THE IMPORTANCE OF EMPLOYMENT STATISTICS FOR STUDENTS APPLYING TO COLLEGE
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Aleong, David Steven
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Abstract
For students applying to college, there has been an increasing interest over the past few decades in the employment opportunities available to them after college, as shown in the Cooperative Institutional Research Program survey (2011). This study utilizes a discrete choice experiment (DCE) to determine the value, as measured by the part-worth utility, prospective students place on hypothetical Higher Education Institution’s (HEI) that offer better employment statistics after graduation, relative to other institutional attributes deemed important to students applying to college. The DCE used the orthogonal main effect plan with six attributes each at three levels. The Firth bias reduction Maximum Likelihood Method was used to estimate the marginal utility of the attributes and interactions of the attributes with the covariates from the conditional logit model which modeled the DCE. The sample consisted of 220 high school juniors and seniors from Texas, Delaware, and Vermont who answered eighteen discrete choice scenarios, making trade-offs in deciding in each which of two hypothetical HEI’s they most preferred (as the three levels of the six attributes that made up each institutional option presented were randomly varied). The aggregated results showed that respondents overwhelmingly preferred institutions that offered, in order from highest utility: the highest average salary levels, the highest 6-year graduation rates, and the highest employment rates. Respondents had no significant preference for the remaining three attributes: tuition rate , distance from home, and U.S. News & World Report ranking. These results show that students at the point of applying to college want evidence to assure them that they will be able, with high probability, to graduate from college and secure a full-time job after they graduate, preferably in jobs that offer the highest average salary.
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Thesis (Ph.D.)--University of Washington, 2019
