alevchem {R2MLwiN} | R Documentation |
Chemistry A-level results from one exam board
Description
Chemistry A-level results from one exam board; subset from Yang & Woodhouse, 2001. See also Rasbash et al. (2012) and Browne (2012).
Usage
alevchem
Format
A data frame with 2166 observations on the following 8 variables:
- lea
Local Education Authority ID.
- estab
Establishment (institution) ID.
- pupil
Pupil ID.
- a_point
A-level point score (an ordered factor with levels:
F
,E
,D
,C
,B
,A
).- gcse_tot
Total GCSE point score.
- gcse_no
Number of GCSEs taken.
- cons
Constant of ones
- gender
Pupil's gender (a factor with levels:
male
,female
).
Details
The alevchem
dataset is one of the sample datasets provided with the
multilevel-modelling software package MLwiN (Rasbash et al., 2009).
Source
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.
Examples
## 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)