A Survivor's Guide to Survival Analysis. Dr. Beckie Hermansen Presentation given October 2007 (NV) Rocky Mountain Association for Institutional Research (RMAIR). Theoretical Base. EXCHANGE Of student’s time, efforts, knowledge for education offered by the institution. Student. Institution.
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Presentation given October 2007 (NV)
Rocky Mountain Association for Institutional Research (RMAIR)
EXCHANGEOf student’s time, efforts, knowledge for education offered by the institution
Persistence and Graduation
Socialization process marked by lower levels of uncertainty and lowered risk of pre-mature departure
Anticipatory Socialization (Orientation)Postsecondary Socialization
Socialization for students not participation in an orientation
Socialization process marked by high levels of uncertainty and increased risk of exit from the institution
Socialization for students participating in an orientation
Hypothesis: Withdrawal over time for orientation non-participants = withdrawal over time for orientation participants (no difference).
~ Age ~ Gender ~ Ethnicity ~ Income Level ~ High School GPA ~ ACT Score ~ Start Smart Participation
~ Logistic regression does not deal well with sample attrition
~ Unique characteristic of “stop-out” from college/university (Mission, marriage, maternity, money, mobility, mental health, miscellaneous).
~ Examine distributions given a time period between two events (matriculation and graduation)
~ Life-Tables, Kaplan-Meier, and Cox Regression analysis
Censored and Uncensored can be a bit confusing. What helps it to keep in mind the main intent of the study or treatment. You are looking at departure or termination, so those student who experienced that “desired” event are UNCENSORED compared to those student who never experienced the terminal event.
aContinuing are censored students who did not have a terminal event (i.e. transfer or graduation).
bTerminal events are marked by students who were uncensored by transfer or graduation.
cMedian survival time = 4.0 semesters (enrolled); 4.0 semesters (not enrolled)
Log-Rank Statistic: For this cohort the log-rank was .628 (p = .428). This works the same as an F-value and indicates no significantly detectable difference between the Orientation and non-Orientation curves. Note: It is difficult for the log-rank to detect a difference when the survival curves cross.
Cox Regression with Variables
Variables in the Equation
Reference Groups for the Cox-Regression Analysis adhere to those parts of the sample group coded “1”. For example, in this study, significance was found for GENDER01. Since males were coded as “1” then they form the reference group. The β of -.393 indicates a negative impact or an inverse relationship – males were less likely to persist through college compared to females. The same applies to the INCOME (TINC01) category with above average income being coded as 1. Lower income students were more likely to persist.
Cox Regression with Interaction Terms
Variables in the Equation
The ability to mix co-variates is most unique to Cox-Regression analysis. Here the combination of Orientation with Gender as well as Orientation with Income proved to be insignificant. This suggests that the Orientation experience does not have that much effect on persistence over time compared to Gender (alone) and Income (alone).
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.
Start Smart students graduated almost 2 to 1 (1.7) compared to non-Start Smart students at the 4 the semester.
No significant relationship existed between Start Smart participation and long-term survival or persistence.
However, in terms of student success orientation students did graduate at a greater rate than non-orientation students.