Are y_col and x_col appropriate indexs for r_mat?

Description

Are y_col and x_col appropriate indexs for r_mat?

Usage

.checkIndex(r_mat, y_col, x_col)

Arguments

Value

Nothing. Will result in an error if test fails.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Appends a new variable into a correlation matrix.

Description

Appends a new variable into a correlation matrix.

Usage

.corAdd(r_mat, r_vec, lab = "")

Arguments

Value

A larger correlation matrix.

Author(s)

Allen Goebl and Jeff Jones

Examples

#data(dls2007)
#dat <- dls2007
#rxx <- dat[1:4, 2:5]
#corAdd(rxx, c(.1,.1,.1,.1), lab="V5")

Computes the intercorrelations of item composites

Description

The key matrix is used to specify any number of weighted item composites. A correlation matrix of these composites is then computed and returned.

Usage

.fuseCom(r_mat, key_mat)

Arguments

Value

A matrix of intercorrelations.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

The intercorrelation among items and composites made of these items.

Description

The key matrix is used to specify any number of weighted item composites. A correlation matrix of these composites and the original correlation matrix is then computed and returned.

Usage

.fuseFull(r_mat, key_mat)

Arguments

Value

A matrix of intercorrelations.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Correlations between item composites and the original correlation matrix

Description

The key matrix is used to specify any number of weighted item composites. The correlations between each specified composite and the original correlation matrix are computed.

Usage

.fuseRmat(r_mat, key_mat)

Arguments

Value

A matrix of intercorrelations.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Finds rxx and rxy for a correlation matrix

Description

Finds rxx and rxy for a correlation matrix

Usage

.indexMat(r_mat, y_col, x_col)

Arguments

Value

Matrix rxx, and vector rxy.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Is a matrix a correlation matrix?

Description

Is a matrix a correlation matrix?

Usage

.isCorMat(r_mat)

Arguments

Value

Nothing. Will return an error if r_mat is not a correlation matrix

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Computes data needed for a XX Pareto plot.

Description

Computes data needed for a XX Pareto plot.

Usage

.paretoXX(r_mat, x_col, y_col, pts = 100)

Arguments

Value

Points along the pareto optimal surface and the predictor weights used to compute them.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Raju-Cabrera-Lezotte Utility Model

Description

Raju-Cabrera-Lezotte Utility Model

Usage

.rclUtility()

Relative weights

Description

Function to implement Johnson's (2000) relative weight computation.

Usage

.relWt(rxx, rxy)

Arguments

Value

DO THIS JEFF

Author(s)

Jeff Jones and Allen Goebl

References

Johnson, J. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1–19.

Examples

print("example needed")

Find regression weights

Description

Find regression weights

Usage

.rmatBeta(rxx, rxy)

Arguments

Value

A vector of regression weights.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Partially evaluated regression

Description

Returns a function for calculating beta weights which has been partially evalauted with respect to rxx.

Usage

.rmatBetaPE(rxx)

Arguments

Value

Partially evaluated regression function.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Find regression weights and R2

Description

Find regression weights and R2

Usage

.rmatReg(rxx, rxy)

Arguments

Value

R2 and Regression weights

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Correlation between weighted predictor composite and criterion.

Description

Correlation between weighted predictor composite and criterion.

Usage

.solveWt(wt_vec, rxx, rxy)

Arguments

Value

A correlation coefficent.

Note

This is a simpler, faster version of the formula used for fuse().

Author(s)

Allen Goebl Jeff Jones

Examples

library(iopsych)
data(dls2007)
dat <- dls2007[1:6, 2:7]
rxx <- dat[1:4, 1:4]
rxy <- dat[1:4, 5]

#.solveWt(wt_vec=c(1,1,1,1), rxx=rxx, rxy=rxy)
#.solveWt(wt_vec=c(1,2,3,4), rxx=rxx, rxy=rxy)

Correlation between weighted predictor composite and criterion.

Description

Correlation between weighted predictor composite and criterion.

Usage

.solveWtExp(wt, rxx, rxy_list)

Arguments

Value

A matrix of correlation coefficent with one row per weight vector and one column per rxy vector.

Note

This function should be merged with the fuse functions and replace the other .solvewt functions.

