1. 2011
Retention: A Dillard Specific
Regression Model
wkirkland
Dillard University
9/26/2011

2. Abstract
Declining student retention has been the subject of serious discussion among
decision-makers at Dillard University during the past two years. The most common
explanation suggests the cause for the low rate centers around the issue of student
academic preparation, especially the academic profile of admitted first-time freshmen.
This study analyzes the impact of nine independent variables in predicting retention for
the entering freshmen cohort group of Fall 2010. Despite expectations that academic
preparation 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 on
retention. The opposite is true for ACT composite score; it is negatively related to
retention. HSGPA has no influence. The most potent predictor of retention is the
amount of 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
academic performance, or first semester grade point average. Thus, the evidence shows
that unmet financial needs play an equal or greater role as academic performance in
predicting retention.
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3. Every fall semester, thousands of recent high school graduates flock to college
campuses around the country only to fail to re-enter the following year. During the
summer of 2010 senior administrators at Dillard asked various offices to identify areas in
which they might help improve retention at the university. The Office of Institutional
Research responded by proposing to conduct a retention study specific to Dillard. As
difficult decisions must be made about budget priorities, one latent function of this study
is to provide information to Dillard policymakers about issues driving retention that may
indirectly have budgetary implications. How does attrition affect the institution? Dillard
makes investments in recruiting students, and, when they do not return it loses a
percentage 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 ten
percentage points. Knowing what influences students to return for their second year of
matriculation may be beneficial in numerous ways to administrators seeking to improve
retention. First, it may help them identify the types of pressures incoming freshmen face
during their initial foray into college. Second, it may assist administrators in designing
first 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 for
efficient and effective deployment of budgetary resources already appropriated to reduce
attrition.
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4. Descriptive Differences Between Returnees and Non Returnees
The focus of this study is the Fall 2010 first-time freshmen cohort group. For the
purpose of this analysis, a returnee (retained student) is defined as an individual who
entered the university as a part of that group and re-enrolled in fall semester 2011. A non
returnee is someone from that cohort who did not re-enroll. Dillard University, Office of
Institutional 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 reports
descriptive differences between the groups based on academic indicators. Returnees tend
to have significantly higher first semester grade point averages but nearly identical high
school grade point averages and ACT composite scores. Table 2 reports differences by
residence indicators. There is little differences between the two groups on both
indicators. Similar proportions of each group are in-state and commuters. Table 3
compares the two groups by financial aid indicators. Returnees tend to have slightly less
original financial aid need and significantly less unmet financial aid need amount. Based
upon this initial analysis, the large differences in grade point averages and unmet
financial aid need suggest that these two variables may play a significant role in
retention. While the three tables show differences between the two groups, they do not
answer 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
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5. Returnee 3.02 2.70 18.60
Non Returnee 2.92 2.05 18.60
N=341
Source: Dillard University, Office of Institutional Research
Table 2. Retention Status of Fall 2010 First-time Freshmen Cohort by Residence Indicators
Percent Percent
Retention Status Commuter In-State
Returnee 52% 66%
Non Returnee 48% 65%
N=341
Source: Dillard University, Office of Institutional Research
Table 3. Retention Status of Fall 2010 First-time Freshmen Cohort by Financial Aid Indicators
Average Average
Amount of Amount of
Original Unmet
Retention Status Need Need
Returnee $21,386 $2,527
Non Returnee $22,765 $6,355
N=341
Source: Dillard University, Office of Institutional Research
Approach
This study approaches retention from a predictive perspective that assumes there
are factors that have varied and independent influences on retention. It also assumes that
these independent influences exist at the margins. It is not intended to yield a “perfect
solution” to the retention issue, but to provide decision-makers with a framework that
explains some of the forces contributing to the problem At best this framework may
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6. assist decision-makers in developing strategies that attack the problem at the margins as a
prelude to getting at the core problem.
A previous study of retention at Dillard, funded by Pew, (Fugar1998) focused on
the issue from a comparative framework looking at differences among students in
learning communities versus non-learning communities. That study focused on retention
at Dillard within a programmatic framework, looking at the effect of a particular program
within a classroom environment.
