Naylor-Shine expected criterion gain

Computes expected standardized criterion gain among selected applicants and, optionally, converts it to utility using sdy, n_selected, tenure, and cost. The expected standardized criterion gain is validity * selected_mean_z(selection_ratio).

Usage

naylor_shine(
  validity,
  selection_ratio,
  sdy = 1,
  n_selected = 1,
  tenure = 1,
  cost = 0
)

Arguments

validity

Predictor-criterion validity, usually denoted r_xy.

selection_ratio

Selection ratio, usually denoted SR.

sdy

Standard deviation of job performance in monetary or criterion units.

n_selected

Number of selected applicants.

tenure

Expected tenure or number of periods.

cost

Total cost.

Value

A psu_ns object.

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, 3, 33-42.

Examples

# Literature: Naylor and Shine (1965).
# Use the first call as a minimal example; the longer block illustrates
# how to interpret the function in the substantive setting discussed in the literature.
# Minimal example: standardized criterion gain only.
naylor_shine(validity = .35, selection_ratio = .20)
#> <psu_ns>
#>   validity: 0.35
#>   selection_ratio: 0.2
#>   selected_mean_z: 1.39981
#>   expected_criterion_z: 0.489933
#>   sdy: 1
#>   n_selected: 1
#>   tenure: 1
#>   cost: 0
#>   gross_utility: 0.489933
#>   net_utility: 0.489933

# Substantive example (Naylor and Shine (1965)): standardized gain translated to monetary utility.
naylor_shine(
  validity = .35,
  selection_ratio = .20,
  sdy = 50000,
  n_selected = 100,
  tenure = 3,
  cost = 75000
)
#> <psu_ns>
#>   validity: 0.35
#>   selection_ratio: 0.2
#>   selected_mean_z: 1.39981
#>   expected_criterion_z: 0.489933
#>   sdy: 50000
#>   n_selected: 100
#>   tenure: 3
#>   cost: 75000
#>   gross_utility: 7349000
#>   net_utility: 7274000