The Effect of Risk Factors and Student Service Interventions on College Retention

Jeff E. Hoyt Michelle Lundell

As colleges throughout the nation face increasing pressure from federal and state

governments and accrediting agencies to demonstrate effective outcomes, student success and

retention issues continue to be top priorities for college administrators. Unfortunately,

administrators often don’t have access to controlled empirical research highlighting multiple-

variable effects to prioritize funding for programs at their unique institution (Levin and Levin,

1993). Indeed, every college must have a systematic approach to assessing their own retention

programs (Wang and Grimes, 2001; Woodard, Mallory, and De Luca, 2001).

This study evaluates the impact of several student service interventions on student

retention at a large public university, utilizing Astin’s (1991) Inputs-Environment-Output (I-E-

O) model. The current study provides an example of a means of evaluating several services

simultaneously controlling for multiple treatment effects and student inputs. Factors assessed in

the study are theoretically derived from the Bean and Metzner (1985) conceptual model.

The current study attempts to answer three major research questions: (1) What are the

risk factors that affect retention rates on campus? (2) Which student service interventions have a

positive impact on increasing student retention? (3) Are relationships consistent across student

subpopulations? Before presenting the methods and results of this research, a literature review

of findings from prior studies is presented to provide the background for the current study.

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Literature Review

The literature provides support for the efficacy of many student service interventions.

One area of continued interest is the effectiveness of freshmen seminars. Freshmen seminars are

often identified with extended orientation courses designed to teach academic skills in addition

to helping new students adjust to college life. Longitudinal studies have shown increased

retention rates using this model (Boudreau and Kromrey, 1994; Stark, Harth, and Sirianni, 2001;

Williford, Chapman, and Kahrig, 2001; Yockey and George, 1998). Whether seeking to retain

African American students or targeting low-income students, from a multifaceted approach to

retention using freshman seminars, student mentors, and connection activities, research has

shown affirmative results (Fidler and Godwin, 1994; Kluepfel, 1994; Glass and Garrett, 1995;

Singleton, Garvey, and Phillips, 1998; Koutsoubakis, 1999; Colton, Connor, Shultz, and Easter,

1999).

Financial aid and scholarships have long been identified as important tools in retention

efforts. Both the size of a scholarship award as well as available financial aid have exhibited

positive relationships with retention rates (OIR, 1999; Schuh, 1999). Although Braunstein,

McGrath, and Pescatrice (2000) did not find significance for this relationship, the authors

provide an excellent review of several studies, with the majority supporting the benefits of

financial aid.

Along with freshmen seminars and financial aid, studies have also linked personal mental

health counseling with increased retention figures in post-secondary institutions (Wilson, Mason,

and Ewing, 1997; Turner and Berry, 2000). Students prefer one-on-one counseling services to

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assist them in personal issues that may be causing them to be unable to focus on their course

work (Gallagher, Golin, and Kelleher, 1992).

Another area extensively researched is the effect of residential living programs on

retaining students. Institutions that have residential housing on campus have reported positive

findings from students participating in residential learning communities and their ability to

connect to the institution (Wisely and Jorgensen, 2000). Research studies report that students

participating in specialized residential housing programs feel a sense of community and have

increased opportunities for social integration and continued persistence (Berger, 1997). Studies

that have not mirrored increased retention rates have reported that residential programs designed

to connect students to campus through learning communities did show significant improvement

in increasing faculty-student interaction or academic performance, thus enhancing persistence

indirectly (Kanoy and Bruhn, 1996; Pike, Schroeder, and Berry, 1997).

Older adults have been shown to have lower retention rates and different needs than the

traditional 18 to 24 year old student population (Ashar and Skenes, 1993; Naretto, 1995). These

students often commute to campus and lack integration into campus life. Campuses can still

provide evening hours, childcare, and other intervention programs for these students (Jacoby,

1992). Woman’s Resource Centers or Turning Point programs are other services that may

disproportionately assist non-traditional students.

