Modelling the Effects of Pupil Mobility and Neighbourhood on School Differences in Educational Achievement

Traditional studies of school differences in educational achievement use multilevel modelling techniques to take into account the nesting of pupils within schools. However, educational data are known to have more complex non-hierarchical structures. The potential importance of such structures is apparent when considering the impact of pupil mobility during secondary schooling on educational achievement. Movements of pupils between schools suggest that we should model pupils as belonging to the series of schools attended and not just their final school. Since these school moves are strongly linked to residential moves, it is important to additionally explore whether achievement is also affected by the history of neighbourhoods lived in. Using the national pupil database (NPD), this paper combines multiple-membership and cross-classified multilevel models to simultaneously explore the relationships between secondary school, primary school, neighbourhood and educational achievement. The results show a negative relationship between pupil mobility and achievement, the strength of which depends greatly on the nature and timing of these moves. Accounting for pupil mobility also reveals that schools and neighbourhoods are more important than shown by previous analysis. A strong primary school effect appears to last long after a child has left that phase of schooling. The additional impact of neighbourhoods, on the other hand, is small. Crucially, the rank order of school effects across all types of pupils is sensitive to whether we account for the complexity of the multilevel data structure.Cross-classified models, Multiple-membership-models, Multilevel modelling, Pupil mobility, School effectiveness, Value-added models

Should we adjust for pupil background in school value-added models? A study of Progress 8 and school accountability in England

In the UK, US and elsewhere, school accountability systems increasingly compare schools using value-added measures of school performance derived from pupil scores in high-stakes standardised tests. Rather than naively comparing school average scores, which largely reflect school intake differences in prior attainment, these measures attempt to compare the average progress or improvement pupils make during a year or phase of schooling. Schools, however, also differ in terms of their pupil demographic and socioeconomic characteristics and these also predict why some schools subsequently score higher than others. Many therefore argue that value-added measures unadjusted for pupil background are biased in favour of schools with more 'educationally advantaged' intakes. But, others worry that adjusting for pupil background entrenches socioeconomic inequities and excuses low performing schools. In this article we explore these theoretical arguments and their practical importance in the context of the 'Progress 8' secondary school accountability system in England which has chosen to ignore pupil background. We reveal how the reported low or high performance of many schools changes dramatically once adjustments are made for pupil background and these changes also affect the reported differential performances of region and of different school types. We conclude that accountability systems which choose to ignore pupil background are likely to reward and punish the wrong schools and this will likely have detrimental effects on pupil learning. These findings, especially when coupled with more general concerns surrounding high-stakes testing and school value-added models, raise serious doubts about their use in school accountability systems

The Limitations of Using School League Tables to Inform School Choice

In England, so-called ‘league tables’ based upon examination results and test scores are published annually, ostensibly to inform parental choice of secondary schools. A crucial limitation of these tables is that the most recent published information is based on the current performance of a cohort of pupils who entered secondary schools several years earlier, whereas for choosing a school it is the future performance of the current cohort that is of interest. We show that there is substantial uncertainty in predicting such future performance and that incorporating this uncertainty leads to a situation where only a handful of schools’ future performances can be separated from both the overall mean and from one another with an acceptable degree of precision. This suggests that school league tables, including value-added ones, have very little to offer as guides to school choice.Examination results, Institutional comparisons, League tables, Multilevel modelling, Performance indicators, Ranking, School choice, School effectiveness, Value-added

runmixregls - A Program to run the MIXREGLS mixed-effects location scale software from within Stata

Hedeker and Nordgren (2013) present the stand-alone MIXREGLS program for fitting the mixed-effects location scale model to continuous longitudinal and other clustered data. This model can be used when interest lies in joint modeling the mean and dispersion of subjects' responses over time. The model extends the standard two-level randomintercept mixed model by allowing both the within- and between-subject variances to be influenced by the covariates and for the within-subject variance to additionally depend on a subject random-scale effect. In this article we present the runmixregls command to run MIXREGLS seamlessly from within Stata. We illustrate the notable advantages of using runmixregls by replicating and extending the two example analyses presented in Hedeker and Nordgren (2013). We then use runmixregls to demonstrate a new and important research finding. Namely, that ignoring the random-scale effect in the withinsubject variance function will lead to the regression coefficients in this function to be estimated with spurious precision, especially the regression coefficients of subject-level covariates

A multilevel modelling approach to measuring changing patterns of ethnic composition and segregation among London secondary schools, 2001-2010

Multilevel binomial logistic regression has recently been proposed for the special case of statistically modelling changing composition and segregation of two groups of individuals over two occasions among organizational units, enabling inferences to be made about the underlying social processes which generate these patterns. A simulation method can then be used to re-express the model parameters in the metric of any desired two-group segregation index. We generalize this combined modelling and simulation approach by proposing multilevel random-coefficient multinomial logistic regression for the general case of statistically modelling multiple groups of individuals over multiple occasions and multiple organizational scales. We illustrate this combined approach with an application to modelling changing three-group white–black–Asian ethnic composition and segregation among London secondary schools and local authorities during the first decade of the 21st century