Organizational Psychology

Description Usage Arguments Value Author(s) References Examples

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

1	aiPuxComposite(r_mat, y_col, x_col, dX, dY, wt_x, wt_y, sr, pct_minority)

`r_mat`	Super correlation matrix between the predictors and criteria. This argument assumes that the predictors come first in the matrix.
`y_col`	A vector of columns representing criterion variables.
`x_col`	A vector of columns representing predictor variables.
`dX`	A vector of d values for the predictors. These d values are expected to have been computed in the direction of Minority - Majority.
`dY`	A vector of d values for the criteria These d values are expected to have been computed in the direction of Minority - Majority.
`wt_x`	Weights for the predictors to form the overall composite predictor.
`wt_y`	Weights for the criteria to form the overall composite criterion.
`sr`	The percentage of the applicant population that is selected.
`pct_minority`	The percentage of the applicant population that is part of a given minority group.

AI: Adverse Impact
Overeall_sr: The overall selection ratio set by the user
Majority_sr: Majority Selection Rate
Minority_sr: Minority Selection Rate
Majority_Standardized: Predicted composite criterion score relative to the majority population
Global_Standardized: Predicted composite criterion score relative to the overall population

Jeff Jones and Allen Goebl

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.

De Corte, W. (2003). Caiqs user's guide. http://allserv.rug.ac.be/~wdecorte/software.html

# Example taken from De Corte, W. (2003)
R <- matrix(c(1.000, 0.170, 0.000, 0.100, 0.290, 0.160, 
              0.170, 1.000, 0.120, 0.160, 0.300, 0.260, 
              0.000, 0.120, 1.000, 0.470, 0.120, 0.200, 
              0.100, 0.160, 0.470, 1.000, 0.240, 0.250, 
              0.290, 0.300, 0.120, 0.240, 1.000, 0.170, 
              0.160, 0.260, 0.200, 0.250, 0.170, 1.000), 6, 6)

wt_x <- c(.244, .270, .039, .206) 
wt_y <- c(6, 2)
sr    <- 0.25
pct_minority <- .20

dX   <- c(-1, -0.09, -0.09, -0.20)
dY   <- c(-0.450, 0.0)

aiPuxComposite(R, 5:6, 1:4, dX, dY, wt_x, wt_y, sr, pct_minority)

# compare the output from predictAI with the output in the CAIQS manual on page 7 where SR = .250