542 applicants sat the UKCAT in 2006. This was a heterogeneous group comprising applicants domiciled in the UK, Europe, and overseas, as well as school leavers and applicants who were not school leavers but were either employed or in higher education (“mature applicants”) to both medical and dental schools in the UK. For the purposes of this study, we analysed the results of only UK domiciled applicants who had at least three recent A levels. This would allow meaningful comparison of contemporaneous qualifications and socioeconomic data. We included 9884 applicants (53% of the total population that sat UKCAT) in the study.
Table 1 shows the socioeconomic characteristics of the selected study group (n=9884), including type of school. For comparison, the equivalent data for the non-selected part of the 2006 UKCAT cohort (n=8658) are also shown. As expected, almost all the applicants in the study group were aged 19 or under when they sat the UKCAT test, compared with 61% in the non-study group. Approximately 56% of both groups were female. The study group had more white and Asian applicants and fewer Chinese applicants. Where the data were available, occupation and socioeconomic categories were similar in both groups.
Table 1 Socioeconomic characteristics of study and non-selected groups. Values are numbers (percentages)
The applicants in the study group were distributed almost equally between our two major categories of secondary schooling (independent/grammar and comprehensive/sixth form college). Comparable data are not available for all the non-selected applicants because we did not receive this information for mature or overseas students who had not sat recent UK school examinations.
UKCAT and A level performance in study group
Table 2 summarises the UKCAT scores of the study group. The distributions of UKCAT scores were significantly non-normal (P<0.001 after Kolmogorov-Smirnov test).
Table 2 Performance in UKCAT. Values are medians (interquartile ranges)
The study group by definition had at least three A level passes, but 30% had four or more. Virtually all had at least two passes in sciences (this category included biology, human biology, chemistry, physics, and all variants of mathematics, but excluded subjects such as applied science, accounting, or computing). Most medical and dental schools require entrants to have at least one A grade in science, usually chemistry; some require an A grade in both chemistry and biology. Of the study group, 23% had an A grade in one of these subjects and 50% had A grades in both. Nearly half the group also had A grades in physics, maths, or both, and 37% had an A grade in another subject (excluding general studies).
Table 3 shows the distribution of A level passes into the “bands” described above. Thus, 68% had achieved at least AAB, required by most medical and dental schools, and only 17% achieved less than BBB. This group would include those with, for example, two A grades but no Bs.
Table 3 “Banding” of three highest A level scores. Values are numbers (percentages)
The figure shows the UKCAT scores achieved within each A level band. Most of the distributions were non-normal, so median scores are shown. A consistent drop in performance in the UKCAT scores occurred with each fall in A level band, and this was highly significant in all cases (P<0.001, Kruskal-Wallis test). The only exception to the downward trend was for abstract reasoning, in which the “BBB” group performed better than expected.
Median UKCAT scores by A level band
Web table A shows the correlation matrix between UKCAT and A level total tariff, both for all subjects and for sciences. The four sections of the UKCAT show a modest correlation between themselves; the highest correlation was between verbal and quantitative reasoning (r=0.406) and the lowest between verbal and abstract reasoning (r=0.288). A similar degree of correlation existed between A level tariff score and total UKCAT score (r=0.392), but the sub-sections correlated less well with tariff score, ranging between 0.267 for abstract reasoning and 0.300 for quantitative reasoning. All correlations were highly significant (P<0.001).
Influence of socioeconomic variables on UKCAT and A level scores
Web table B shows the results of the univariate analysis. Various data were missing as a result of unreported ethnicity, parental occupation, and schooling. Also, 21 applicants did not achieve any passes in science A levels, so their tariff scores in this category are zero and they have no average science tariff.