A critical component in any successful program seeking to improve student success and

retention is an effective partnership between the academic and student affairs’ areas. Early

identification is key to effective intervention programs. High-risk students often fail to seek out

available services until it is too late (Himelstein, 1992). Students can be surveyed during

orientation to determine services needed, with follow-up by student affairs departments

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(Santovec, 2003). Other examples of early intervention strategies involve use of the Noel Levitz

College Student Inventory (Wang and Grimes, 2000), Environmental Deprivation Scale

(Witherspoon, Long, and Chubick, 1999), and Life Skills Development Inventory (Picklesimer

and Miller, 1998). Faculty also can notify administrators of students experiencing academic

difficulty in sufficient time to refer students to tutoring or other academic interventions before

grades become final (Newton, 1990; Simmons, 1994).

Students, who enter college requiring developmental courses or with low GPAs from

their high school experiences, show lower retention levels and graduation rates (Adelman, 1999;

Murtaugh, Burns, and Schuster, 1999; OIR, 1999). Early and intrusive intervention programs

that structure the student’s experience can successfully retain students who otherwise would drop

out during their first year of their college because of poor academic performance (Weissman,

Bulakowski, and Jumisko (1997).

Methods

Data for the study were obtained from multiple sources: the student information system

(SIS); the New Student Survey (NSS) completed by students at orientation, the institution’s Non-

Returning Student Survey (NRSS), and the American College Testing Student Profile survey

(ACT Profile); along with data files from the National Student Loan Clearinghouse (NSLC),

Board of Regents (BOR), and a nearby private university to track student transfer. The Office of

Institutional Research (OIR) sent out information on the fall 2000 first-time freshmen cohort to

several campus departments to track services received during the first term.

Both full-time and part-time degree-seeking first-time freshmen were retained in the

cohort because about half of the students on campus are part-time, and an understanding of their

retention is also important to the college. Students still attending high school, enrolling in the

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armed forces or foreign aid service, serving a religious mission, concurrently attending another

nearby private university, and completely withdrawing without receiving any grades at the

institution were excluded from the study.

This research utilized binary logistic regression in SPSS, which “is well suited for the

study of categorical outcome variables such as staying in or dropping out of college… [and] has

been shown to produce fairly accurate results” (Peng et al., 2002, p. 260, 262). Additional

statistics were calculated separately, following guidelines provided by Menard (2002), Peng et

al. (2002), and Jaccard (2001).

Theory and sequential modeling determined which predicator variables were entered into

the model. The definition and operationalization of these variables are outlined in the appendix.

No transformation of any variables was necessary.

This analysis involved use of several statistics to evaluate the results of the regression

analyses. The Hosmer and Lemenshow test supported the null hypothesis that all the regressions

fit the data well. Differences in the model Chi-square values (Gm), were used to evaluate the

value-added of including additional variables to alternative models. McFadden’s index (R2 L )

measured the “proportional reduction in the absolute value of the log likelihood” for the models

(Menard, 2002, p. 24). Predictive efficiency was assessed using Lambda-p (λp), Tau-p (τp ), the

binomial statistic d, and the percentage of cases correctly classified (overall model O, enrolled

students E, not enrolled D). The Wald statistic tested the null hypothesis that β = 0 for each of

the coefficients. The cutoff was set differently depending on the outcomes of interest: (1) .70

when comparing enrolled students versus dropouts, (2) .80 for stop-outs versus enrolled students,

and (3) .60 for transferouts versus enrolled students.

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Statistical procedures also involved testing to ensure that the assumptions of logistic

regression were met by evaluating minimum observation to predictor ratios, nonlinearity,

collinearity, normality of residuals, and influential cases (Menard, 2002). For all regressions,

sample sizes were more than adequate, exceeding the ten observations per parameter

recommended by statisticians (Long, 1997).