Author(s)

Allen Goebl Jeff Jones

Examples

library(iopsych)
data(dls2007)
dat <- dls2007[1:6, 2:7]
rxx <- dat[1:4, 1:4]
rxy1 <- dat[1:4, 5]
rxy2 <- dat[1:4, 6]
rxy_list <- list(rxy1, rxy2)

wt1 <- c(1,1,1,1)
wt2 <- c(1,2,3,4)
wt_mat <- rbind(wt1, wt2)

#.solveWtExp(wt=wt_mat, rxx=rxx, rxy_list=rxy_list)

Correlation between weighted predictor composite and criterion.

Description

Correlation between weighted predictor composite and criterion.

Usage

.solveWtVec(wt, rxx, rxy)

Arguments

Value

A correlation coefficent.

Author(s)

Allen Goebl Jeff Jones

Examples

library(iopsych)
data(dls2007)
dat <- dls2007[1:6, 2:7]
rxx <- dat[1:4, 1:4]
rxy <- dat[1:4, 5]
wt1 <- c(1,1,1,1)
wt2 <- c(1,2,3,4)
wt_list <- list(wt1, wt2)

#.solveWtVec(wt=wt1, rxx=rxx, rxy=rxy)
#.solveWtVec(wt=wt2, rxx=rxx, rxy=rxy)
#.solveWtVec(wt=wt_list, rxx=rxx, rxy=rxy)

Unpacks key vector

Description

Unpacks key vector

Usage

.unpack(key_vec)

Arguments

Value

Returns (1) A list of indices (2) A list of standardized weights.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Unpacks key matrix

Description

Works like .unpack but accepts matrices rather than vectors.

Usage

.unpackMat(key_mat)

Arguments

Value

Returns (1) A list of indices (2) A list of standardized weights.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Utility Input Switch

Description

Utility functions

Usage

.utilitySwitch(pux = NULL, uxs = NULL, rxy = NULL, sr = NULL)

Arguments

Value

The expected average criterion score of selected applicants.

Note

Many utility functions can except either (1) pux, (2) uxs and rxy, or (3) sr and rxy.

Author(s)

Allen Goebl and Jeff Jones

An internal function for generating criterion weights for XX pareto plots.

Description

An internal function for generating criterion weights for XX pareto plots.

Usage

.wtPair(pts)

Arguments

Value

A matrix of of criterion weights with one column per criteiron.

Author(s)

Allen Goebl and Jeff Jones

Examples

print("example needed")

Estimate adverse impact given d and sr

Description

Estimate adverse impact given d and sr

Usage

aiEst(d, sr, pct_minority)

Arguments

Value

(1) The adverse impact ratio, (2) The overall selection ration, (3) The selection ratio for the majority group, (4) The selection ratio for the minority group, and (5) the predictor cutoff value that corresponds to the given overall selection ratio

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

aiEst(d = 0.15, sr = 0.25, pct_minority = 0.30)

aiEst(d = 0.40, sr = 0.10, pct_minority = 0.15)

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

Description

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

Usage

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

Arguments

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.

Examples

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

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

Description

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

Usage

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

Arguments

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

# 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

Wee, Newman, & Joseph, (2014) ASVAB data

Description

This dataset was published in Wee, S., Newman, D. A., & Joseph, D. L. (2014) and describes the results of a military validation study. The first four rows contain the intercorrelations of the four predictor variables. The fifth row contains the black-white score differences (d). Rows 6-12 contain the correlations between the four predictor variables and the six job performance variables.

Usage

asvab

Format

A data frame with 12 rows and 4 columns.

References

Wee, S., Newman, D. A., & Joseph, D. L. (2014). More than g: Selection quality and adverse impact implications of considering second-stratum cognitive abilities. Journal of Applied Psychology, 99(4), Journal of Applied Psychology, 92(5), 1380.

Convert from r to d

Description

Convert from r to d

Usage

cor2d(r)

Arguments

Value

A d value or a vector of d values.

Author(s)

Allen Goebl and Jeff Jones

Examples

cor2d(.3)
cor2d(((1:9)/10))

Convert from d to r

Description

Convert from d to r

Usage

d2cor(d)

Arguments

Value

A r value or a vector of r values.

Author(s)

Allen Goebl and Jeff Jones

Examples

d2cor(.3)
d2cor(((1:9)))

Estimates the d of a composite.

