orientation, graduation, and anticipatory socialization dissertation defense beckie hermansen utah...

Orientation, Graduation, and Anticipatory SocializationDissertation Defense

Beckie Hermansen Utah State University 12/12/06

Exchange

EXCHANGEOf student’s time, efforts, knowledge

for education offered by the

institution

Student Institution

Explicit Contracts and Implicit Contracts

Little or no guarantee = uncertainty

Uncertainty

Uncertainty

Anticipatory Socialization

Persistence and

Graduation

Postsecondary Socialization

Socialization process marked by high levels

of uncertainty and increased risk of exit from the institution

Registration Graduation

Socialization for students not participation in an orientation

Anticipatory Socialization (Orientation)

Registration Graduation

Socialization process marked by

lower levels of uncertainty and

lowered risk of pre-mature departure

Socialization for students participating in an orientation

Persistence Study Model

CollegePersistence

First Semester G

PA

First Year GPA

Graduation Rates

Departure over time

Transfer Rates

Survival over time

Research Questions1. Participants = Higher 1st semester

and 1st year cumulative GPA

2. Participants = higher graduation and/or certificate completion.

3. Non-Participants = greater withdrawal at the end of the first year than participants

4. Participants = higher transfer to 4 year programs/institutions.

Descriptive Statistics (N = 1143)

• 587 Start Smart; 556 non-Start Smart• 731 (64%) female; 408 (36%) male• Average age = 19• White = 93.7%• Average family contribution = $6,447• Average High school GPA = 3.4• Average ACT score = 20.65• 50.1% declared a major at matriculation• 52.6% received a degree • 34% transferred to a higher educational

institution

RQ1: Participants = Higher 1st semester and 1st

year cumGPA

• Multiple Regression on First Semester Cumulative GPA

• Multiple Regression on First Year Cumulative GPA

• Dependent Variable = GPA (T1 or T2)

• Independent Variables =

~ Age~ Gender~ Ethnicity~ Income Level~ High School GPA~ ACT Score~ Start Smart Participation

These variables were included to account for

the socioeconomic factors known to influence GPA

and college success.

RQ1:

Participants = Higher 1st semester and 1st year cumGPA

• Multiple Regression on First Semester Cumulative GPA

• Coefficient of Determination (r2) = .391 or 39%(F(7,483) = 44.295, p = .000)

• Significant relationships:

• ACT Score (t(490) = 4.581, p = .000)

• Start Smart (t(490) = 4.720, p = .000)

• High school GPA (t(490) = 10.998, p = .000)

This indicated that Start Smart enrollment did have an effect; however, it was less powerful or indirect when combined with high school GPA and ACT score.

Cohort average high school GPA = 3.4 (Coding: 0 is < average; 1 is >= average)

RQ1:


1. CRS01 Dependent Variable: GPA_T1

95% Confidence Interval

CRS01 Mean Std. Error Lower Bound Upper Bound .00 2.523 .037 2.450 2.597 1.00 2.908 .038 2.834 2.982

• Tests of Between-Subjects Effects• Start Smart X High School GPA = F(7,1069) = 3.635, p = .057

2. HSGPA01 Dependent Variable: GPA_T1


HSGPA01 Mean Std. Error Lower Bound Upper Bound .00 2.270 .041 2.190 2.350 1.00 3.161 .034 3.094 3.227

4. CRS01 * HSGPA01 Dependent Variable: GPA_T1


CRS01 HSGPA01 Mean Std. Error Lower Bound Upper Bound .00 2.027 .053 1.924 2.131 .00

1.00 3.019 .053 2.916 3.123 .00 2.513 .062 2.391 2.635 1.00

1.00 3.302 .043 3.219 3.386

Averages:

T1 GPA = 2.84High school GPA =3.4ACT Score = 20.65

RQ1: Participants = Higher 1st semester and 1st

year cumGPA

• Multiple Regression on First Year Cumulative GPA

• Coefficient of Determination (r2) = .337 or 34%(F(7,401) = 29.075, p = .000)

• Significant relationships:

• Start Smart (t(408) = 3.627, p = .000)

• ACT Score (t(408) = 4.009, p = .000)

• High school GPA (t(408) = 10.254, p = .000)

These results were consistent with first semester GPA with high school GPA have the most powerful effect followed by ACT score and Start Smart participation.

Cohort average high school GPA = 3.4 (Coding: 0 is < average; 1 is >= average)

Cohort average ACT score = 20.65 (Coding: 0 is < average; 1 is >= average)

RQ1:


1. CRS01 Dependent Variable: GPA_T2


CRS01 Mean Std. Error Lower Bound Upper Bound .00 2.549 .045 2.460 2.637 1.00 2.833 .042 2.750 2.915

• Tests of Between-Subjects Effects• Start Smart x High School GPA; Start Smart x ACT; Start Smart x ACT x High School GPA = not significant!

