areas inwhich they might help improve retention at the university. The Office of InstitutionalResearch responded by proposing to conduct a retention study specific to Dillard. Asdifficult decisions must be made about budget priorities, one latent function of this studyis to provide information to Dillard policymakers about issues driving retention that mayindirectly have budgetary implications. How does attrition affect the institution? Dillardmakes investments in recruiting students, and, when they do not return it loses apercentage of that cost. What factors may be hindering or promoting retention at Dillard? During the past two years, Dillard’s retention rate has declined nearly tenpercentage points. Knowing what influences students to return for their second year ofmatriculation may be beneficial in numerous ways to administrators seeking to improveretention. First, it may help them identify the types of pressures incoming freshmen faceduring their initial foray into college. Second, it may assist administrators in designingfirst year programs specifically tailored to the needs of first-time freshmen at Dillard.Third, it may help administrators develop proactive strategies for reducing attrition,including identifying “at risk” students. And, finally, it may point to strategic areas forefficient and effective deployment of budgetary resources already appropriated to reduceattrition. 3
  4. 4. Descriptive Differences Between Returnees and Non Returnees The focus of this study is the Fall 2010 first-time freshmen cohort group. For thepurpose of this analysis, a returnee (retained student) is defined as an individual whoentered the university as a part of that group and re-enrolled in fall semester 2011. A nonreturnee is someone from that cohort who did not re-enroll. Dillard University, Office ofInstitutional Research tracked the retention of 341 cohort members. Of that group, 226(66%) returned in fall 2011. What are some differences between returnees and non returnees? Table 1 reportsdescriptive differences between the groups based on academic indicators. Returnees tendto have significantly higher first semester grade point averages but nearly identical highschool grade point averages and ACT composite scores. Table 2 reports differences byresidence indicators. There is little differences between the two groups on bothindicators. Similar proportions of each group are in-state and commuters. Table 3compares the two groups by financial aid indicators. Returnees tend to have slightly lessoriginal financial aid need and significantly less unmet financial aid need amount. Basedupon this initial analysis, the large differences in grade point averages and unmetfinancial aid need suggest that these two variables may play a significant role inretention. While the three tables show differences between the two groups, they do notanswer the central question, what are the predictors of retention at Dillard? Table 1. Retention Status of Fall 2010 First-time Freshmen Cohort by Academic Indicators High First School Semester Grade Grade Act Point Point Composite Retention Status Average Average Average 4
  5. 5. Returnee 3.02 2.70 18.60Non Returnee 2.92 2.05 18.60N=341Source: Dillard University, Office of Institutional ResearchTable 2. Retention Status of Fall 2010 First-time Freshmen Cohort by Residence Indicators Percent PercentRetention Status Commuter In-StateReturnee 52% 66%Non Returnee 48% 65%N=341Source: Dillard University, Office of Institutional ResearchTable 3. Retention Status of Fall 2010 First-time Freshmen Cohort by Financial Aid Indicators Average Average Amount of Amount of Original UnmetRetention Status Need NeedReturnee $21,386 $2,527Non Returnee $22,765 $6,355N=341Source: Dillard University, Office of Institutional Research Approach This study approaches retention from a predictive perspective that assumes thereare factors that have varied and independent influences on retention. It also assumes thatthese independent influences exist at the margins. It is not intended to yield a “perfectsolution” to the retention issue, but to provide decision-makers with a framework thatexplains some of the forces contributing to the problem At best this framework may 5
  6. 6. assist decision-makers in developing strategies that attack the problem at the margins as aprelude to getting at the core problem. A previous study of retention at Dillard, funded by Pew, (Fugar1998) focused onthe issue from a comparative framework looking at differences among students inlearning communities versus non-learning communities. That study focused on retentionat Dillard within a programmatic framework, looking at the effect of a particular programwithin a classroom environment. While this study does not cover the full parameters of retention issues, never theless, it incorporates many of the assumptions found in traditional predictive retentionmodels (Porter 1990; McGrath and Braunstien 1997; Deberard, Speilmans and Julka2004). In addition, it incorporates assumptions