The results from these analyses can be summarised as follows. Male applicants performed better than female applicants in verbal reasoning and quantitative reasoning and overall in the UKCAT test, but they did less well in abstract reasoning. The largest differential was in quantitative reasoning. These differences were all highly statistically significant (P<0.001), although the actual differences in median scores were small. We found no difference between the sexes in decision analysis. Male applicants also performed better in terms of A level total tariff and total science tariff scores, but not in average tariff scores and only slightly (P=0.001) in average science tariff scores. White students performed better than non-white students in all parameters (P<0.001) apart from the total science tariff. The differences were greatest for verbal reasoning, decision analysis, and total UKCAT score. For the 71% of candidates with valid socioeconomic data, those from the top professional/managerial backgrounds performed significantly better in all parameters than did those from all other backgrounds (P<0.001 in all cases). Applicants from independent/grammar schools also performed better than others in all parameters (P<0.001 in all cases).
In view of the lack of information on socioeconomic status for 30% of the study group, we examined the potential limitations that resulted. We created a new variable to denote whether socioeconomic status was known and then did χ2 for this new variable against sex, ethnicity, and schooling, to examine the possible effect of the missing data. This analysis showed that candidates without known socioeconomic status were slightly more likely to be male (odds ratio 1.22, 95% confidence interval 1.12 to 1.33), less likely to be white (0.39, 0.36 to 0.43), and slightly less likely to be from independent/maintained/grammar schools (0.85, 0.78 to 0.93) (P<0.001 in all cases). Candidates with unknown socioeconomic status were also less likely to score at or above the 30th centile for UKCAT or to have high A level banding. For the UKCAT, the odds ratios varied between 0.58 (0.53 to 0.63) for the total score and 0.77 (0.70 to 0.85) for quantitative reasoning; for A levels, the odds ratio was 0.67 (0.61 to 0.73).
Multivariate analysis: independent predictors of UKCAT and A level scores
Linear regression analysis was not a suitable statistical test for independent predictors, because of the non-normal distributions of UKCAT and A level scores. Instead, we created simple binary markers of achievement so that binary logistic regression could be used. For A levels, we used the banding variable to select those applicants who had achieved the minimum requirement for admission—that is, AAA or AAB for their top three passes. This selected out the top 68% of candidates. For UKCAT, we calculated the 30th centile and set a “high score” marker for scores at or above this level. We then used univariate analysis (χ2 tests) to test these two outcomes against socioeconomic predictors; tables 4 and 5 show summary statistics. These confirm the results of the Mann-Whitney tests (above), in relation to the UKCAT scores. For A level banding, male candidates were not significantly better than female candidates, but white ethnicity and professional/managerial background did confer a significant advantage, and the strongest effect was from schooling.
Table 4 Univariate (χ2) analysis of binary predictor variables against A level band
Table 5 Univariate (χ2) analysis of binary predictor variables against higher UKCAT scores (at or above 30th centile)
We then subjected these data to multivariate binary logistic regression, using a hierarchical model in two blocks: the “primary” characteristics of sex and ethnicity and the “secondary” characteristics of parental socioeconomic status and schooling. Tables 6 and 7 show the results of these analyses. For UKCAT, male sex was a positive independent predictor of success in verbal reasoning, quantitative reasoning, and overall score but a weak negative predictor for abstract reasoning. It had no predictive influence in decision analysis. White ethnicity predicted success in all sections of the UKCAT apart from abstract reasoning, for which we found no influence. Professional/managerial background predicted success in all parts of the UKCAT but only weakly in quantitative and abstract reasoning. Schooling was an independent predictor throughout the test. For A levels, ethnicity, socioeconomic background, and schooling predicted being in the top A level band, although sex did not. The predictive effect of schooling was the strongest, with an odds ratio of 2.26. The amount of variance contributed by these four predictors was quite small in each case, especially for abstract reasoning.
Table 6 Binary logistic regression: independent predictors of high banding (AAA or AAB) for top three A level passes
Table 7 Hierarchical binary logistic regression: independent predictors of attaining UK Clinical Aptitude Test scores at or above 30th centile