An article in yesterday’s Guardian newspaper reports on research conducted by Stephen Gorard stating that “claims that schools in the North of England are worse than those in the South are based on myth and bad data.”
Interestingly, his analysis, conducted on three annual cohorts of pupils, finds the same results as our single cohort school-level analysis reported in our recent blog; that differences between GCSE performance are not driven by a North/South divide and that factors affecting performance are, in fact, multifaceted and complex.
The article advises against just using high-level statistics, and highlights the importance of undertaking in-depth analyses. In our analyses of the available data, we fit a statistical model to the data and found deprivation to be a driver of performance; areas of high-levels of deprivation tended to have lower GCSE performance. Interestingly, Stephen Gorard’s research has delved into this a little deeper and shows that it is not just whether or not pupils are eligible for free school meals that affects attainment, but that a more important factor is the length of time that pupils have faced disadvantage. The article says that the current measure of deprivation, whether a child is eligible for free school meals or not, does not capture enough of the aspects of socio-economic deprivation or disadvantage.
Of course, in the education sector and other fields that use observational studies, whilst we include as many of the influential and relevant factors in any analysis, we must always be aware that analyses are often limited by the factors you can include, or more importantly, what you can’t include. Many analyses of student outcomes can’t, for example, take account of factors such as motivation, the effect of inspirational teachers, or home resources since these are not simple to measure.
Given past headlines stating the existence of a North/South educational divide, how do we know that an analysis has been conducted appropriately and whether or not to believe a headline? In our experience, clear and honest reporting is vital; detailing not only the results, but also the methods, any assumptions and limitations. By being clear about what is and isn’t included in your analyses and what it does and doesn’t tell you enables others to appropriately evaluate the evidence themselves.