based on the understanding andexperiences of Dillard personnel. Finally, the study incorporates an approach that viewsDillard in a unique context as a private “historically black” institution serving anunderserved population with financial challenges. In other words, some things related toretention may be different from what is assumed in traditional models. Traditional explanations of student retention have centered on studentachievement and predictors of achievement as relevant variables for study. In keepingwith that approach our model includes high school grade point average (GPA) and ACTcomposite score (Daughtery and Lane 1999). Antidotal accounts suggest the relevance ofthe traditional approach in reporting retention data to the public. An article referring toretention at local institutions in Galesburg Illinois stated, “Retention rates at three localcolleges are linked to admission requirements and average ACT scores, school officialssaid Monday” (Essig 2010 p. 1). 6
  7. 7. Others have approached the problem from a personal perspective- that is- focuson the role personality and personal behavior plays in influencing retention (Lu 1994;Musgrave-Marquart et al. 1997; Jeynes 2002). Time and resources were not sufficient toincorporate this aspect into this study. Such an approach would have required theselection of a sample and the development and distribution of a survey instrument. Neverthe less, the personality approach is widely used In addition to variables used in traditional models, this model tapped theexperience of Dillard staff members. Some members from the first year program, overthe years, have consistently alluded to their feeling that there are differences between in-state and out-of-state students as well as between commuter and residential students.Staff in the Office of Records and Registration suggested that credit hours attempted maybe affecting retention, They noted the high credit hour load taken in the first semester byfirst-time freshmen. Officials here appear to share the same view of officials fromTemple University’s enrollment management office; they indicated that the credit hoursattempted variable was a major player in getting students to re-enroll (Scannnell 2011). A third set of variables were incorporated to account for the unique context inwhich Dillard students matriculate. Predicting academic success for African-Americanshas usually focused on retention within the context of majority institutions (Seidman2007). Traditional models may miss the unique features of understanding retention in ahomogenous predominate African-American setting. Consequently, our model takesthis into account by focusing on the role financial aid may play in retention. Nationalfinancial aid data indicate that 65 percent of all undergraduates receive financial aid and79 percent of full-time/full year students receive aid (National Center for Educational 7
  8. 8. Statistics, 2009). On the other hand, 94 percent of first-time freshmen enrolled at Dillardin 2009-2010 received financial aid (Dillard University, 2011). Therefore three financialaid indicators are included in our model. Retention Model A retention regression model specific to Dillard was developed to predictretention of first-time freshmen. The model includes nine independent variables. Theyare: (1) in state versus out-of-state, (2) first semester grade point average, (3) hours takenfirst semester, (4) on campus versus commuter, (5) high school grade point average (6)ACT composite score, (7) original financial aid need amount, (8) unmet financial aidneed amount, and (9) percent of unmet financial aid need. The independent variables in-state and off-campus are treated as dummy variables. In-state and off-campus studentsare coded 1 and out-of-state and on-campus students are coded 0. The dependentvariable retention is also treated as a dummy variable. Persons who returned were coded1 and non returnees were coded zero, Variables Influencing Retention at Dillard Three variables are found to influence retention at Dillard. They are: firstsemester grade point average, ACT composite score, and amount of unmet financial aidneed. The most potent predictor is the amount of unmet financial aid need (beta weight-.368) followed by grade point average (beta weight .324) and ACT score (beta weight-.176). While grade point average is positively related to retention, unmet need and ACTscore are negatively related to retention. In other words, for every unit increase inretention there is an increase in grade point average. On the other hand, for every unitincrease in retention unmet need decreases, the same can be said about ACT score, 8
