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

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Description

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

Usage

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

Arguments

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 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.

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 who are selected.

pct_minority

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

Value

  • AIAdverse Impact

  • Overeall_srThe overall selection ratio set by the user

  • Majority_srMajority Selection Rate

  • Minority_srMinority Selection Rate

  • Majority_StandardizedPredicted composite criterion score relative to the majority population

  • Global_StandardizedPredicted composite criterion score relative to the overall population

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

Examples

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# 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

# Note that the d-values are reversed from what the CAIQS manual reports (see pg 4)
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

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