di_prop_index {DisImpact} R Documentation

## 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


[Package DisImpact version 0.0.21 Index]