Schmidt-Hunter-Pearlman intervention utility

Computes utility from an intervention effect size rather than from a selection validity coefficient. This is appropriate for training or intervention designs where the key input is a standardized mean difference.

Usage

shp_utility(effect_size_d, sdy, n_treated, tenure, cost = 0)

Arguments

effect_size_d

Standardized mean difference caused by the intervention.

sdy

Standard deviation of job performance in monetary units.

n_treated

Number of employees receiving the intervention.

tenure

Expected duration of the effect in periods.

cost

Total intervention cost.

Value

A psu_shp object.

References

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

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.

Examples

# Literature: Schmidt, Hunter, and Pearlman (1982); Cohen (1988).
# 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 (Schmidt, Hunter, and Pearlman (1982); Cohen (1988)).
shp_utility(effect_size_d = .30, sdy = 50000, n_treated = 100,
            tenure = 2, cost = 40000)
#> <psu_shp>
#>   effect_size_d: 0.3
#>   approximate_r: 0.14834
#>   sdy: 50000
#>   n_treated: 100
#>   tenure: 2
#>   cost: 40000
#>   gross_utility: 3e+06
#>   net_utility: 2960000

# Substantive example (Schmidt, Hunter, and Pearlman, 1982;
# Cohen, 1988). Compare two training designs.
short_training <- shp_utility(.20, 50000, n_treated = 120, tenure = 1, cost = 30000)
intensive_training <- shp_utility(.35, 50000, n_treated = 120, tenure = 1,
                                  cost = 95000)
c(short = short_training$net_utility, intensive = intensive_training$net_utility)
#>     short intensive 
#>   1170000   2005000