3. ACT01 Dependent Variable: GPA_T2


ACT01 Mean Std. Error Lower Bound Upper Bound .00 2.538 .042 2.456 2.620 1.00 2.843 .045 2.754 2.932

2. HSGPA01 Dependent Variable: GPA_T2


HSGPA01 Mean Std. Error Lower Bound Upper Bound .00 2.244 .048 2.149 2.339 1.00 3.137 .038 3.062 3.212

7. CRS01 * HSGPA01 * ACT01 Dependent Variable: GPA_T2


CRS01 HSGPA01 ACT01 Mean Std. Error Lower Bound Upper Bound .00 1.905 .081 1.745 2.064 .00

1.00 2.236 .106 2.028 2.444 .00 2.873 .097 2.683 3.063

.00

1.00

1.00 3.180 .073 3.037 3.323 .00 2.253 .079 2.098 2.409 .00

1.00 2.582 .116 2.354 2.810 .00 3.122 .075 2.975 3.269

1.00

1.00

1.00 3.373 .055 3.265 3.481

Averages:

T2 GPA = 2.89High school GPA =3.4ACT Score = 20.65

RQ2: Participants = Higher graduation rates

• Graduation Rate Comparison

•Dependent Variable = Graduation


~ Start Smart Group~ Non Start Smart Group

RQ2: Participants = Higher graduation rates

• Correlation on Graduation Rate and Group

• Start Smart result was r = .185, α = .01

Descriptive Comparison between START SMART and non START SMART Graduation

AA AS ASB AAS CER No Degree % Graduates

Start Smart 68 240 8 14 1 256 331/587 = 56%

Non-Start Smart 46 149 7 7 2 345 211/556 = 38%

185

84

020406080

100120140160180200

GNST

N-GNST

Start Smart students graduated almost 2 to 1 (1.7) compared to non-Start Smart students at the 4 the semester.

RQ3:

Non-Participants = greater withdrawal at the end of the first year than participants

• Survival Analysis•Dependent Variable = Time and Status -- for this cohort there were 12 time intervals or semesters, excluding summer terms-- status was either censored (no event) or uncensored (terminating event)


~ Age~ Gender~ Ethnicity~ Income Level~ High School GPA~ ACT Score~ Start Smart Participation

Survival-Time Analysis

• Logistic regression does not deal well with sample attrition

• Unique characteristic of “stop-out” from college/university.– Mission, marriage, maternity, money.

• Examine distributions given a time period between two events (matriculation and graduation)

• Life-Tables, Kaplan-Meier, and Cox Regression analysis

RQ3:

RQ3:


• Kaplan – Meier survival probabilities (see handout)

• Mean Life statistic: •Start Smart = 4.4 semesters/ non-Start Smart = 4.2 semesters

• Hazard Probabilities: (see handout)

• Log-Rank Statistic:

• Log-Rank value = .628 (α = .428) . . . not significant.

It is difficult for the log-rank test to find a difference when survival curve lines cross, as was the case in this study. In the absence of a significant log-rank statistic, reliance on graphical representation of survival curves and associated survival probabilities is paramount.

RQ3:


12.0010.008.006.004.002.000.00

TIME

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1.00-censored

.00-censored

1.00

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CRS01

Survival Functions

12.0010.008.006.004.002.000.00

TIME

6

5

4

3

2

1

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1.00-censored

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CRS01

Hazard Function

Predicted Survival and Hazard Functions for the Fall 200 Freshman Cohort

(00 equals non-Start Smart or Orientation participants; 1.00 = Start Smart Orientation students).

RQ3:


• Cox Regression Analysis

• Accounts for the influence of different variables on survival over time

• Unique ability to analyze interactions between variables.

RQ3:


Variables in the Equation

B SE Wald df Sig. Exp(B)

95.0% CI for Exp(B)

Lower Upper

Eth01 .454 .188 5.836 1 .016 1.575 1.090 2.277

Gender01 -.346 .083 17.319 1 .000 .708 .601 .833

TINC01 -.393 .093 17.768 1 .000 .675 .562 .810

MJR -.010 .070 .022 1 .883 .990 .863 1.135

CRS01 .009 .072 .017 1 .897 1.009 .876 1.163

HSGPA -.060 .078 .602 1 .438 .941 .809 1.096

ACT01 .048 .074 .417 1 .518 1.049 .907 1.213

age01 -.013 .077 .028 1 .867 .987 .850 1.147

Cox Regression with Variables

RQ3:


Cox Regression with Interaction Terms

Variables in the Equation

B SE Wald df Sig. Exp(B)

95.0% CI for Exp(B)

Lower Upper

Eth01 .436 .189 5.348 1 .021 1.547 1.069 2.238

Gender01 -.236 .111 4.474 1 .034 .790 .635 .983

TINC01 -.388 .149 6.778 1 .009 .678 .506 .909

MJR -.002 .070 .001 1 .976 .998 .870 1.145

CRS01 .088 .096 .834 1 .361 1.092 .904 1.319

HSGPA -.061 .078 .612 1 .434 .941 .808 1.096

ACT01 .051 .074 .465 1 .495 1.052 .910 1.216

age01 -.006 .077 .007 1 .934 .994 .855 1.155

CRS01*Gender01-.221 .150 2.184 1 .139 .802 .598 1.075

CRS01*TINC01 .001 .190 .000 1 .994 1.001 .690 1.454

RQ3:


• With betas of -.346 for gender and -.393 for income (p = .000), persistence significance was found for female students with lower than average family income contributions.