Findings

The organization of the study results are as follows: First, descriptive statistics on

student inputs and interventions are discussed, followed by presentation of the overall logistic

regressions. Results on student subpopulations are then described concluding with a discussion

of the implications for practice.

Student Inputs

The overall one-year retention rate for new freshmen is 50%, with another 22%

transferring out to other institutions, 20% being dropouts, 7% stop-outs, and 1% earning a degree

or certificate (Table 1). Descriptive statistics show that part-time and older students, single

parents, and students with children have substantially higher attrition rates. International students

also have high dropout rates; however, the OIR does not have access to adequate data that

identifies whether these students attend other institutions outside the United States. As expected,

student attrition increases as first term GPA decreases. Other risk factors associated with lower

retention rates include marital status, lack of state residency, excessive work, and the need for

developmental reading or more than one area of remediation. There are smaller differences in

retention rates by ethnicity, income level, first generation status, and gender.

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Table 1. One Year Retention Rates by Inputs (Risk Factors) Fall 2000 Cohort Transfer Degree Stopout Dropout Enrolled

1 Year Students Percent of Cohort

Overall Retention 22% 1% 7% 20% 50% 2,363 100%Part-Time 22% 0% 8% 26% 43% 912 39%Status Full-Time 22% 1% 7% 17% 54% 1,451 61%Male 19% 0% 8% 22% 50% 1,107 47%Gender Female 25% 1% 6% 18% 50% 1,256 53%Single 24% 0% 7% 18% 51% 1,929 82%Marital Status Married 14% 1% 9% 29% 47% 434 18%No 22% 0% 7% 19% 52% 2,235 95%Children Yes 21% 1% 16% 42% 20% 128 5%Age 18-24 24% 1% 7% 18% 51% 2,096 89%Age Group Age >24 11% 0% 7% 40% 42% 267 11%Non-Resident 25% 0% 6% 22% 46% 624 26%Residency Resident 21% 1% 7% 19% 51% 1,739 74%US Citizen 23% 0% 7% 19% 50% 2,245 95%International International 6% 1% 7% 39% 47% 118 5%No 21% 1% 7% 19% 51% 1,771 75%Undecided Yes 24% 1% 6% 23% 46% 592 25%Non-Minority 22% 1% 7% 20% 50% 2,245 95%Ethnicity Minority 24% 0% 9% 19% 48% 118 5%No 22% 0% 7% 18% 52% 1,225 52%First

Generation Yes 22% 1% 8% 22% 48% 1,138 48%No 22% 1% 7% 20% 50% 2,336 99%Single Parent Yes 22% 0% 22% 37% 19% 27 1%Middle-High Income 22% 1% 7% 20% 50% 2,084 88%Income Level Low-Income 23% 0% 9% 20% 48% 279 12%< 31 Hours 23% 1% 7% 20% 50% 2,195 93%Excessive

Work 31 + Hours 16% 0% 13% 27% 44% 168 7%Remedial Reading 17% 0% 7% 30% 46% 213 9%Remedial English 21% 0% 8% 25% 47% 613 26%Remedial Math 21% 0% 8% 22% 49% 1,193 50%One Area 21% 0% 8% 19% 52% 634 27%Two Areas 23% 0% 7% 23% 47% 439 19%Three Areas 16% 0% 7% 32% 45% 169 7%

Remedial Needs

No Remedial 24% 1% 7% 18% 51% 1,121 47%A Range 16% 1% 7% 14% 62% 480 20%B Range 19% 1% 6% 13% 61% 1,051 44%C Range 26% 0% 10% 21% 44% 378 16%

GPA

Below C 34% 0% 6% 43% 17% 454 19%

Interventions

Additional descriptive statistics also support the efficacy of various interventions to

counter the impact of the risk factors described above (Table 2). Living with parents or relatives,

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a job on campus, and receipt of any student financial aid appear related to higher retention rates.

