print_relimp {DECIDE} | R Documentation |
Presents various estimates for measures of educational differentials, the relative importance of primary and secondary effects and corresponding standard errors and confidence intervals.
print_relimp(dataset)
dataset |
A data frame with 4 columns only, in the following order: 1: student's ID, 2: class, 3: transition (0 if not, 1 if yes) and 4: performance score. |
Returns a more nicely presented version of the results given by relative.importance
.
Christiana Kartsonaki
Kartsonaki, C., Jackson, M. and Cox, D. R. (2013). Primary and secondary effects: Some methodological issues, in Jackson, M. (ed.) Determined to succeed?, Stanford: Stanford University Press.
Erikson, R., Goldthorpe, J. H., Jackson, M., Yaish, M. and Cox, D. R. (2005) On Class Differentials in Educational Attainment. Proceedings of the National Academy of Sciences, 102: 9730–9733
Jackson, M., Erikson, R., Goldthorpe, J. H. and Yaish, M. (2007) Primary and secondary effects in class differentials in educational attainment: The transition to A-level courses in England and Wales. Acta Sociologica, 50 (3): 211–229
# generate a dataset
set.seed(1)
data <- data.frame(seq(1:10), rep(c(1, 2, 3), length.out = 10),
rbinom(1, n = 10, p = 0.7), c(rnorm(8, 0, 1), NA, NA))
# run function
print_relimp(data)