  9. 9. although the latter has one-half the predictive value. As ACT scores decline retentionincreases. The unstandardized coefficient identifies the threshold at which unmet need islikely to influence retention. As one moves from the category non returnee to returneethe amount of unmet need declines by $3,257. The remaining six independent variableshave little influence on retention and fail to reach statistical significance. For a detailedtable of the regression results see Appendix A. Our findings corroborate previous research findings yet ours also differs fromthem significantly. That fact is substantiated in other published material: According to University Business Magazine, “the research shows there are anumber of other drivers that influence re-enrollment trends.” It further states, “First andforemost is the level of academic success (e.g., term 1 GPA) followed by variables suchas entry qualifications (GPA in high school, standardized test scores, etc,) residentialversus commuter status; attempted hours; participation in intercollegiate athletics or otherextracurricular activities; gender; and race. Variables such as amount of borrowing,unmet need, and level of grant sometimes emerge as statistically significant variables inpredictive retention models, but their influence on behavior is often minor” (Scannell2011 p.1). Our results show that GPA is a significant predictor. On the other hand, incontrast to other findings, unmet financial need is the best predictor while standardizedtest is a weaker predictor. The results raise the question, why is there a negativerelationship between ACT score and retention? This seems counter intuitive. Evidencepresented earlier in Table 1 showing returnees and non returnees with identical averageACT score may hold a clue. This suggests that high achievers are returning at the samerate as low achievers. Perhaps higher achieving students have high expectations that arenot being met by the institution. No doubt, this issue needs further study. Why is there a weaker than expected relationship between ACT score andretention? The answer may be related to the inherent nature of the relationship between 9
  10. 10. GPA and retention that may reduce the influence of ACT score. ACT score impact onretention is probably indirect as evidenced by its correlation (.352) with first semestergrade point average (see Appendix B). If one considers the intuitive nature betweenACT score and retention versus that between grade point average and retention thesurprise may wane. In fact, retention is a function of grade point average. If one fails toobtain a specific level one is dismissed by the institution. On the other hand, a low ACTscore is likely to affect ones admission to the institution, but will not result in a student’sdismissal after enrolling. Conclusion This report began by asking what factors influence retention at Dillard. Afterdeveloping and analyzing a regression model specific to Dillard, it is clear that the modeldid not identify a “silver bullet” to explain retention. Never the less, it identified unmetfinancial aid need as the most potent predictor in the model. This is contrary to nationaltrends. That in itself probably validates the need to use a Dillard specific predictivemodel when approaching retention. The potential budgetary ramifications exposed by this study are significant. Nonreturnees were awarded more than $1.9 million in aid during their matriculation. Onemay infer that much of the aid followed the students when they left. What if fifty percentof them had returned? As the proportion of first-time freshmen in need of some type offinancial aid at the institution consistently hovers at 90 percent or above, and studentattendance is sensitive to financial aid needs, unmet need will probably continue toinfluence retention in a significant way. 10
  11. 11. What options do students with substantial unmet need have? The most plausibleanswer is probably the need to fill that gap in order to remain in school. Those who areable to do so stand an increased chance of continuing their matriculation. Those who areunable to do so may find it difficult to remain at the institution. Those who stay may fillthe gap by securing employment on a full-time or part-time basis or securing more aid.Consequently, any future efforts aimed at stabilizing or increasing retention may need toincorporate strategies that address this issue. As traditional strategies focusing on academic success appear to be thepredominant approach at Dillard for addressing retention, and the model provides validityfor continuing this approach, perhaps it needs to be broadened to include a co-equalstrategy that focuses on unmet financial aid need as well. The institution has longemployed the tactic of “early warning” based on academic performance as anintervention strategy. Perhaps now is the time to implement a tandem process thatfocuses on both issues. Students with high levels of unmet need may require as much monitoring as thosewith academic issues. This may require decision-makers to re-think current retentionstrategies and include tactics that allow for flexible and expanded class schedules. Rigidschedules may preclude these students from seeking or obtaining employment. A secondtactic may include targeting institutional need based grants to at risk students. Those withsufficient grade point averages but high levels of unmet need may benefit most from suchan effort. Given current budget constraints, the university may have to consider re-directing resources to more effective strategies. 11