Prior research by the OIR has shown that low-income status is correlated with lower retention

(OIR, 1999); yet, this study shows no major effect. About 73% of low income students received

financial aid when only 32% of middle to high-income students received such aid. Financial aid

awards may be offsetting the risk for the large majority of low-income students.

Table 2. Retention Rates for Interventions Fall 2000 Cohort

Transfer Degree Stopout Dropout Enrolled 1 Year Students Percent of

Cohort No 24% 0% 6% 22% 48% 1,750 74%Live Parent

/Relative Yes 16% 1% 11% 16% 56% 613 26%Grant 22% 0% 8% 17% 52% 477 20%Loan 25% 0% 6% 14% 54% 366 15%Scholarship 19% 2% 6% 14% 59% 360 15%Work Experience 21% 0% 0% 17% 63% 24 1%Any Aid 21% 1% 7% 16% 55% 855 36%No Aid 23% 0% 7% 23% 47% 1,508 64%

Financial Aid

Campus Job 19% 0% 5% 16% 59% 79 3%Yes 20% 0% 8% 22% 50% 616 26%Remedial First

Term No 21% 0% 7% 22% 49% 626 27%Student Success 27% 0% 5% 10% 58% 226 10%TRIO Services 18% 0% 12% 12% 58% 33 1%Clubs 33% 0% 6% 12% 48% 33 1%WCTP Services 7% 0% 20% 7% 67% 15 1%Career Center 29% 0% 4% 19% 48% 75 3%Math Lab 24% 0% 4% 17% 54% 46 2%Writing Lab 18% 0% 8% 20% 54% 373 16%

Other Student Services

Any Lab 19% 0% 7% 20% 54% 422 18%No Unmet Needs 23% 1% 7% 10% 59% 467 20%1 Unmet Need 20% 1% 7% 17% 56% 791 33%2 Unmet Needs 24% 0% 7% 22% 48% 599 25%3 Unmet Needs 23% 1% 7% 31% 39% 310 13%4 Unmet Needs 27% 0% 6% 37% 29% 126 5%5 Unmet Needs 22% 0% 12% 36% 31% 59 2%

Unmet Needs

6 Unmet Needs 30% 0% 20% 20% 30% 10 0% 7 Unmet Needs 0% 0% 0% 100% 0% 1 0%

Enrollment in student success classes during one’s first term, along with participation in

TRIO services, and the Women’s Center or Turning Point program (WCTP Services) also result

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in positive retention outcomes. The latter two programs often serve single parents (a group with

a high dropout rate on campus); nevertheless, data for these services show the highest one-year

retention rate for program participants of all groups in the study.

Descriptive statistics for variables measuring attendance at the career center, math lab,

writing center, taking developmental/remedial coursework during the first term, and participation

in student clubs do not demonstrate large effects on retention. An alternative way to view the

latter is that some of these services may address deficits of students participating in the

programs, resulting in no difference in retention rates.

The large majority of first-time freshmen appear to have unmet needs during their first

term of college attendance. As the number of risk areas without intervention increases, the

likelihood of dropping out of college increases. Even though about half of new freshmen

required remedial education, only 26% of the students in the cohort took a remedial course

during their first term, and 18% attended the math lab or writing labs on campus for tutoring and

assistance. In addition, 25% of students earned “C” grades or lower during their first term—a

group disproportionately made up of remedial students, who also should have attended the labs.

Another 25% of new freshmen were undecided about their major, yet roughly 3% took a career

interest inventory during their first term. There are 128 students with children, yet only two

freshmen (2%) used the campus childcare center during the fall term. Nearly half of the new

student body is first generation college students, and about 12% are low income, but only 1% of

first-time freshmen were served by TRIO services. Student success courses are designed to

address a wide range of needs, but only 10% of first-time freshmen enrolled in these courses.

