Chemistry A-level results from one exam board; subset from Yang & Woodhouse, 2001. See also Rasbash et al. (2012) and Browne (2012).
A data frame with 2166 observations on the following 8 variables:
Local Education Authority ID.
Establishment (institution) ID.
A-level point score (an ordered factor with levels:
Total GCSE point score.
Number of GCSEs taken.
Constant of ones
Pupil's gender (a factor with levels:
alevchem dataset is one of the sample datasets provided with the multilevel-modelling software package MLwiN (Rasbash et al., 2009).
Browne, W. J. (2012) MCMC Estimation in MLwiN Version 2.26. University of Bristol: Centre for Multilevel Modelling.
Rasbash, J., Charlton, C., Browne, W.J., Healy, M. and Cameron, B. (2009) MLwiN Version 2.1. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Steele, F., Browne, W.J. and Goldstein, H. (2012) A User's Guide to MLwiN Version 2.26. Centre for Multilevel Modelling, University of Bristol.
Yang, M., Woodhouse, G. (2001) Progress from GCSE to A and AS level: institutional and gender differences, and trends over time. British Educational Research Journal 27: 245-267.
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## Not run: data(alevchem, package = "R2MLwiN") alevchem$gcseav <- alevchem$gcse_tot/alevchem$gcse_no - 6 # Note: Establishment codes on their own do not uniquely identify schools. # Schools are instead uniquely identified by LEA code, establishment ID # combination. Thus, here we generated a unique school ID. alevchem$school <- as.numeric(factor(paste0(alevchem$lea, alevchem$estab))) (mymodel <- runMLwiN(logit(a_point, cons, 6) ~ 1 + gcseav[1:5] + I(gcseav^2)[1:5] + gender[1:5] + (1[1:5] + gcseav[1:5] | school), D = "Ordered Multinomial", estoptions = list(EstM = 1), data = alevchem)) ## End(Not run)
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