Description

Estimates the d of a composite.

Usage

dComposite(rxx, d_vec, wt_vec = rep(1, length(d_vec)))

Arguments

Value

A vector of correlation coefficients.

Note

This is essentially the same function as solveWt().

Author(s)

Jeff Jones and Allen Goebl

References

Sackett, P. R., & Ellingson, J. E. (1997). Personnel Psychology., 50(3), 707-721.

Examples

Rxx <- matrix(.3, 3, 3); diag(Rxx) <- 1
ds  <- c(.2, .4, .3)
dComposite(rxx = Rxx, d_vec = ds)

Rxx <- matrix(c(1.0, 0.3, 0.2, 
                0.3, 1.0, 0.1,
                0.2, 0.1, 1.0), 3, 3)
ds  <- c(.1, .3, .7)
ws  <- c(1, .5, .5)
dComposite(rxx = Rxx, d_vec = ds, wt_vec = ws)

Decorte, Lievens, & Sackett (2007) example data

Description

This hypothetical dataset was published in Decorte, W., Lievens, F., Sackett, P. R. (2007). The first column contains black-white subgroup difference scores. Columns 2-7 contain a hypothetical predictor, job performance correlation matrix.

Usage

dls2007

Format

A data frame with 6 rows and 7 columns.

References

De Corte, W., Lievens, F., & Sackett, P. R. (2007) Combining predictors to achieve optimal trade-offs between selection quality and adverse impact. emphJournal of Applied Psychology, 92(5), 1380.

Computes the correlation between two composites of items.

Description

Computes the correlation between two composites of items. Composites may contain overalapping items. Items weights for each composite may be specified.

Usage

fuse(r_mat, a, b, wt_a = rep(1, length(a)), wt_b = rep(1, length(b)))

Arguments

Value

A correlation coefficient.

Author(s)

Allen Goebl and Jeff Jones

References

Lord, F.M. & Novick, M.R. (1968). Statisticl theories of menal test scores., 97-98.

Examples

Rxx <- matrix(c(1.00, 0.25,  0.50,  0.61,
                0.25, 1.00,  0.30,  0.10,
                0.50, 0.30,  1.00, -0.30,
                0.61, 0.10, -0.30,  1.00), 4, 4)
a   <- c(1, 3)
b   <- c(2, 4)

# Example using overlapping items and weights
Rxx  <- matrix(.3, 4, 4); diag(Rxx) <- 1
a    <- c(1, 2, 4)
b    <- c(2, 3)
wt_a <- c(.60, .25, .15)
wt_b <- c(2, 3)

fuse(r_mat = Rxx, a = a, b = b, wt_a = wt_a, wt_b = wt_b)

The intercorrelation among items and composites made of these items.

Description

The key matrix is used to specify any number of weighted item composites. A correlation matrix of these composites and the original correlation matrix is then computed and returned.

Usage

fuseMat(r_mat, key_mat, type = "full")

Arguments

Value

If type = cxc then a matrix of the intercorrelations between the specified composites are returned. If type = cxr then the intercorrelations between the original item and the specified composites are returned. If type = full then all the intercorrelations between both the original items and the specified composites are returned.

Author(s)

Allen Goebl and Jeff Jones

Examples

Rxx <- matrix(c(1.00, 0.25,  0.50,  0.61,
                0.25, 1.00,  0.30,  0.10,
                0.50, 0.30,  1.00, -0.30,
                0.61, 0.10, -0.30,  1.00), 4, 4); Rxx

# Single composite
Key <- matrix(c(1, 2, 3, -1), 1, 4); Key

fuseMat(r_mat = Rxx, key_mat = Key)

# Three composites
Key <- matrix(c(1, 2, 3, -1,
                2, 1, 0, -2,
                1, 1, 0,  0), 3, 4, byrow = TRUE)

fuseMat(Rxx, Key)

Computes the correlation between a composite and a vector of items.

Description

Computes the correlation between a composite and a vector of items.

Usage

fuseVec(r_mat, a, wt_a = rep(1, length(a)), output = "vec")

Arguments

Value

A vector of correlation coefficients.

Author(s)

Allen Goebl and Jeff Jones

References

Lord, F.M. & Novick, M.R. (1968). Statisticl theories of mental test scores., 97-98.