The emphasis on a student’s first term of attendance is purposeful because student

dropout behavior occurs most frequently after the first term, followed by the transition from the

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first to second year of college. In order to be effective, student service interventions must be

“front loaded”--before the student drops out of college.

Overall Logistic Regressions

The ability to predict stopout and transferout behavior using logistic regression was

limited, with an R2 L of .12 and .11 respectively. The Lambda-p value for the stopout equation is

negative, “a proportional increase in error” (Menard, 2002 p. 32). In contrast, the logistic

regression procedure did a much better job in predicting drop-out behavior. Given these results,

student retention is examined below in terms of contrasting students who either drop-out or

remain enrolled at the college (Table 3).

For the logistic regression, student inputs are entered first into one equation to identify

their effects, followed by a second equation adding in the interventions. The institution has no

residential learning programs or on-campus housing. All students participate in orientation,

receive advising and services when placed on probation, which become constants.

Factors in the equation significantly decreasing the likelihood of dropout behavior are

full-time status and state residency. Students with children, older adults, undecided students, and

those with first generation status, working excessive hours, and earning low grades are less likely

to remain enrolled at the institution, compared with students without these characteristics in the

reference categories.

The interaction variables have more dynamic relationships. Gender and excessive work

are not significant in the regression equations without the interaction terms, yet the predictors

become significant with the addition of these terms. The difference in the model Chi-square

values for the equations with and without the interactions (Gm) is 14.054 for the student input

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only models and 14.114 for the combined input-intervention models, both statistically significant

and increasing the explained variance (df = 2, p <.01).

Table 3. Predicting Student Retention—Dropouts Versus Enrolled Students Inputs (Risk Factors) Combined Interventions N = 1,658 B S.E. Exp (B) B S.E. Exp (B) Full-Time .607* .133 1.835 .476* .144 1.610 Married -.172 .187 .842 -.187 .201 .830 Children -1.404* .331 .246 -1.573* .339 .207 Age Group -.821* .207 .440 -.874* .215 .417 Resident .445* .154 1.560 .360** .162 1.433 International -.492 .267 .611 -.493 .280 .611 Undecided -.369** .144 .691 -.569* .200 .566 Ethnicity .354 .310 1.424 .208 .318 1.231 Gender -.639** .262 .528 -.728* .277 .483 First Generation -.390* .133 .677 -.575* .195 .563 Single Parent .183 .766 1.200 -.051 .885 .950 Low Income .123 .212 1.130 -.088 .222 .915 Excessive Work -.537** .248 .584 -.627** .262 .534 Remedial 1 Area .005 .160 1.005 -.286 .246 .751 Remedial 2 Areas -.217 .175 .805 -.630 .353 .533 Remedial 3 Areas -.518** .237 .596 -1.092** .485 .366 B Range GPA -.383** .184 .682 -.394** .186 .674 C Range GPA -1.089* .216 .337 -1.247* .256 .288 Below C GPA -2.701* .212 .067 -2.790* .254 .061 Gender x Work -.716* .245 .489 -.673* .248 .510 Gender x Married -.356** .158 .701 -.436** .175 .646 Live Parent/Relative .184 .167 1.202 Financial Aid .359** .159 1.432 Remedial First Term .350 .273 1.419 Success Class .574** .285 1.775 TRIO Services .386 .671 1.471 Clubs -.139 .664 .871 WCTP Services 1.317 1.164 3.734 Career Center -.230 .405 .794 Campus Job .350 .400 1.419 Lab Attendance .124 .212 1.113 Unmet Needs .186 .151 1.205 Constant 2.603* .362 13.507 2.607* .370 13.564 R2

L, λp, τp .22 .17 .42 .23 .19 .43 O, E, D 76 81 64 77 81 66 **p<.05, *p<.01.

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The interactions are moderator variables that partition the “focal independent variable[s]

into subgroups that establish its domains of maximal effectiveness in regards to the dependent

variable” (Baron and Kenny, 1986, p. 1,173). The effect is “conditioned on the moderator

variables” (Jaccard, 2001, p. 20).

