Nonparametric Survival Analysis of Undergraduate Engineering Student Dropout Young Kyoung Min 1,3, Guili Zhang 1,4, Russell A. Long 2, Timothy J. Anderson 1, Matthew W. Ohland 2 1 University of Florida, 2 Purdue University 3 The Pennsylvania State University, 4 East Carolina University The major questions for this study are  Does the profile of risk of students leaving engineering different among cohorts and groups with different cognitive factors (SAT math and verbal scores) and the non-cognitive individual characteristics (gender and ethnicity)?  When are students most likely to leave the engineering as a major?  Is SAT score a good predictor of the risk of leaving engineering? Research Questions DATA SOURCE METHODOLOGY RESULTS CONCLUSIONS Multiple-Institution Database for Investigating Engineering Longitudinal Development database (MIDFIELD)  Student academic records for nine public universities of USA  Diverse by several characteristics, including institutional size, setting, levels of transfer, and matriculation process  1987 to 2004 cohorts  Study span of 19 years (1987 to 2005)  Limited to engineering freshman and no transfer students  Minority (African American, Hispanics, Native Americans, Alaskan Natives, and Native Pacific Islanders)  Other category includes international students  Population: 100,179  Table 1. The frequency of gender by ethnicity Methodological Definitions  A non-failure: A student who did not leave an engineering major, i.e., a student who either graduated with an engineering degree or is still attending school and had not changed major from engineering to any other non-engineering major.  A failure: A student who left an engineering major, i.e., a student who changed his/her major from engineering to another discipline or left the university.  A major change from one engineering major to another one at the same institution does not constitute failure.  A student who leaves engineering but returns to engineering at the same institution (enrolled or subsequently graduated with an engineering degree) also does not constitute failure. Statistical Methodology  Nonparametric survival analysis  Life-table method for large numbers of observation  A student is regarded as censored if he or she does not leave engineering in each time period.  Tests of homogeneity for survival functions: Log-rank tests (Later survival times) and Wilcoxon tests (early survival time)  Hazard functions indicate the risk of loss of engineering students as a function of semester.  There are no significant differences among cohort subgroups for long survival times, but there are significant differences between cohort subgroups for early survival times, as well as for gender, ethnicity, and SAT math and verbal scores subgroups.  Females show higher risk of leaving engineering in semesters 3 to 5 than males, while the risks are similar during other semesters.  White students tend to leave engineering slightly more than Minority students, which leave engineering more than Asians, which leave engineering more than Other students. The Minority and Other categories show an increase in hazard rate for the 9 th semester and beyond, possibly related to financial or other pressures.  Except for groups with SAT math <550, engineering college students have the highest hazard rate during the third semester, which in part may due to probationary periods offered in earlier periods.  SAT math score better predicts the risk of ‘failure’ than SAT verbal score. That is, the lower a student’s SAT math score the more likely that student is to leave engineering.  Engineering college students with SAT verbal score between 200 and 500 are slightly more likely to survive than the students whose SAT verbal is between 500 and by Ethnicity Survival Functions 1 and Negative log of Survival Probability 2, 3, 4, 5, 6 3. by Gender2. by Cohort Groups 5. by SAT Math Score Groups 6. by SAT Verbal Score Groups 1. First-time-in-Eng-college Students Matriculating Hazard Functions 7, 8, 9, 10, 11, First-time-in-Eng-college Students Matriculating8. by Cohort Groups9. by Gender 10. by Ethnicity11. by SAT Math Score Groups 12. by SAT Verbal Score Groups MIDFIELDEthnicity GenderWhiteAsianMinorityOtherTotal Female14,1441,4784, ,782 Male61,4355,40410,4452,11379,397 Total75,5796,88215,2412,477100,179