Examples

data(dls2007)
dat <- dls2007
rxx <- dat[1:4, 2:5]
items <- c(1,3)
wt_a <- c(2,1)

fuseVec(r_mat=rxx, a=items)
fuseVec(r_mat=rxx, a=items, wt_a=wt_a, output="mat")

Lawley multivariate range restriction correction.

Description

Lawley multivariate range restriction correction.

Usage

lMvrrc(rcov, vnp, as_cor = TRUE)

Arguments

Value

The the correlation matrix or variance covariance in the unrestricted population.

Author(s)

The original function was written by Adam Beatty and adapted by Allen Goebl.

References

Lawley D. N (1943). A note on Karl Pearson's selection formulae. Proceedings of the Royal Society of Edinburgh., 62(Section A, Pt. 1), 28-30.

Examples

data(rcea1994)
vstar <- rcea1994$vstar
vpp   <- rcea1994$vpp

lMvrrc(rcov=vstar, vnp=vpp)

Computes data needed for a XX Pareto plot.

Description

Computes data needed for a XX Pareto plot.

Usage

paretoXX(r_mat, x_col, y_col, pts = 100)

Arguments

Value

• betasA matrix of beta weights for each criteria weight

• wt_oneThe weight given to the first criterion

• multiple_rThe correlation between the predictor and criterion composites

Author(s)

Allen Goebl and Jeff Jones

Examples

# Setup Data
data(dls2007)
r_mat <- dls2007[1:6, 2:7]

#Run Model
XX1 <- paretoXX(r_mat=r_mat, x_col=1:4, y_col=5:6)

# Plot Multiple correlations
plot(c(0,1), c(.3,.5), type="n", xlab="C1 Wt", ylab="mr") 
lines(XX1$wt_one, (XX1$R2)[,1])
lines(XX1$wt_one, (XX1$R2)[,2])

Computes data needed for a XY Pareto plot.

Description

Computes data needed for a XY Pareto plot.

Usage

paretoXY(r_mat, x_col, y_col, d_vec, gen = 100, pop = 100,
  pred_lower = rep(-2, length(x_col)), pred_upper = rep(2, length(x_col)))

Arguments

Value

• betasA matrix of beta weights for each criteria weight

• mr_dA matrix of multiple correlations or d values corresponding to each row of beta weights.

• pareto_optimalA vector indicating whether each value is pareto optimal

Author(s)

Allen Goebl Jeff Jones

Examples

data(dls2007)
dat <- dls2007
r_mat <- dat[1:6, 2:7]
x_col <- 1:4 
y_col <- 5:6
d_vec <- dat[1:4, 1]

paretoXY(r_mat=r_mat, x_col=1:4, y_col=5, d_vec=d_vec, pred_lower=c(0,0,0,0))
paretoXY(r_mat=r_mat, x_col=1:4, y_col=c(5,6))

Ree, Carretta, Earles, Albert (1994)

Description

This example data was published in Ree, M. J., Carretta, T. R., Earles, J. A., & Albert, W. (1994). The data set contains two matrices stored as a list, which can be used to demonstrate multivariate range restriction corrections. The vstar matrix is the variance-covariance matrix of the unrestricted sample. The vpp matrix is the variance covariance matrix of the restricted sample. The vpp matrix represents the subset of variables which were explicitly used for selection, which are also found in the upper left corner of the vstar matrix.

Usage

rcea1994

Format

A list containing a 4x4 matrix and a 2x2 matrix as elements.

References

Ree, M. J., Carretta, T. R., Earles, J. A., & Albert, W. (1994). Sign changes when correcting for range restriction: A note on Pearson's and Lawley's selection formulas. Journal of Applied Psychology, 72(2), 298.

Relative weights

Description

Function to implement Johnson's (2000) relative weight computation.

Usage

relWt(r_mat, y_col, x_col)

Arguments

Value

A list containing the objects eps, beta_star, and lambda_star. The object eps contains the vector of relative weights of the predictors whose sum is equivalent to the model R^2 (see Johnson, 2000, ps 8 - 9). The object beta_star contains the regression weights from regressing the criterion on Z, the 'best fitting orthogonal approximation' of the predictor variables (see Johnson, 2000, p. 5). The object lambda_star contains the regression coefficients from regressing Z on the predictor variables (see Jonhson, 2000, p. 8).

