print_relimp {DECIDE} | R Documentation |
Print tables of estimates
Description
Presents various estimates for measures of educational differentials, the relative importance of primary and secondary effects and corresponding standard errors and confidence intervals.
Usage
print_relimp(dataset)
Arguments
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. |
Value
Returns a more nicely presented version of the results given by relative.importance
.
Author(s)
Christiana Kartsonaki
References
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
See Also
Examples
# 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)