• No significance was found for high school GPA, gender, or Start Smart participation, even with interaction terms.

• In fact, high school GPA did not have a significant influence on persistence beyond the first year of college.

• This confirms the Kaplan-Meier findings (similar curves).

• Start Smart was not a factor in long-term student persistence: participants and non-participants experienced equal or close to equal termination and persistence rates over time.

RQ4:

Participants = higher transfer to 4 year programs/schools.

• Correlation between participation status and transfer

• Dependent Variable = Transfer


~ Start Smart Group~ Non-Start Smart Group

RQ4:

Participants = higher transfer to 4 year programs/schools.

• Pearson Correlation: r = -.079; α = .05

44

15

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0

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20

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Fall 20

00

Spring 2

001

Fall 20

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Spring 2

002

Fall 20

02

Spring 2

003

Fall 20

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Spring 2

004

Fall 20

04

Spring 2

005

Fall 20

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Spring 2

006

GNST

N-GNST

It seemed that Start Smart students were less likely to transfer than their non-Start

Smart peers.

Non-Start Smart Transfer Rate = 116/556 or 21%

Start Smart Transfer Rate = 96/587 or 16%

Repetitive Findings (N = 4,536)

• RQ1: Start Smart and GPA– Same variation in scores (2001 = 33%, 2002 = 29%, 2003 =

21%)– High school average t-score = 11.42– ACT average t-score = 6.43– Start Smart average t-score = 4.155

• RQ2: Graduation Rates– Same observed pattern– Start Smart average = 263 compared to 164 (1.7 :1)– Combined correlation 2.4% (r2 = .0243) toward Start Smart

and graduation

Repetitive Findings

• RQ3: Survival Analysis– Non-significant log rank values (survival curves are

similar)– 2003 females = higher persistence (more in the study)– 2001 was significant (log rank = 16.007, α = .001). Median

survival for Start Smart = 4 semesters; non-Start Smart = 3 semesters.– Gender was the greatest predictor of persistence (females).

• RQ4: Transfer Rates– Cohorts 2001 and 2002 were mixed– No results for 2003 – Pearson correlation = .054 resulting in a 1% transfer

difference in favor of Start Smart students (r2 = .002916)

ConclusionsRQ1: Start Smart had an indirect but

significant impact on first term and first year cumulative GPA. High school GPA was most significant.

RQ2: Start Smart students experienced higher, timely graduation rates compared to their non-Start Smart peers.

RQ3: No significant relationship existed between Start Smart participation and long

term survival or persistence.

RQ4: Start Smart students did not experience equal transfer rates; non-Start Smart students had greater transfer.

ConclusionsAll of these results, illustrate the complex

dynamics of college student persistence and departure. This study affirmed the importance of high school GPA on early college academics and the fact that attendance in a one credit orientation program positively effected timely graduation. In this sense, the anticipatory socialization expressed in Start Smart did help students negotiate the implicit contract(s) leading to degree completion. In addition, this study displayed the importance of studying the potential effects of retention-related variables on a semester-by-semester basis.

Limitations

• Historical Threat to Validity– Programmatic changes over time– Administrative policy changes over

time– Program delivery changes over time

• Missionary effect– No consideration given to re-entry

or re-enrollment.

Implications• Survival analysis:

– Incorporating time as a dependent variable (whether and when a terminating event occurs)

• Different elements affecting persistence:– Pre-college characteristics– Collegiate characteristics

• Predictive ability:– Logistic regression goes beyond correlation to

prediction– High school GPA x Start Smart = First semester or

first year success

• In-depth assessment of effectiveness– Fiscal support of Start Smart– Comprehensive program assessment for

accreditation– Support to competing enrollments and retention

Recommendations• Survival analysis applied to other

intervention/retention programs– Remedial education programs– Upward Bound-type programs– Early college programs– Sports/Intramurals/Student Leadership

• Interactions between predictors and time:– Look at each predictor over time– Determine transient or permanent effects

• Missionary effect:– Allow for re-entry either with original cohort or existing

cohort– Allow for part-time student analysis/study

• Survival analysis in terms of student decision-making– Variables affecting decisions to withdraw or persist

over time

Future Opportunities

• Application to other programs (athletics, remedial classes (Math or English).

• Study involving part-time students and persistence over time.

• Postsecondary “IEP” or SEP—an advancement of educational anticipatory socialization theory (best practices).

• Start Smart combined with a capstone course for student success (measured college outcomes)

THANK YOU!

orientation, graduation, and anticipatory socialization dissertation defense beckie hermansen utah...

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