Author(s)

Jeff Jones and Allen Goebl

References

Johnson, J. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1–19.

Examples

Rs <- matrix(c(1.0, 0.2,  0.3, 0.4, -0.4,
               0.2, 1.0,  0.5, 0.1,  0.1,
               0.3, 0.5,  1.0, 0.2, -0.3,
               0.4, 0.1,  0.2, 1.0,  0.4,
              -0.4, 0.1, -0.3, 0.4,  1.0), 5, 5)
ys <- 5
xs <- 1:4

relWt(Rs, ys, xs)

Disattenuate a correlation matrix using an estimate of the component reliabilities

Description

Disattenuate a correlation matrix using an estimate of the component reliabilities

Usage

reliabate(r_mat, rel_vec)

Arguments

Value

A reliabated (disattenauted) correlation matrix.

Author(s)

Allen Goebl and Jeff Jones

Examples

r_mat <- matrix(c(1.00, 0.25, 0.30, 
                  0.25, 1.00, 0.50, 
                  0.30, 0.50, 1.00), 3, 3)
rel   <- c(.70, .64, .81)
reliabate(r_mat = r_mat, rel_vec = rel)

Regression

Description

Regression

Usage

rmatReg(r_mat, y_col, x_col)

Arguments

Value

Regression beta weights and R2.

Author(s)

Allen Goebl and Jeff Jones

Examples

Rs <- matrix(c(1.0, 0.2,  0.3, 0.4, -0.4,
              0.2, 1.0,  0.5, 0.1,  0.1,
              0.3, 0.5,  1.0, 0.2, -0.3,
              0.4, 0.1,  0.2, 1.0,  0.4,
             -0.4, 0.1, -0.3, 0.4,  1.0), 5, 5)
ys <- 5
xs <- 1:4

rmatReg(Rs, ys, xs)

Partially evaluated regression

Description

Returns a function for calculating beta weights and R2 which has been partially evalauted with respect to rxx.

Usage

rmatRegPE(rxx)

Arguments

Value

Partially evaluated regression function.

Author(s)

Allen Goebl and Jeff Jones

Examples

Rxx <- matrix(c(1.00, 0.25, 0.40,
                0.25, 1.00, 0.30,
                0.40, 0.30, 1.00), 3, 3)

rmatRegPE(Rxx)

Find r given arbitrary predictor weights

Description

Find r given arbitrary predictor weights

Usage

solveWt(r_mat, y_col, x_col, wt)

Arguments

Value

The correlation between the weighted predictor composite and criterion.

Note

This uses a simpler, faster version of the same formula used for fuse().

Author(s)

Allen Goebl and Jeff Jones

Examples

library(iopsych)
#Get Data
data(dls2007)
r_mat <- dls2007[1:6, 2:7]

#Get weights
unit_wt <- c(1,1,1,1)
other_wt <- c(1,2,1,.5)
wt_list <- list(unit_wt, other_wt)

#Solve
solveWt(r_mat=r_mat, y_col=6, x_col=1:4, wt=unit_wt)
solveWt(r_mat=r_mat, y_col=6, x_col=1:4, wt=other_wt)
solveWt(r_mat=r_mat, y_col=6, x_col=1:4, wt=wt_list)

Find R2 given arbitrary predictor weights

Description

Find R2 given arbitrary predictor weights

Usage

solveWtR2(r_mat, y_col, x_col, wt)

Arguments

Value

Regression R2.

Note

This just calls solveWt() and squares the output.

Author(s)

Allen Goebl and Jeff Jones

Examples

library(iopsych)
#Get Data
data(dls2007)
r_mat <- dls2007[1:6, 2:7]

#Get weights
unit_wt <- c(1,1,1,1)
other_wt <- c(1,2,1,.5)
wt_list <- list(unit_wt, other_wt)

#Solve
solveWtR2(r_mat=r_mat, y_col=6, x_col=1:4, wt=unit_wt)
solveWtR2(r_mat=r_mat, y_col=6, x_col=1:4, wt=other_wt)
solveWtR2(r_mat=r_mat, y_col=6, x_col=1:4, wt=wt_list)

Taylor-Russell Ratio

Description

Computes the Taylor Russel ratio

Usage

trModel(rxy, sr, br)

Arguments

Value

The success ratio.