Differences by gender may exist because of various cultural practices, norms, or beliefs.

Married women may post-pone their college plans while supporting a husband through school;

or, they may not feel the need to continue their education relying on the male to be the primary

source of income. The latter is becoming less common in our society as more women enter the

workforce full-time. Married males may continue their education because of an increased sense

of responsibility to provide for the family, at the same time, continuing to work more hours.

A three-way interaction of gender by work by children was not significant, as having

children has a negative effect on retention for both males and females. The birth of a child

creates greater demands on both males and females to work and care for offspring, which

appears to have a negative impact on college retention.

A few interventions are significant predictors of student retention, when controlling for

the inputs or risk factors discussed above. New freshmen enrolled in student success classes and

students receiving financial aid are more likely to be retained at the college compared with

students who do not receive these services.

Measuring the strength or influence of predictors on the dependent variable requires the

use of standardized regression coefficients. Calculation of these coefficients indicate that having

“C” or lower grades is the most influential factor, followed by the variables: gender, children,

age group, remedial three areas, first generation, undecided, full-time, financial aid, student

success, excessive work and residency (in rank order).

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The difference in the model Chi-square values for the equations with and without the

interventions (Gm) is 15.948, not statistically significant (df = 11, p <.01). The addition of

student service interventions slightly increased the fit of the model and the overall percentage of

correctly classified students. The modest R2 L value of .22, overall percentage of cases

classified correctly (77%), Lambda-p (.19), Tau-p (.43 ), and significant binomial d statistics

indicate that the model fit and predictive efficiency could be improved, though the equation does

have predictive value.

Perhaps if the institution involved more students in appropriate student services during

their first term, the explained variance would be greater, and additional services may achieve

significance. In other words, it is believed that student services other than financial aid and

student success courses have a positive effect on retention (particularly when examining

descriptive statistics); however, several variables fail to reach significance in the regression

equation because of the small number of students utilizing the services during their first term.

Analysis of Student Subpopulations

Several regressions were calculated for ten student subpopulations to identify any

significant differences (Table 4). It became apparent, that when statistical significance was

achieved, the direction of the relationships was consistent across groups. Positive effects are

found for full-time status, state residency, receipt of financial aid, and student success courses.

Students who are older, undecided about their major, first generation, remedial, and earning

lower grades are less likely to be retained. Students with children are also less likely to be

retained one year later.

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Table 4. Effect of Factors on Retention Across Subpopulations 1 2 3 4 5 6 7 8 9 10 Full-Time + + + + + Married Children - - - - - - - - Age Group - - - - - - - Resident + + + International Undecided - - - - - Ethnicity Gender - - - - First Generation - - - - - Single Parent Low Income Excessive Work - - - Remedial 1 Area - Remedial 2 Areas - Remedial 3 Areas - - B Range GPA - - C Range GPA - - - - - - - - - - Below C GPA - - - - - - - - - - Gender x Work - - - Gender x Married - - Live Parent /Relative

+

Financial Aid + + Remedial First Term

+

Success Class + + + + TRIO Services Clubs WCTP Services Career Center Campus Job Lab Attendance Unmet Needs + R2

L, .21 .23 .22 .26 .23 .27 .21 .26 .21 .28λp .14 .24 .17 .18 .15 .21 .18 .19 .15 .17τp .46 .40 .40 .44 .42 .41 .40 .46 .39 .44O 83 72 74 78 77 74 74 79 74 78E 91 69 78 83 82 75 77 84 78 82D 51 77 66 65 63 73 67 67 65 68Sample Size 814 844 797 861 1,228 430 797 861 888 770Predictor Ratio 1:25 1:26 1:24 1:27 1:38 1:13 1:24 1:26 1:27 1:24Note: Relationship not displayed unless significant p<.05. 1 Traditional 3 Male 5 Residents 7 First Generation 9 Remedial 2 Non-Traditional 4 Female 6 Non-Residents 8 Second Generation 10 Non-Remedial

