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Unfairness in access to higher education: a 11 year comparison of grade inflation by private and public secondary schools in Portugal

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Abstract

Fairness in access to HE is unarguably a subject of paramount importance. Wherever a student’s secondary school scores are relevant for access to HE, grade inflation practices may jeopardize fair access. Pressures for high grading are common in the context of educational consumerism and competition between schools and students. However, they are not equally distributed across different types of schools, given that they have distinct relationships with the State and the market, and work with distinct populations. Specifically, the schools that are more subject to market pressures (namely private schools) are, in principle at least, the ones with more incentives to inflate their students’ grades. This paper presents an empirical study based on a large, 11 years database on scores in upper secondary education in Portugal, probing for systematic differences in grade inflation practices by four types of schools: public schools, government-dependent private schools, independent (fee-paying) private schools, and specially funded public schools in disadvantaged areas (TEIP schools). More than 3 million valid cases were analysed. Our results clearly show that independent private schools inflate their students’ scores when compared to the other types of schools. They also show that this discrepancy is higher where scores matter most in competition for HE access. This means that—usually wealthier—students from private independent schools benefit from an unfair advantage in the competition for the scarce places available in public higher education. We conclude discussing possible solutions to deal with such an important issue.

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Notes

  1. At this point, an important methodological issue needs to be addressed. It refers to the use of significance tests with population data. We acknowledge that there is a long-standing, unsettled debate about this (see, for example, Blalock 1972; Cowger 1984; Rubin 1985). While it is beyond the scope of this paper to discuss this matter in detail, the debate can be summarised as revolving around two competing perspectives. At its core, one argues that significance tests are inferential procedures used to rule out sampling error. When population data is available there is no sampling error. Therefore, significance tests are pointless and meaningless in these situations (Cowger 1984). The other view concurs that such tests are devoid of meaning when the goal is simply to describe variations between subpopulations; however they are deemed necessary when one seeks to produce causal, theoretical inferences (Blalock 1972; Rubin 1985). In this debate, we tend to agree with the first perspective. Also, we are well aware that significance testing as usually applied and interpreted, even in studies dealing only with samples, is subject to contention (e.g., McCloskey and Zilliak 1996; Zilliak and McCloskey 2004). In fact, Null Hypothesis Significance Testing, when interpreted correctly, tell us the probability of observing sample statistics when that sample comes from a population where the null hypothesis is true, i.e., from a population in which there are no differences or associations between groups or variables (Cohen 1992; Thompson 2006). Therefore, some authors argue that it is just not useful to test for the null hypothesis when you already know if the null is true and, more importantly, when you know the extent of the differences between groups or associations between variables. We agree with this perspective. However, we acknowledge that the matter is unresolved. Therefore, we also provide here significance tests' results.

  2. Although the usefulness of doing statistical significance testing on population data is controversial, as discussed in the previous footnote, we have conducted Analysis of Variance (ANOVAs) for each one of the 20 classes of scores in national exams (the rows in Tables 3, 4 or the x axis in the graphs). Regarding the data presented in Table 3 (and Fig. 1), the differences between the three types of schools yielded statistical significance in all classes, with the first class (0–0.99) reaching a p value under 0.05 and all the others classes a p value under 0.001. Post hoc tests (Tukey HSD) revealed differences between independent private schools and government-dependent private schools in the first class, and between all 3 types of schools in all other classes, with the exception of the 7th, 8th, and 9th classes, where the difference between public and dependent-private schools did not reach statistical significance. As observed in Fig. 1 (or Table 3), that is the point where the two lines intercept and cross each other. Regarding the data presented in Table 4 (and Fig. 2), the differences between the four types of schools yielded statistical significance in all classes but the first (0–0.99), with a p value under 0,01. Post hoc tests (Tukey HSD) revealed differences between independent private schools and the rest throughout the score range. Also, from the class 10–10.99 onwards, post hoc tests revealed differences between government dependent private and public schools. Finally, post hoc tests show that TEIP schools do have a more irregular pattern.

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Authors and Affiliations

  1. CIIE, Centro de Investigação e Intervenção Educativas, Faculdade de Psicologia e de Ciências da Educação, Universidade do Porto, Dr. Manuel Pereira da Silva, 4200-392, Porto, Portugal

    Gil Nata, Maria João Pereira & Tiago Neves

  2. Universidade Portucalense, Rua Doutor António Bernardino de Almeida 541, 4200-072, Porto, Portugal

    Gil Nata

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  1. Gil Nata
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Correspondence to Gil Nata.

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Nata, G., Pereira, M.J. & Neves, T. Unfairness in access to higher education: a 11 year comparison of grade inflation by private and public secondary schools in Portugal. High Educ 68, 851–874 (2014). https://doi.org/10.1007/s10734-014-9748-7

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