Computes BCG net utility for all combinations of selected parameter values.
References
Cronshaw, S. F., Alexander, R. A., Wiesner, W. H., & Barrick, M. R. (1987). Incorporating risk into selection utility. Organizational Behavior and Human Decision Processes, 40, 270-286.
Ock, J., & Oswald, F. L. (2018). The utility of personnel selection decisions: Comparing compensatory and multiple-hurdle selection models. Journal of Personnel Psychology, 17(4), 172-182.
Boudreau, J. W. (1991). Utility analysis for decisions in human resource management. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (Vol. 2, pp. 621-745). Consulting Psychologists Press.
Examples
# Literature: Cronshaw et al. (1987); Boudreau (1991); Ock and Oswald (2018).
# 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 (Cronshaw et al. (1987); Boudreau (1991); Ock and Oswald (2018)).
sensitivity_grid(validity = c(.20, .30), selection_ratio = c(.10, .20),
sdy = c(40000, 60000), n_selected = 100, tenure = 3)
#> validity selection_ratio sdy selected_mean_z net_utility
#> 1 0.2 0.1 40000 1.754983 4211960
#> 2 0.3 0.1 40000 1.754983 6317940
#> 3 0.2 0.2 40000 1.399810 3359543
#> 4 0.3 0.2 40000 1.399810 5039315
#> 5 0.2 0.1 60000 1.754983 6317940
#> 6 0.3 0.1 60000 1.754983 9476910
#> 7 0.2 0.2 60000 1.399810 5039315
#> 8 0.3 0.2 60000 1.399810 7558972
# Substantive example (Cronshaw et al., 1987; Boudreau, 1991;
# Ock and Oswald, 2018). Find the best sensitivity scenario.
grid <- sensitivity_grid(validity = seq(.20, .40, .10),
selection_ratio = c(.10, .20, .40),
sdy = c(30000, 60000),
n_selected = 100, tenure = 3, cost = 75000)
grid[which.max(grid$net_utility), ]
#> validity selection_ratio sdy selected_mean_z net_utility
#> 12 0.4 0.1 60000 1.754983 12560880