Inequality of opportunity in selection procedures limits diversity in higher education: An intersectional study of Dutch selective higher education programs

category within a variable had <5 or <10 persons, the next smallest category also had to be rounded to the nearest pentad (e.g. <15, <20). Variables. The study includes the following applicant characteristics: sex, year of birth, migration background, country of birth, and degree of urbanity of postal code of address in 2015. It also includes the following applicants’ parental data: Income percentile of parent with highest income; household assets percentile; number of parents who receive social welfare; number of parents who receive a social services income (excl. social welfare); number of parents who are registered healthcare professionals (HP) according to the official Dutch BIG- register; and the number of parents who work in the primary, secondary or vocational educa- tion sector. Details on these variables are described in Table 2. The Studielink dataset also con- tained information on whether an applicant had received an offer of admission, whether this offer was accepted, and whether an applicant had enrolled in the program by October 1st in the year of application. Bias. We followed Sˇimundić’s [54] classifications of bias in research to study potential sources of bias in advance of the analyses. By including all selective programs in HE, and an age cohort of all 16-year-olds on October 1st, 2015 as reference group for the demographics of the young population, we aimed to eliminate potential bias in sampling. Statistical analysis. To determine the representativeness of each cluster’s student popula-

tion compared to the applicant pools, age cohort, and all first-year students in HBO and uni- versity, we used frequency tables to compare the distribution of students on all variables. Not all applicants who received a placement offer became students in that program. There can be several reasons for this. For example, they may have received an offer of admission in two dif- ferent study clusters, and only accepted one. They could also decide to enter a non-selective study, or refrain from entering HE. It is also possible that after a student accepted an offer of placement, they failed their final exams in high school. This excludes them from entering HE. After that, we examined data for evidence of multicollinearity amongst the independent variables using both the Pearson correlation coefficients between variables, and the variance inflation factor (VIF) of each variable. We then performed univariable logistic regression anal- yses to examine which of the independent variables were associated with being offered admis- sion (the dependent variable). The results are shown with odds ratios (ORs) and the corresponding 95% confidence intervals (95% CIs). Statistical level of significance was set at 0.05. Multivariable logistic regression was used to create a model for each cluster in each aca- demic year. This was done to investigate the effect of background variables of applicants on the odds of receiving an offer of placement, while adjusting for the other variables in the model. The results are shown with adjusted ORs and 95% CIs. We did not apply forward or

backward selection. The regression analyses were performed on the applicants who received a ranking number. Next, we used each cluster’s multivariable model to create an intersectional analysis of the admission rates of two groups of applicants within that cluster: 1) applicants who possessed all demographic attributes which were significantly positively associated with the odds of being offered admission in the final model of that cluster (referred to as group ALL); and 2) appli- cants who possessed none of these demographic attributes (group NONE). If a variable had 3 categories (e.g. A, B, C) and only category B was shown to have significantly different odds of admission compared to reference group A, additional regression analyses were performed with reversed reference categories, to investigate if B and C also significantly differed from each other. The double analyses with different reference categories provided the information which of the three categories had significantly higher odds of admission. For group ALL and NONE, we calculated the percentage of applicants which were offered admission. The comparison between these groups (and the ratio of the acceptance rate of PLOS ONE Inequality of opportunity in Dutch selection procedures limits diversity in higher education PLOS ONE | https://doi.org/10.1371/journal.pone.0292805 October 13, 2023 8 / 24