Author(s)

Allen Goebl and Jeff Jones

References

Taylor, H. C., & Russell, J. T. (1939). The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables. Journal of Applied Psychology, 25(5), 565.

Examples

trModel(rxy=.5, sr=.5, br=.6)

Taylor-Russell Utility

Description

The Taylor Russel Model can be used to estimate the utility of selecting for a dichotomous criterion.

Usage

trUtility(n = 1, rxy, sr, br, dbr, cost = 0, period = 1)

Arguments

Value

Estimated gain in utility.

Author(s)

Allen Goebl and Jeff Jones

References

Roomsburg (1989). Utility as a function of selection ratio and base rate: An empirical investigation of military aviation selection. (Doctoral dissertation).

Examples

#trUtility(rxy=.5, sr=.5, br=.6, dbr=1000)

Boudreau Utility Model.

Description

This utility model extends the BCG model with additional financial variables.

Usage

utilityB(n = 1, sdy, rxy = NULL, uxs = NULL, sr = NULL, pux = NULL,
  cost = 0, period = 1, v = 0, tax = 0, i = 0)

Arguments

Value

Estimated gain in utility.

Note

This functions can except either (1) pux, (2) uxs and rxy, or (3) sr and rxy.

Author(s)

Allen Goebl and Jeff Jones

References

Boudreau, J.W. (1983). Economic considerations in estimating the utility of human resource productivity improvement programs. Personnel Psychology, 36, 551-576.

Examples

utilityB(sdy=10000, rxy=.50, sr=.30, period=4, v=.5, tax=.1, i=.02)

Brogeden-Cronbach-Gleser Utility Model.

Description

Estimates the utility of an employee selection system.

Usage

utilityBcg(n = 1, sdy, rxy = NULL, uxs = NULL, sr = NULL, pux = NULL,
  cost = 0, period = 1)

Arguments

Value

Estimated gain in utility.

Note

This functions can except either (1) pux, (2) uxs and rxy, or (3) sr and rxy.

Author(s)

Allen Goebl and Jeff Jones

References

Cronbach, L. J., & Gleser, G. C. (1965). Psychological tests and personnel decisions., 37-40.

Examples

utilityBcg(sdy=10000, rxy=.50, sr=.30)

Raju-Burke-Normand Utility Model

Description

This utility model uses SD of job performance ratings rather than the SD of job performance in monetary units.

Usage

utilityRbn(n = 1, sdr, a, rxy, uxs = NULL, sr = NULL, pux = NULL,
  cost = 0, period = 1)

Arguments

Value

Estimated gain in utility.

Note

This functions can except either (1) pux, (2) uxs and rxy, or (3) sr and rxy.

Author(s)

Allen Goebl and Jeff Jones

References

Raju, N.S., Burke, M.J. and Normand, J. (1990). A new approach for utility analysis. Journal of Applied Psychology, 75, 3-12.

Examples

utilityRbn(sdr=10000, a=90000, rxy=.50, sr=.30)

Schmidt-Hunter-Pearlman Utility Model.

Description

This model calculates the utility of an intervention accepting d rather than rxy as an argument.

Usage

utilityShp(n = 1, sdy, d, cost = 0, period = 1)

Arguments

Value

Estimated gain in utility.

Author(s)

Allen Goebl and Jeff Jones

References

Schmidt, F. L., Hunter, J. E., & Pearlman, K. (1982). Assessing the economic impact of personnel programs on workforce productivity. Personnel Psychology, 35(2), 333-347.

Examples

utilityShp(sdy=10000, d=.50, period=4)

The average score of selected applicants on a predictor composite.

Description

When scores on the predictor composite are assumed to be normally distributed, the average score of selected applicants can be computed for an arbitrary selection ratio using the ordinate of the normal curve.

Usage

ux(sr)

Arguments

Value

ux: The average score of those selected on a predicter composite.

Author(s)

Allen Goebl and Jeff Jones

References

Naylor, J. C., & Shine, L. C. (1965). A table for determining the increase in mean criterion score obtained by using a selection device. Journal of Industrial Psychology, 78-109.

Examples

ux(.6)