DECIDE (version 1.3)

compare.relimp: Compare estimates of log odds, log odds ratios and relative importance obtained by two datasets

Description

Computes 95% confidence intervals for the differences in log odds of transition, log odds ratios and relative importance estimates between the two datasets. Also calculates chi-squared test statistics and p-values for testing whether the differences are different from zero.

Usage

compare.relimp(dataset1, dataset2)

Arguments

dataset1

is the first dataset; a data frame with 4 columns, in the following order: 1: student's ID, 2: class, 3: transition (0 if not, 1 if yes) and 4: performance score.

dataset2

is the second dataset; a data frame with 4 columns, in the following order: 1: student's ID, 2: class, 3: transition (0 if not, 1 if yes) and 4: performance score.

Value

ci.diff.lo

95% confidence intervals for differences in log odds of transition

test.diff.lo

Test statistic for differences in log odds

test.diff.lo.pvalue

p-value for testing for differences in log odds

ci.diff.lor

95% confidence intervals for differences in log odds ratios

test.diff.lo

Test statistic for differences in log odds ratios

test.diff.lo.pvalue

p-value for testing for differences in log odds ratios

ci.diff.ri.1

95% confidence intervals for relative importance estimates - 1

ci.diff.ri.2

95% confidence intervals for relative importance estimates - 2

ci.diff.ri.avg

95% confidence intervals for relative importance estimates - average

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

Examples

# NOT RUN {
# generate two datasets
set.seed(1)
data1 <- data.frame(seq(1:10), rep(c(1, 2), length.out = 10), 
c(rep(0, times = 3), rep(1, times = 7)), 
c(rnorm(4, 0, 1), rnorm(4, 0.5, 1), NA, NA))

data2 <- data.frame(seq(1:10), rep(c(1, 2), length.out = 10), 
c(rep(0, times = 5), rep(1, times = 5)), 
c(rnorm(5, 1, 1), rnorm(5, 0.5, 1)))

# run function
compare.relimp(data1, data2)
# }