While this study does not cover the full parameters of retention issues, never the
less, it incorporates many of the assumptions found in traditional predictive retention
models (Porter 1990; McGrath and Braunstien 1997; Deberard, Speilmans and Julka
2004). In addition, it incorporates assumptions based on the understanding and
experiences of Dillard personnel. Finally, the study incorporates an approach that views
Dillard in a unique context as a private “historically black” institution serving an
underserved population with financial challenges. In other words, some things related to
retention may be different from what is assumed in traditional models.
Traditional explanations of student retention have centered on student
achievement and predictors of achievement as relevant variables for study. In keeping
with that approach our model includes high school grade point average (GPA) and ACT
composite score (Daughtery and Lane 1999). Antidotal accounts suggest the relevance of
the traditional approach in reporting retention data to the public. An article referring to
retention at local institutions in Galesburg Illinois stated, “Retention rates at three local
colleges are linked to admission requirements and average ACT scores, school officials
said Monday” (Essig 2010 p. 1).
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7. Others have approached the problem from a personal perspective- that is- focus
on 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 to
incorporate this aspect into this study. Such an approach would have required the
selection of a sample and the development and distribution of a survey instrument. Never
the less, the personality approach is widely used
In addition to variables used in traditional models, this model tapped the
experience of Dillard staff members. Some members from the first year program, over
the 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 may
be affecting retention, They noted the high credit hour load taken in the first semester by
first-time freshmen. Officials here appear to share the same view of officials from
Temple University’s enrollment management office; they indicated that the credit hours
attempted 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 in
which Dillard students matriculate. Predicting academic success for African-Americans
has usually focused on retention within the context of majority institutions (Seidman
2007). Traditional models may miss the unique features of understanding retention in a
homogenous predominate African-American setting. Consequently, our model takes
this into account by focusing on the role financial aid may play in retention. National
financial aid data indicate that 65 percent of all undergraduates receive financial aid and
79 percent of full-time/full year students receive aid (National Center for Educational
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8. Statistics, 2009). On the other hand, 94 percent of first-time freshmen enrolled at Dillard
in 2009-2010 received financial aid (Dillard University, 2011). Therefore three financial
aid indicators are included in our model.
Retention Model
A retention regression model specific to Dillard was developed to predict
retention of first-time freshmen. The model includes nine independent variables. They
are: (1) in state versus out-of-state, (2) first semester grade point average, (3) hours taken
first 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 aid
need 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 students
are coded 1 and out-of-state and on-campus students are coded 0. The dependent
variable retention is also treated as a dummy variable. Persons who returned were coded
1 and non returnees were coded zero,
Variables Influencing Retention at Dillard
Three variables are found to influence retention at Dillard. They are: first
semester grade point average, ACT composite score, and amount of unmet financial aid
need. 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 ACT
score are negatively related to retention. In other words, for every unit increase in
retention there is an increase in grade point average. On the other hand, for every unit
increase in retention unmet need decreases, the same can be said about ACT score,
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9. although the latter has one-half the predictive value. As ACT scores decline retention
increases. The unstandardized coefficient identifies the threshold at which unmet need is
likely to influence retention. As one moves from the category non returnee to returnee
the amount of unmet need declines by $3,257. The remaining six independent variables
have little influence on retention and fail to reach statistical significance. For a detailed
table of the regression results see Appendix A.
Our findings corroborate previous research findings yet ours also differs from
them significantly. That fact is substantiated in other published material:
According to University Business Magazine, “the research shows there are a
number of other drivers that influence re-enrollment trends.” It further states, “First and
foremost is the level of academic success (e.g., term 1 GPA) followed by variables such
as entry qualifications (GPA in high school, standardized test scores, etc,) residential
versus commuter status; attempted hours; participation in intercollegiate athletics or other
extracurricular activities; gender; and race. Variables such as amount of borrowing,
unmet need, and level of grant sometimes emerge as statistically significant variables in
predictive retention models, but their influence on behavior is often minor” (Scannell
2011 p.1).
Our results show that GPA is a significant predictor. On the other hand, in
contrast to other findings, unmet financial need is the best predictor while standardized
test is a weaker predictor. The results raise the question, why is there a negative
relationship between ACT score and retention? This seems counter intuitive. Evidence
presented earlier in Table 1 showing returnees and non returnees with identical average
ACT score may hold a clue. This suggests that high achievers are returning at the same
rate as low achievers. Perhaps higher achieving students have high expectations that are
not being met by the institution. No doubt, this issue needs further study.
