Calculate disproportionate impact per the proportionality index (PI) method.
di_prop_index(success, group, cohort, weight, data, di_prop_index_cutoff = 0.8)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.
A vector of success indicators (1/0 or TRUE/FALSE) or an unquoted reference (name) to a column in data if it is specified. It could also be a vector of counts, in which case weight should also be specified (group size).
A vector of group names of the same length as success or an unquoted reference (name) to a column in data if it is specified.
(Optional) A vector of cohort names of the same length as success or an unquoted reference (name) to a column in data if it is specified. disproportionate impact is calculated for every group within each cohort. When cohort is not specified, then the analysis assumes a single cohort.
(Optional) A vector of case weights of the same length as success or an unquoted reference (name) to a column in data if it is specified. If success consists of counts instead of success indicators (1/0), then weight should also be specified to indicate the group size.
(Optional) A data frame containing the variables of interest. If data is specified, then success, group, and cohort will be searched within it.
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.
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
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