Post on 21-Dec-2015
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:
Participants = Higher 1st semester and 1st year cumGPA
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
95% Confidence Interval
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
95% Confidence Interval
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:
Participants = Higher 1st semester and 1st year cumGPA
1. CRS01 Dependent Variable: GPA_T2
95% Confidence Interval
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
95% Confidence Interval
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
95% Confidence Interval
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
95% Confidence Interval
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
• Independent Variables =
~ 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)
• Independent Variables =
~ 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:
Non-Participants = greater withdrawal at the end of the first year than participants
• 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:
Non-Participants = greater withdrawal at the end of the first year than participants
12.0010.008.006.004.002.000.00
TIME
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Survival Functions
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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:
Non-Participants = greater withdrawal at the end of the first year than participants
• Cox Regression Analysis
• Accounts for the influence of different variables on survival over time
• Unique ability to analyze interactions between variables.
RQ3:
Non-Participants = greater withdrawal at the end of the first year than participants
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:
Non-Participants = greater withdrawal at the end of the first year than participants
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:
Non-Participants = greater withdrawal at the end of the first year than participants
• 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
• Independent Variables =
~ Start Smart Group~ Non-Start Smart Group
RQ4:
Participants = higher transfer to 4 year programs/schools.
• Pearson Correlation: r = -.079; α = .05
44
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Fall 20
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Fall 20
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Fall 20
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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!