di_prop_index {DisImpact}

Calculate disproportionate impact per the proportionality index (PI) method.

Description

Calculate disproportionate impact per the proportionality index (PI) method.

Usage

di_prop_index(success, group, cohort, weight, data, di_prop_index_cutoff = 0.8)

Arguments

success

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).

group

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.

cohort

(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.

weight

(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.

data

(Optional) A data frame containing the variables of interest. If data is specified, then success, group, and cohort will be searched within it.

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.

Details

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.

Value

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.

References

California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans.

Examples

library(dplyr)
data(student_equity)
di_prop_index(success=Transfer, group=Ethnicity, data=student_equity) %>%
  as.data.frame