Why is there a weaker than expected relationship between ACT score and
retention? The answer may be related to the inherent nature of the relationship between
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10. GPA and retention that may reduce the influence of ACT score. ACT score impact on
retention is probably indirect as evidenced by its correlation (.352) with first semester
grade point average (see Appendix B). If one considers the intuitive nature between
ACT score and retention versus that between grade point average and retention the
surprise may wane. In fact, retention is a function of grade point average. If one fails to
obtain a specific level one is dismissed by the institution. On the other hand, a low ACT
score is likely to affect ones admission to the institution, but will not result in a student’s
dismissal after enrolling.
Conclusion
This report began by asking what factors influence retention at Dillard. After
developing and analyzing a regression model specific to Dillard, it is clear that the model
did not identify a “silver bullet” to explain retention. Never the less, it identified unmet
financial aid need as the most potent predictor in the model. This is contrary to national
trends. That in itself probably validates the need to use a Dillard specific predictive
model when approaching retention.
The potential budgetary ramifications exposed by this study are significant. Non
returnees were awarded more than $1.9 million in aid during their matriculation. One
may infer that much of the aid followed the students when they left. What if fifty percent
of them had returned? As the proportion of first-time freshmen in need of some type of
financial aid at the institution consistently hovers at 90 percent or above, and student
attendance is sensitive to financial aid needs, unmet need will probably continue to
influence retention in a significant way.
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11. What options do students with substantial unmet need have? The most plausible
answer is probably the need to fill that gap in order to remain in school. Those who are
able to do so stand an increased chance of continuing their matriculation. Those who are
unable to do so may find it difficult to remain at the institution. Those who stay may fill
the 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 to
incorporate strategies that address this issue.
As traditional strategies focusing on academic success appear to be the
predominant approach at Dillard for addressing retention, and the model provides validity
for continuing this approach, perhaps it needs to be broadened to include a co-equal
strategy that focuses on unmet financial aid need as well. The institution has long
employed the tactic of “early warning” based on academic performance as an
intervention strategy. Perhaps now is the time to implement a tandem process that
focuses on both issues.
Students with high levels of unmet need may require as much monitoring as those
with academic issues. This may require decision-makers to re-think current retention
strategies and include tactics that allow for flexible and expanded class schedules. Rigid
schedules may preclude these students from seeking or obtaining employment. A second
tactic may include targeting institutional need based grants to at risk students. Those with
sufficient grade point averages but high levels of unmet need may benefit most from such
an effort. Given current budget constraints, the university may have to consider re-
directing resources to more effective strategies.
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12. The study results also provide the university with the opportunity to be more
specific in exploration of grant opportunities related to retention. Now that it is known
that certain factors influence retention at Dillard the institution is in a better position to
articulate its retention needs to agencies that fund retention initiatives.
At this point a handful of ideas have been promulgated; it is expected that
officials from various entities across the campus may use the results presented in this
study to develop and launch an array of retention initiatives. Those efforts may result in
the development of novel new strategies to address the problem. If and when those
strategies are implemented they may create the need for a continuous monitoring
mechanism to assess and evaluate the effectiveness of those programs.
This study represents the first step in spurring attempts to find a solution(s) to the
recent decline in retention. Perhaps in the future retention studies on Dillard’s student
population may be expanded to focus on individual personal behavior.
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13. Appendix A
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14. Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
.877
.260
3.373
.001
High School GPA
.032
.049
.034
.651
.516
Instate
-.016
.054
-.016
-.298
.766
Residency
-.040
.051
-.042
-.776
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15. Appendix B
Correlations
High
ACT Original Unmet School
Hours Comp GPA Need Pack Need Residency Instate GPA
Hours 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 341 341 339 335 335 341 341 341
ACT 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 341 341 339 335 335 341 341 341
GPA 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 339 339 339 334 334 339 339 339
Original Pearson
Need 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 335 335 334 335 335 335 335 335
Unmet 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 335 335 334 335 335 335 335 335
Residency 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 341 341 339 335 335 341 341 341
Instate 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
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16. High Pearson
School Correlation .177(**) .299(**) .332(**) -0.061 -0.064 0.011 -0.080 1
GPA 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).
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