Abstract
Within the past decade, there has been a growing number of studies examining undermatch—when students apply to or enroll in institutions less selective than their academic qualifications permit. To estimate undermatch, researchers must define institutions’ selectivity levels and determine which students are eligible to gain admission to these selectivity levels. Researchers examining undermatch have used different approaches to defining institutional selectivity and student qualifications. This, in turn, has produced a wide range of undermatch rates, and at times, conflicting or inconclusive findings for underrepresented students. As the body of literature on undermatch expands, the tradeoffs and limitations in estimation approaches must be better understood. Using a nationally representative sample of students (ELS:2002), this study empirically tested these differences in undermatch estimations using two different definitions of institutional selectivity and three distinct approaches of calculating student qualifications on the (1) distribution of students across qualification levels; (2) undermatch rates; and (3) likelihood of undermatch. Findings show that depending on the approach taken, the distribution of student qualifications, undermatch rates, and odds ratios in subsequent analyses can vary greatly. For underrepresented students, the difference in estimation methods can change their representation in various qualification levels, the gaps in undermatch rates, and the significance of results in their likelihood of undermatching. Implications for future undermatch research are discussed.
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Notes
The predicted probability grids are used for illustrative purposes only, as this approach did not rely on GPA/SAT rubrics in the same way that the acceptance rate and enrollment rate approaches did. The grid for predicted probability shows the destination where most students with the given combination of GPA and SAT scores were qualified to have gone using the predicted probability approach, or the mode. There is variation within each cell as to the qualification level to which students were actually assigned.
Because there are very few students in the far reaches of the grids (e.g., with very low grade point averages and very high SAT scores) some smoothing had to occur, where starting from left to right and top to bottom, the highest selectivity superseded subsequent cells underneath it and to the right. This precluded the case of someone with lower GPA and/or SAT scores from being qualified for a more selective institution. In the case of the predicted probability grids, I did not adjust, or smooth, the grid in this manner because its purpose was illustrative and students were not assigned to qualification level based on the grid.
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Acknowledgments
The research reported here is based on data provided by the Educational Longitudinal Survey of 2002 and was supported in part by the Institute of Education Sciences, U.S. Department of Education, through Grant #R305B090015 to the University of Pennsylvania. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education.
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Rodriguez, A. Tradeoffs and Limitations: Understanding the Estimation of College Undermatch. Res High Educ 56, 566–594 (2015). https://doi.org/10.1007/s11162-015-9363-1
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DOI: https://doi.org/10.1007/s11162-015-9363-1