Abstract
Race shapes many aspects of students’ high school experiences that are relevant to the college admissions process. We examine the racially-specific effects of high school course of study on college selectivity. Using NELS 1988–1994, we test how race and track interactively predict the prestige of the first post-secondary institution attended. We find support for a “redemptive equity model” of college prestige for Latinos, who attend more selective colleges than White students, net of background and academic variables. Asian American students also attend more selective institutions than White students. Results for African-American students are more complicated, in that the colleges they attend are not significantly different from those of Whites, on average. When we exclude students who attend historically Black colleges and universities, however, African-American students attend significantly more prestigious universities than Whites, net of other factors. We also find racially-specific effects of high school course of study, with Latinos, Asian Americans, and African-Americans appearing to benefit more from taking more rigorous academic courses than Whites.
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Notes
Hout et al.’s argument is based on the socioeconomic background of the student: they do not consider race or gender.
One issue that Blau’s work does not address is whether high-achieving African-American students buy into the meritocracy myth. Future research should address this point.
The original sample in 1988 included 24,599 students. This sample was freshened in 1990 and 1992. A subsample of 14,915 students was followed into the 1994 wave (National Center for Education Statistics 2002). Of those respondents 5,346 reported attending a 4-year post-secondary institution their first year out of high school. We then matched their reported post-secondary institution to the College Board dataset in order to measure prestige. In this matching process approximately 1,336 cases were lost due to two factors: (1) the school was not listed on one of the two datasets or (2) the school did not report an average ACT or SAT score. This brought our sample to roughly 4,010 cases. Of those cases, 1,350 were missing data from the NELS dataset on one or more of our independent variables.
In constructing our sample several potential biases are introduced. At the school level our sample is biased towards the Northeast region and private schools. On the individual level, our sample is more likely to be on a higher course of study, has higher GPA and SAT scores, and is more likely to participate in extracurricular activities. A racial and economic bias is also introduced where White and higher SES students are over-represented in our sample. Importantly, our dependent variable is not biased: there are no significant differences in the dependent variable between those who do and do not have full data on our independent variables.
Throughout this paper, we use the SAT scores. For those colleges that only reported ACT scores (837 students attended these schools), we converted the ACT scores to SAT scores, using the College Board’s concordance table (Dorans et al. 1997). Although other sources, such as the Integrated Postsecondary Education Data System (IPEDS), include information on the 75th percentile in SAT scores for colleges in more recent years, such data were not available from IPEDS for colleges in the early 1990s.
Some researchers have created measures of course sequences, primarily sequences of math and science courses (e.g., Schneider et al. 1998). Although this method is valuable, we chose not to pursue this path because the sequences created focused only on science and math, while colleges assess other courses taken as well. Lucas (1999) developed another strategy to measure high school course of study. Lucas created course-based indicators of high school students’ records, examining the course titles of the students’ transcripts, and categorizing them into five categories—remedial; business and vocational; lower college; regular college; and elite college. Lucas’s approach does not focus, however, on the credits earned with the various types of courses.
College attendance is prevalent even in these lowest quintiles. Although the students who attend college tend to be of higher socioeconomic status than those students from the lowest quintiles who do not attend college, their socioeconomic status does not surpass the status of students in the most elite track.
If grades from one of these categories were missing, we took the average of the three remaining categories.
The within-school sample size is sufficiently small (44% of our observations come from high schools with fewer than five students) so that hierarchical linear models are not appropriate (Bryk and Raudenbush 1992).
The coefficient for “high course intensity” is statistically significant and positive when student’s SAT scores are not included in the model.
In models run with the Heckman selection adjustment, White middle track is not statistically significant and Latino low track is statistically significant in a positive direction.
There was also one Latino student who reported attending an HBCU. This student was excluded from the analyses reported in Table 5, Models 4–6.
We also caution against interpreting the results of achievement-related factors as being purely the outcome of individual merit. Prior research has thoroughly established the impact of socioeconomic background, race, and gender on various educational outcomes, including grades, such that these outcomes must been seen as jointly determined and influenced by both individual meritocratic and ascriptive characteristics (Oakes 1985; Mickelson 2001).
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Acknowledgements
The authors wish to thank Linda Renzulli for her helpful comments. This research was supported by a grant from the American Educational Research Association which received funds for its “AERA Grants Program” from the National Science Foundation and the National Center for Education Statistics of the Institute of Education Sciences (U.S. Department of Education) under NSF Grant #REC-0634035 and a grant from the Spencer Foundation. Opinions reflect those of the authors and do not necessarily reflect those of the granting agencies. The data presented, the statements made, and the views expressed are solely the responsibility of the authors.
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Appendix 1: Courses Taken by Students in Each of Three Track Levels: NELS 1988–2000
Appendix 1: Courses Taken by Students in Each of Three Track Levels: NELS 1988–2000
Gradation | English | Math | Science | Foreign language | History and social studies | Highest math | Remedial math | Remedial English | APs | Computer science |
|---|---|---|---|---|---|---|---|---|---|---|
High track | 3.5–3.75 | 3.0–3.75 | 2.0–3.0 | 2.0 | 2.0 | >Algebra 2 | No | No | 0–>1.0 | 0–1.0 |
Middle track | 3.0–3.5 | 2.0–3.5 | 1.0–2.5 | 0–2.0 | 1.0–2.0 | Alg 2 and >Alg 2 | No | No | 0–1.0 | 0–1.0 |
Low track | 2.0–3.0 | 1.0–3.0 | 0.5–2.0 | 0–2.0 | 0–2.0 | Alg 2 and <Alg 2 | Some | Some | 0 | 0–1.0 |
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Stearns, E., Potochnick, S., Moller, S. et al. High School Course-Taking and Post-Secondary Institutional Selectivity. Res High Educ 51, 366–395 (2010). https://doi.org/10.1007/s11162-009-9161-8
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DOI: https://doi.org/10.1007/s11162-009-9161-8