Abstract
Low-income and racial minority students access college at lower rates than their more-advantaged peers, caused in part by lesser social capital. Low socio-economic status (SES) students’ networks of rarely provide help navigating the application and enrollment process, preventing even academically-capable students from competing in the near-Darwinian process of college admission because of their low social capital. Research indicates that counselors can mediate SES-based disparities in college readiness but provides little guidance on how counselors should help students. I conduct multi-level logistic regression analyses of nationally representative longitudinal data to investigate (a) which specific advising activities impact college knowledge, eligibility, and enrollment, and (b) how impacts differ for underserved students. I find that the outcomes respond to different treatments. Creating an education plan in 9th grade increases students’ likelihood of reaching college eligibility in math and annual review of plans increases the odds of planed Free Application for Federal Student Aid submission, with larger effects for underserved students. However, most marginal benefits are small and do not persist to become differences in college enrollment.
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Notes
The main determination is based on credits earned in each, which is then supplemented by other variables including the highest math class completed and the current math course 12th-graders said they were taking. Students not enrolled in any math class have an approximately normal distribution with a mean near that of students enrolled in math classes that do not demonstrate college eligibility, so unenrolled students are treated as not eligible.
In FAFSA analyses, I exclude students who said no because they are ineligible or because they can afford college without the FAFSA. Descriptive statistics show that these students’ characteristics align much more with the FAFSA ‘yes’ group. For these students, intent to submit the FAFSA is probably not a valid measure of college knowledge.
In all cases where data were missing from the 2009 wave of data, missing values (for race, gender, parent education, and school-level characteristics) were replaced by values from the 2012 wave. For the few variables still missing data on covariates, I make use of flags for cases with missing values in order to retain those cases in my sample, after testing whether the variables are missing at random (MAR) with respect to treatment variables.
Table A1 (in the appendix) shows the correlation matrix for all covariates. The only covariates that are highly correlated are expected to be so, e.g. a student’s family income and their school’s percent eligible for free lunch.
If interaction terms were used, in order to estimate a coefficient for plan submission on one outcome (even setting aside the presence of multiple levels of treatment and multiple outcomes of interest) for low-income racial minorities, the reader would need to sum the coefficients of Submitted, MinorityXSubmitted, Low-IncomeXSubmitted, and Low-IncomeXMinorityXSubmitted; interaction terms would also preclude separately considering low-income and poverty. Including 3- and 4-way interaction terms for each of three counseling variables would add 21 terms to each regression model, which would be unwieldy for analysis, unwieldy for readers, and unwieldy for printing.
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Fitzpatrick, D. Challenges Mitigating a Darwinian Application of Social Capital: How Specific Advising Activities by High School Counselors Shift Measures of College Readiness But Not College-Going. Res High Educ 61, 652–678 (2020). https://doi.org/10.1007/s11162-019-09575-7
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DOI: https://doi.org/10.1007/s11162-019-09575-7