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Despite this general consistency in the direction of effect, specific risk factors and

interventions are not always statistically significant for each subpopulation. Various predictors

become constants due to the nature of the subpopulation. Statistical significance may not be

achieved because of differences in student inputs or risk factors inherent in each group, or the

need for various interventions. Other factors to consider include variation in sample sizes

(particularly the smaller sample sizes and lower statistical power), and low participation rates in

specific services, and the negative affect this would have on reaching statistical significance.

Implications for Practice

A major finding in this study is the low participation rates by first-time freshmen in

student service intervention programs. A current policy change is the implementation of a

survey during orientation where students self-identify their need(s) for specific student services.

The students’ self identification of the need for services are sent to the appropriate department,

which in turn proactively contacts, invites, and informs students of the available resources.

Given that the results of the research study show positive effects for the First-Year

Experience Program, the institution should require students who have been identified as at-risk

of dropping out of college to enroll in this program during their first year (such as those requiring

developmental education in more than one area, undecided majors, first generation college

students, low income students, etc.). As the program expands to meet the needs of all high-risk

students, it would be prudent to address the possibility of requiring all first time freshmen to

participate in the First-Year Experience program. This has to be an incremental process due to

the high intensity and involvement of faculty, student services, student life, and student mentors.

Training additional instructors, advisors, and student mentors is a time intensive project and

cannot be done without the commitment of additional resources from college administration.

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The campus should continue to support strong financial aid programs through additional

personnel to handle the increased workload. From academic year 1997 to 2001, the total amount

of Pell grants for students increased from $4.6 million to $13.1 million. Over the last two years,

the office tripled the amount of work-study available to students without increasing the number

of staff. This should be a high priority for additional resource allocation in the future.

The institution could strengthen its financial aid services by offering more scholarships.

Institutional development should partner with financial aid to approach additional donors

regarding scholarship gifts. Even small amounts of scholarship monies that can be used for

books or fees appear to increase student success and retention.

Services for married females and older adults should be expanded to provide personal

support and encouragement for them to continue their studies. Expanding grant programs such

as Women’s Resource Center (Turning Point), Federal TRIO programs, and School to College

University Partnership (SCUP) are ways to assist nontraditional and first generation students

with interventions needed for increased retention. Student mentors are already incorporated in

these programs. Childcare services often have long waiting lists and require additional resources

of personnel as well as space. Student services must be accessible during evenings and

weekends to address non-traditional student needs. Given study results, advisors should also

discourage excessive work hours (particularly for women) and help students obtain financial aid

where appropriate.

With an open admissions policy, the college’s developmental education program and

tutoring services should be maintained and strengthened where possible. The college has set up

a new School of General Academics to give more attention to the needs of undecided and

developmental students. A strong partnership between the School of General Academics and

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Student Services is the key to effective retention programs. Academic advisors must be trained

in retention strategies and campus programs. Advisors are a key source of information for

students.

Prerequisites for entrance to college level courses must be enforced. Currently

prerequisites for English, math, and reading are enforced before entrance to college level English

and math courses however, students can still take a college level course in science, psychology,

or history when they do not have adequate reading skills to comprehend the textbook. Skill

prerequisites should be enforced for all courses, not just English and math.

Developmental courses should be taken the first semester in college. Although this type

of structure was not significant in the models, the need for developmental education is related to

lower first term grade point averages at the college, indirectly impacting retention rates. Many

students on campus delay completion of developmental courses (especially in math) when they

should be taken at the beginning of the student’s academic program.

Even with increased resources, at-risk students often fail to participate in targeted student

service interventions during their first term, which underscores the need for early intervention. If

this connection is not established during the first term, many students, who may otherwise

succeed, will drop out of college.

