relative.importance: Relative importance of primary and secondary effects

Description

Calculates various estimates for measures of educational differentials, the relative importance of primary and secondary effects and corresponding standard errors and confidence intervals.

Usage

relative.importance(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

sample_size

Total number of individuals

no_classes

Number of classes

class_size

A list of no_classes elements, each element containing the size of each class

percentage_overall

Overall percentage that made the transition

percentage_class

A list of no_classes elements, each element containing percentage that made the transition for each class

fifty_point

50% point of transition

parameters

A data frame with the parameters of logistic regression (\(\alpha, \beta\)) and normal distribution (\(\mu, \sigma\)) for each class

transition_prob

A data frame with the transition probabilities

log_odds

A data frame with log odds of transition (diagonal elements: actual log odds for each class, off-diagonal: counterfactual log odds)

se_logodds

A data frame with the standard errors of the log odds of transition

ci_logodds

Approximate 95% confidence intervals for the log odds of transition

odds

Odds of transition

log_oddsratios

Log odds ratios

se_logoddsratios

Standard errors for the log odds ratios

ci_logoddsratios

Approximate 95% confidence intervals for the log odds ratios

oddsratios

Odds ratios

rel_imp_prim1

Estimates of the relative importance of primary effects using the first equation for calculating the relative importance

rel_imp_prim2

Estimates of the relative importance of primary effects using the second equation for calculating the relative importance

rel_imp_prim_avg

Estimates of the relative importance of primary effects using the the average of the two equations for calculating the relative importance

rel_imp_sec1

Estimates of the relative importance of secondary effects using the first equation for calculating the relative importance

rel_imp_sec2

Estimates of the relative importance of secondary effects using the second equation for calculating the relative importance

rel_imp_sec_avg

Estimates of the relative importance of secondary effects using the the average of the two equations for calculating the relative importance

se.ri.1

Standard errors of the relative importance estimates given by the first equation

ci.ri.1

Approximate 95% confidence intervals for the relative importance of secondary effects given by the first equation

se.ri.2

Standard errors of the relative importance estimates given by the second equation

ci.ri.2

Approximate 95% confidence intervals for the relative importance of secondary effects given by the second equation

se.ri.avg

Standard errors of the relative importance estimates given by the average of the two equations

ci.ri.avg

Approximate 95% confidence intervals for the relative importance of secondary effects given by the average of the two equations

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

Run this code

# NOT RUN {
# generate a dataset
set.seed(1)
data <- 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))

# run function
relative.importance(data)
# }