di_prop_index {DisImpact} | R Documentation |
Calculate disproportionate impact per the proportionality index (PI) method.
di_prop_index(success, group, cohort, weight, data, di_prop_index_cutoff = 0.8)
success |
A vector of success indicators ( |
group |
A vector of group names of the same length as |
cohort |
(Optional) A vector of cohort names of the same length as |
weight |
(Optional) A vector of case weights of the same length as |
data |
(Optional) A data frame containing the variables of interest. If |
di_prop_index_cutoff |
A numeric value between 0 and 1 that is used to determine disproportionate impact if the proportionality index falls below this threshold; defaults to 0.80. |
This function determines disproportionate impact based on the proportionality index (PI) method, as described in this reference from the California Community Colleges Chancellor's Office. It assumes that a higher rate is good ("success"). For rates that are deemed negative (eg, rate of drop-outs, high is bad), then consider looking at the converse of the non-success (eg, non drop-outs, high is good) instead in order to leverage this function properly.
A data frame consisting of:
cohort
(if used),
group
,
n
(sample size),
success
(number of successes for the cohort-group),
pct_success
(proportion of successes attributed to the group within the cohort),
pct_group
(proportion of sample attributed to the group within the cohort),
di_prop_index
(ratio of pct_success to pct_group),
di_indicator
(1 if di_prop_index < di_prop_index_cutoff
), and
success_needed_not_di
(the number of additional successes needed in order to no longer be considered disproportionately impacted as compared to the reference), and
success_needed_full_parity
(the number of additional successes needed in order to achieve full parity with the reference).
When di_prop_index < 1
, then there are signs of disproportionate impact.
California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans.
library(dplyr)
data(student_equity)
di_prop_index(success=Transfer, group=Ethnicity, data=student_equity) %>%
as.data.frame