di_ppg {DisImpact}  R Documentation 
Calculate disproportionate impact per the percentage point gap (PPG) method.
Description
Calculate disproportionate impact per the percentage point gap (PPG) method.
Usage
di_ppg(
success,
group,
cohort,
weight,
reference = c("overall", "hpg", "all but current", unique(group)),
data,
min_moe = 0.03,
use_prop_in_moe = FALSE,
prop_sub_0 = 0.5,
prop_sub_1 = 0.5,
check_valid_reference = TRUE
)
Arguments
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 
reference 
Either

data 
(Optional) A data frame containing the variables of interest. If 
min_moe 
The minimum margin of error (MOE) to be used in the calculation of disproportionate impact and is passed to ppg_moe. Defaults to 
use_prop_in_moe 
A logical value indicating whether or not the MOE formula should use the observed success rates ( 
prop_sub_0 
For cases where 
prop_sub_1 
For cases where 
check_valid_reference 
Check whether 
Details
This function determines disproportionate impact based on the percentage point gap (PPG) 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 dropouts, high is bad), then consider looking at the converse of the nonsuccess (eg, non dropouts, high is good) instead in order to leverage this function properly. Note that the margin of error (MOE) is calculated using using 1.96*sqrt(0.25^2/n)
, with a min_moe
used as the minimum by default.
Value
A data frame consisting of:

cohort
(if used), 
group
, 
n
(sample size), 
success
(number of successes for the cohortgroup), 
pct
(proportion of successes for the cohortgroup), 
reference_group
(reference group used in DI calculation), 
reference
(reference value used in DI calculation), 
moe
(margin of error), 
pct_lo
(lower 95% confidence limit for pct), 
pct_hi
(upper 95% confidence limit for pct), 
di_indicator
(1 if there is disproportionate impact, ie, whenpct_hi <= reference
), 
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).
References
California Community Colleges Chancellor's Office (2017). Percentage Point Gap Method.
Examples
library(dplyr)
data(student_equity)
# Vector
di_ppg(success=student_equity$Transfer
, group=student_equity$Ethnicity) %>% as.data.frame
# Tidy and column reference
di_ppg(success=Transfer, group=Ethnicity, data=student_equity) %>%
as.data.frame
# Cohort
di_ppg(success=Transfer, group=Ethnicity, cohort=Cohort
, data=student_equity) %>%
as.data.frame
# With custom reference (single)
di_ppg(success=Transfer, group=Ethnicity, reference=0.54
, data=student_equity) %>%
as.data.frame
# With custom reference (multiple)
di_ppg(success=Transfer, group=Ethnicity, cohort=Cohort
, reference=c(0.5, 0.55), data=student_equity) %>%
as.data.frame
# min_moe
di_ppg(success=Transfer, group=Ethnicity, data=student_equity
, min_moe=0.02) %>%
as.data.frame
# use_prop_in_moe
di_ppg(success=Transfer, group=Ethnicity, data=student_equity
, min_moe=0.02
, use_prop_in_moe=TRUE) %>%
as.data.frame