R2MLwiN (version 0.1-7)

tutorial: Exam results for six inner London Education Authorities

Description

A subset of data from a much larger dataset of examination results from six inner London Education Authorities (school boards).

source

Goldstein, H., Rasbash, J., Yang, M., Woodhouse, G., Pan, H., Nuttall, D., Thomas, S. (1993) A multilevel analysis of school examination results. Oxford Review of Education, 19, 425--433. Rasbash, J., Browne, W. J., Healy, M., Cameron, B., Charlton, C. M. J. (2013) MLwiN v2.27. University of Bristol: Centre for Multilevel Modelling.

Details

The tutorial dataset is one of the sample datasets provided with the multilevel-modelling software package MLwiN (Rasbash et al., 2013), and is a subset of data from a much larger dataset of examination results from six inner London Education Authorities (school boards). The original analysis (Goldstein et al., 1993) sought to establish whether some secondary schools had better student exam performance at 16 than others, after taking account of variations in the characteristics of students when they started secondary school; i.e., the analysis investigated the extent to which schools `added value' (with regard to exam performance), and then examined what factors might be associated with any such differences.

See Also

See mlmRev package for an alternative format of the same dataset.

Examples

# NB: hange path as appropriate
MLwiN <- "C:/Program Files (x86)/MLwiN v2.27/"
data(tutorial)

# Fit 2-level variance components model, using IGLS (default estimation method)
F1 <- "normexam ~ (0|cons) + (2|cons) + (1|cons)"
ID <- c("school", "student")
(VarCompModel <- runMLwiN(Formula = F1, levID = ID, indata = tutorial, MLwiNPath = MLwiN))
# print variance partition coefficient (VPC)
print(VPC <- VarCompModel["RP"][["RP2_var_cons"]] / (VarCompModel["RP"][["RP1_var_cons"]] + 
VarCompModel["RP"][["RP2_var_cons"]]))

# Fit same model using MCMC
(VarCompMCMC <- runMLwiN(Formula = F1, levID = ID, indata = tutorial, 
estoptions = list(EstM = 1), MLwiNPath = MLwiN))
# return diagnostics for VPC
VPC_MCMC <- VarCompMCMC["chains"][,"RP2_var_cons"] / (VarCompMCMC["chains"][,"RP1_var_cons"] + 
VarCompMCMC["chains"][,"RP2_var_cons"])
sixway(VPC_MCMC, name = "VPC")

# Adding predictor, allowing its coefficient to vary across groups (i.e. random slopes)
F2 <- "normexam ~ (0|cons + standlrt) + (2|cons + standlrt) + (1|cons)"
(standlrtRS_MCMC <- runMLwiN(
Formula = F2, levID = ID, indata = tutorial,
estoptions = list(EstM = 1), MLwiNPath = MLwiN))

# Example modelling complex level 1 variance
F3 <- "normexam ~ (0|cons + standlrt) + (2|cons + standlrt) + (1|cons + standlrt)"
standlrtC1V_MCMC <- runMLwiN (
Formula = F3, levID = ID, indata = tutorial,
estoptions = (list(EstM = 1,
#fit log of precision at level 1 as a function of predictors
mcmcMeth = list(lclo = 1))), 
MLwiNPath = MLwiN)