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The Influence of Financial Aid on the Educational Trajectories of First-Year Students Starting College at a Large Research University

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Abstract

While the literature on postsecondary student success identifies important academic and social factors associated with student outcomes, one question that persists concerns the influence of financial aid. We use the National Student Clearinghouse’s StudentTracker service to develop a more complete model of student success that accommodates opportunities for students to choose to either graduate from the university of first-entry, graduate from a transfer university, or depart from college without a degree. The multinomial regression model reveals differential effects of financial aid. Results suggest that loan aid appears to encourage students to search out alternative institutions or drop out of college entirely, and merit aid appears to increase the likelihood of students persisting and graduating from the university of first-entry.

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Notes

  1. For example, more than 85 % of students enrolled as freshman examined in our study returned for a second year. Of those that chose not to return after their first year, estimates from the NSC suggest that as many as 62 % of these students end up in another post-secondary institution within the next year.

  2. Jones-White et al. (2010) also included a category for reverse transfers; however, due to sample size limitations we were unable to reproduce that category in our study. For the purpose of our study, which focuses on degree completion from a 4-year institution, successful reverse transfer students were categorized as having not obtained a degree.

  3. Due to the reliance on information obtained from a student’s completed FAFSA in our study’s empirical model, it was important for us to consider exactly how to best deal with missing data that was produced when students failed to submit a FAFSA. Out of our initial sample of 5,188 students, 1,021 students (or 19.7 %) appeared to have failed to fill out a FAFSA and as a result have missing values for the unmet need variable. Believing that both listwise deletion and assuming that individuals who failed to submit a FAFSA had $0 in unmet need were both undesirable, our study employed regression-based missing value imputation (using STATAs mi impute command) to estimate values for individuals with no FAFSA.

  4. Administrative records did not contain either ACT or SAT scores for sixty-four (1.23 %) students. Rather than exclude these individuals from out study, we used regression-based missing value imputation to produce score estimates.

  5. The only exception is the effect of composite ACT score which is reported to increase the risk of non-degree attainment relative to completion at the university of first-entry.

  6. Only if none of the variables in the contrast No Degree | Graduate (Transfer) were statistically significant would we believe that the categories completion (transfer) and non-degree attainment could be combined (Long 1997). The Wald test serves as the formal test for combining alternatives and the results support our treatment of different outcomes for transfer success and departure as each of the resulting χ2 values were statistically different from zero.

  7. Percentage change in the odds are calculated by exponentiating the product of the logit coefficient and the specific aid value at each of the five distinct points in the associated distribution. For need aid these values are (in $1,000s): 0.25, 0.787, 2.079, 3.794 and 5.37. For loan aid (in $1,000s): 0.985, 1.294, 2.794, 5.12 and 7.369. For merit aid (in $1,000s): 0.5, 1, 1, 1.5, and 2.5.

  8. The relative risk, or odds-ratio, is calculated by taking the exponential of the beta coefficient, exp(β i ). Demaris (1992) suggests that “Interpreting logistic regression results in terms of odds rather than probabilities confers certain advantages. Most important among these is that exp(β i ) is a single summary statistic for the partial effect of a given predictor on the odds, controlling for other predictors in the model. There is no comparable statistic for the probability. That is, it is not possible to summarize the impact on the conditional probability of a unit increase in a given predictor, net of the others. The reason for this is that the model is nonlinear, and therefore nonadditive, in the probabilities” (48).

  9. The choice of starting values for calculating predicted probabilities is subjective. For the purposes of this analysis, we construct a hypothetical average student by setting each of the independent variables at their mean value. While this does not represent the predicted probability for any individual existing student, it represents the student body in the aggregate.

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Authors and Affiliations

  1. University of Minnesota-Twin Cities, Minneapolis, MN, USA

    Daniel R. Jones-White, Peter M. Radcliffe, Linda M. Lorenz & Krista M. Soria

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  1. Daniel R. Jones-White
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  2. Peter M. Radcliffe
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  3. Linda M. Lorenz
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  4. Krista M. Soria
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Correspondence to Daniel R. Jones-White.

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Jones-White, D.R., Radcliffe, P.M., Lorenz, L.M. et al. Priced Out?. Res High Educ 55, 329–350 (2014). https://doi.org/10.1007/s11162-013-9313-8

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