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Table 5 Part A. Variable Definitions Dependent Variables Transferout 1 = Transferred out to another educational institution, 0 = Other Degree 1 = Earned a degree/certificate within one year, 0 = No degree/certificate Stopout 1 = Records show that the student returned at a later semester, 0 = Other Dropout 1 = Not enrolled one year later, did not transfer, not a stopout, no degree

0 = Other Enrolled 1 Year 1 = Enrolled at the institution in fall 2001, 0 = Not enrolled Student Inputs (Risk Factors) Full-Time 1 = Full-Time, 0 = Part-Time Married 1 = Married, 0 = Single Children 1 = Child(ren), 0 = No child(ren) Age Group 1 = Older than 24 years old, 0 = 24 years old or younger Resident 1 = State Resident, 0 = Non-Resident International 1 = International Student, 0 = US Citizen/Permanent Resident Undecided 1 = Undecided (individualized and no transfer intent), 0 = Major Selected Ethnicity 1 = Black, Hispanic, Asian, Pacific Islander, American Indian 0 = Other Gender 1 = Female, 0 = Male First Generation 1 = Neither parent earned a bachelors, 0 = Parent(s) earned a bachelors Single Parent 1 = Single Parent, 0 = Other Low Income 1 = Family income < $24, 075, 0 = Other Excessive Work 1 = Work 31 or more hours per week, 0 = work less than 31 hours Non-Remedial Reference category—students requiring no remediation Remedial 1 Area 1 = Remedial math, English, or reading, but one area only as determined

by placement testing, 0 = Other Remedial 2 Areas 2 = Two remedial areas, 0 = Other Remedial 3 Areas 3 = Three remedial areas, 0 = Other A Range GPA Reference category—students earning a 3.7 GPA or higher B Range GPA 1 = 2.7 to 3.69 first term grade point average, 0 = Other C Range GPA 1 = 1.7 to 2.69 first term grade point average, 0 = Other Below C GPA 1 = Below 1.7 first term grade point average, 0 = Other Gender x Married 1 = Female Married and Single Male

0 = Female Single and Married Male Gender x Work 1 = Female Excessive Work and Male No Excessive Work

0 = Female No Excessive Work and Male Excessive Work

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Table 5 Part B. Variable Definitions Interventions Live Parent /Relative

1 = Lives with parent or relative, 0 = Other living arrangements

Financial Aid 1 = Offered any type of financial aid, 0 = No aid offered Remedial First Term

1 = Took remedial English, reading, or math during the first term 0 = No remedial courses during the first term

Success Class 1 = Any type of student success class during the first term (Hardiness, Learning to Learn, Career Exploration, Library Research, Student Success, 7 Habits, First Things First) 0 = No student success class during the first term

TRIO Services 1 = Received TRIO, Upward Bound, Talent Search, Educational Opportunity Centers, or Student Support Services during the first term. 0 = None of these services

Clubs 1 = Listed on the club roster submitted during the fall term 0 = Not on the club roster

WCTP Services 1 = Participating in the Women’s Center or Turning Point programs 0 = None of the services

Career Center 1 = Took the career interest inventory or identified as attending the center 0 = No participation identified

Campus Job 1 = Human Resource record of working on campus 0 = No campus job

Lab Attendance 1 = Attended the math lab, writing center, or ATAC lab 0 = None of these services

Unmet Needs Sum of total risk factors where corresponding intervention is lacking (0-9) Risk Factors

(1) Excessive Work (2) Undecided (3) Female (>24 years old or children or married) (4) First Generation (5) Low Income (6) Remedial Reading (7) Remedial English (8) Remedial Math (9) C GPA or Lower

Lack of the Corresponding Intervention Any aid or living with parents/relatives Career Center or interest inventory Women’s Center or Turning Point TRIO Services or Student Success Any aid or living with parents/relatives Remedial reading course Remedial English course Remedial math course Lab Assistance/Tutoring