Female 133 39.35 39.35 Male 205 60.65 100.00 338 100.00 Table 5. Frequency of Hispanic Hispanic Freq. Percent Cum. Not Hispanic 308 91.12 91.12 Hispanic 30 8.88 100.00 Total 338 100.00 There are several limitations to the data used in this thesis. First, because survey data are being used, all answers are self-reported and may not be accurate due to respondents misremembering or not having full information to answer some survey questions. For example, a respondent may misremember the zip code of their hometown when applying to college, which would affect the value of distattend. Second, because survey respondents were being paid, there was some incentive for a respondent to complete the survey even if they did not fill the eligibility requirements. Also, some respondents could be just clicking randomly to get through the survey as quickly as possible, and not providing thoughtful answers. Much of this problem has been eradicated by including several attention checks in the survey as described in the Methodology section and in Appendix I, but there remains a possibility that some respondents were able to get through the attention checks without providing thoughtful answers to all questions. Additionally, there may be determinants of undermatching that are not included as variables in the regression. Not all determinants of undermatching are known or able to be easily measured, which restricts how undermatching can be modeled. Nonetheless, the
low .074 .069 1.09 0.275 .207 high -.186 .063 0.005 -.056 Table 9. Predicted Probabilities by Distance and Income Category Margin Std. Err. z P>z 95% Conf. Interval Distance 0 miles Low income .742 .05 14.36 0.000 .641 .843 Middle income .675 .04 18.05 0.000 .602 .748 High income .500 .05 9.22 0.000 .393 .605 500 miles Low income .637 .06 10.57 0.000 .519 .755 Middle income .561 .04 13.45 0.000 .480 .643 High income .382 .05 6.99 0.000 .275 .489 1000 miles Low income .521 .08 6.53 0.000 .365 .677 Middle income .443 .06 7.01 0.000 .319 .566 High income .274 .06 4.30 0.000 .150 .399 1500 miles Low income .402 .10 3.98 0.000 .204 .600 Middle income .329 .08 3.91 0.000 .164 .493 High income .184 .07 2.68 0.007 .049 .318 2000 miles Low income .292 .11 2.56 0.010 .069 .516
considered distance “slightly important” to his decision, and he considered attending colleges that were flying distance from home. Respondent D is currently still attending University of Maryland – College Park. As these four respondents illustrate, students with very similar high school achievement can experience vastly different outcomes. They were chosen to represent the key findings of the regression results. The high-income students, respondents A and D, did not undermatch and focused on academics when evaluating colleges. Respondent A traveled far from home for college, and although respondent D ended up going to college close to home, he considered colleges across the country. Respondent B is an example of a low-income student who did not capitalize on his talent. He only considered colleges very close to home, constrained by the desire to remain close to his family. Respondent C, on the other hand, was able to overcome the challenges presented by coming from a low-income background, and attended a prestigious college far from home. The purpose of this section has been to give detailed descriptions of students to personify the statistical results, but it is important to note that the sample contains a variety of students. Additionally, the types of students represented by respondents A, B, C, and D are not equally common in the dataset. Regression results have shown that low-income students are likely to undermatch, so students similar to Respondent B may be more prevalent than students similar to Respondent C.