  12. 12. The study results also provide the university with the opportunity to be morespecific in exploration of grant opportunities related to retention. Now that it is knownthat certain factors influence retention at Dillard the institution is in a better position toarticulate its retention needs to agencies that fund retention initiatives. At this point a handful of ideas have been promulgated; it is expected thatofficials from various entities across the campus may use the results presented in thisstudy to develop and launch an array of retention initiatives. Those efforts may result inthe development of novel new strategies to address the problem. If and when thosestrategies are implemented they may create the need for a continuous monitoringmechanism to assess and evaluate the effectiveness of those programs. This study represents the first step in spurring attempts to find a solution(s) to therecent decline in retention. Perhaps in the future retention studies on Dillard’s studentpopulation may be expanded to focus on individual personal behavior. 12
  13. 13. Appendix A 13
  14. 14. CoefficientsaModel Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta1(Constant) .877 .260 3.373 .001High School GPA .032 .049 .034 .651 .516Instate -.016 .054 -.016 -.298 .766Residency -.040 .051 -.042 -.776 14
  15. 15. Appendix B Correlations High ACT Original Unmet School Hours Comp GPA Need Pack Need Residency Instate GPAHours Pearson Correlation 1 .339(**) .203(**) -0.065 -.141(**) 0.030 0.055 .177(**) Sig. (2-tailed) 0.000 0.000 0.237 0.010 0.587 0.315 0.001 N 341 341 339 335 335 341 341 341ACT Pearson -.220(**Comp Correlation .339(**) 1 .352(**) -.139(*) 0.006 0.004 .299(**) ) Sig. (2-tailed) 0.000 0.000 0.000 0.011 0.912 0.939 0.000 N 341 341 339 335 335 341 341 341GPA Pearson Correlation .203(**) .352(**) 1 -0.080 -.262(**) 0.047 0.000 .332(**) Sig. (2-tailed) 0.000 0.000 0.147 0.000 0.389 0.995 0.000 N 339 339 339 334 334 339 339 339Original PearsonNeed Correlation -0.065 -.220(**) -0.080 1 .296(**) 0.065 0.083 -0.061 Sig. (2-tailed) 0.237 0.000 0.147 0.000 0.235 0.127 0.268 N 335 335 334 335 335 335 335 335Unmet Pearson -.141(** -.262(**Pack Correlation -.139(*) .296(**) 1 -0.053 -0.055 -0.064 ) )Need Sig. (2-tailed) 0.010 0.011 0.000 0.000 0.338 0.314 0.245 N 335 335 334 335 335 335 335 335Residency Pearson Correlation 0.030 0.006 0.047 0.065 -0.053 1 .486(**) 0.011 Sig. (2-tailed) 0.587 0.912 0.389 0.235 0.338 0.000 0.845 N 341 341 339 335 335 341 341 341Instate Pearson Correlation 0.055 0.004 0.000 0.083 -0.055 .486(**) 1 -0.080 Sig. (2-tailed) 0.315 0.939 0.995 0.127 0.314 0.000 0.143 N 341 341 339 335 335 341 341 341 15
  16. 16. High PearsonSchool Correlation .177(**) .299(**) .332(**) -0.061 -0.064 0.011 -0.080 1GPA Sig. (2-tailed) 0.001 0.000 0.000 0.268 0.245 0.845 0.143 N 341 341 339 335 335 341 341 341**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). ReferencesDaughtery, T.K. & Lane, E.J. (1999). A longitudinal study of academic and socialpredictors of college attrition. Social Behavior and Personality, 27 (4) 355-362.DeBerard, M.S., Spielmans, Glen I., Julka, D.L (2004). Predictors of academicachievement and retention among college freshmen: a longitudinal study. CollegeStudent Journal (March 2004).Dillard University 2011 IPEDS Financial Aid Survey.Essig, C. (2010, October 12). Local colleges’ retention rates way above average.Galesburg.com. Retrieved from http:www.galesburg.com/newsnow/Fugar, C. V. (1998). Student retention, progression and academic performance at DillardUniversity. Unpublished.Jaynes, W.H. (2002). The relationship between the consumption of various drugs byadolescent and their academic achievement. American Journal of Drug and AlcoholAbuse, 28 (1), 15-35. 16
  17. 17. Lu, L. (1994) University transition: major and minor stressors, personality characteristicsand mental health. Psychological Medicine, 24, 81-87.McGrath, M. & Braunstien, A. (1997). The prediction of freshmen attrition, Anexamination of the importance of certain demographic, academic, financial, and socialfactors. College Student Journal, 31 (3), 396-408.Musgrave-Marquart, D., Bromley, S.P., Dalley, M.B. (1997). Personality, academic,attribution, and substance use as predictors of academic achievement in college students.Journal of Social Behavior and Personality, 12 (2), 501-511.National Center for Educational Statistics (2009).Porter, O.F. (1990). Undergraduate completion and persistence at four-year colleges anduniversities: Detailed Findings, Washington, DC: National Institute of IndependentColleges and Universities.Scannell, J. (2011). The role of financial aid and retention. Retrieved fromhttp://www.universitybusiness.com/Seidman, A. (2007) Minority student retention. Amityville N.Y.: Baywood PublishingCo., Inc. 17