Abstract

The aim of the quantitative element of this research project was to investigate the factors impacting GCSE achievement in secondary education in Sunderland using statistical analysis. Information governance arrangements were secured, and the researchers received child-level attainment, attendance, exclusion and school census data from Sunderland. Descriptive statistics were used to highlight the distribution of pupils throughout demographic categories in Sunderland, and overall Attainment 8 and Progress 8 outcomes relative to these demographic characteristics. A multi-level modelling approach was taken to conduct inferential statistical analysis on these datasets, distinguishing between school-level and individual-level factors, and focusing on Attainment 8 and Progress 8 as outcome variables. The largest impact on both attainment and progression appears to be the pupil’s prior attainment band at KS2, where high prior attainers were likely to have higher KS4 attainment than ‘Middle’ or ‘Low’ band prior attainers. The sizes of these effects are by far the largest in the data set. Girls predominantly outperformed boys aside from Maths scores, and Asian/British Asian pupils tended to outperform White and Black/Black British pupils. Those with no SEN status showed higher attainment and progress scores than those on SEN support or in possessing an education, health and care plan (EHCP). Frequently, effects such as being a looked after child, receiving free school meals, suspensions, and absences were greater in the high prior attainment band. The statistical models account for substantial variability in attainment and progress, but suggest that there are other unmeasured factors associated with variability in the outcomes.Some effects are more pronounced in certain cohorts or prior attainment groups, such as being a looked after child, receiving at least one suspension, being eligible for free school meals, and higher rates of absence. Frequently, these effects were greater in the high prior attainment band.
The best model generated by this analysis explained 60% of the variability in KS4 attainment and progress, while explained variability dropped to 20-30% for some more specified models. This suggests that there are other unmeasured factors associated with variability in the outcomes, and these factors are not measurable using this study’s data.