aiPux: Estimate ai and average criterion scores for majority and...

Description Usage Arguments Value Author(s) References Examples

Description

Estimate ai and average criterion scores for majority and minority groups.

Usage

1
aiPux(mr, dx, dy = 1, sr, pct_minority)

Arguments

mr

The correlation between the predictor and criterion composites.

dx

A vector of d values for the predictors. These d values are expected to have been computed in the direction of Majority - Minority.

dy

A vector of d values for the criteria These d values are expected to have been computed in the direction of Majority - Minority.

sr

The percentage of the applicant population who are selected.

pct_minority

The percentage of the applicant population who are part of a given minority group.

Value

Author(s)

Jeff Jones and Allen Goebl

References

De Corte, W., Lievens, F.(2003). A Practical procedure to estimate the quality and the adverse impact of single-stage selection decisions. International Journal of Selection and Assessment., 11(1), 87-95.

Examples

1
2
aiPux(.6, dx=.8, sr=.3, pct_minority=.25)
aiPux(.6, dx=.8, dy=.2, sr=.3, pct_minority=.25)

Example output

$AI
[1] 0.3420737

$Overall_sr
[1] 0.3

$Majority_sr
[1] 0.3590524

$Minority_sr
[1] 0.1228224

$Majority_Standardized
           [,1]
Zi -0.006678063
Za  0.624605487
Zt  0.559981664

$Global_Standardized
        [,1]
Zi 0.2232875
Za 0.8025930
Zt 0.7432902

$AI
[1] 0.3420737

$Overall_sr
[1] 0.3

$Majority_sr
[1] 0.3590524

$Minority_sr
[1] 0.1228224

$Majority_Standardized
        [,1]
Zi 0.7933219
Za 0.6246055
Zt 0.6418633

$Global_Standardized
        [,1]
Zi 0.8401772
Za 0.6720899
Zt 0.6892833

iopsych documentation built on May 2, 2019, 2:27 p.m.