personnelSelectionUtility systematizes classical and
contemporary utility-analysis methods for personnel selection under
consistent notation, organised by criterion scale (classification or
continuous/monetary) and selection structure (compensatory or
multiple-hurdle).
The package implements Taylor-Russell univariate and Thomas-Owen-Gunst multivariate classification models, Brogden-Cronbach-Gleser and Boudreau-style continuous and monetary utility, Schmidt-Hunter-Pearlman intervention utility, Sturman-style incremental validity for multiple predictors and multiple outcomes, the integrated Sturman comprehensive cascade, simulation tools for compensatory and staged multiple-hurdle systems, Pareto frontiers for validity-diversity trade-offs, AUC-to-effect-size conversions, and Monte Carlo uncertainty propagation.
# Stable installation from CRAN (when available)
install.packages("personnelSelectionUtility")
# Development installation
pak::pak("rgempp/personnelSelectionUtility")
# or
remotes::install_github("rgempp/personnelSelectionUtility", build_vignettes = TRUE)The package uses readable R argument names while preserving the notation used in the utility-analysis literature.
library(personnelSelectionUtility)
argument_glossary()Key conventions:
base_rate = BR or phi:
population proportion successful before selection.
selection_ratio = SR: proportion selected
by one cutoff or composite.
selection_ratios = vector of marginal SRs
for multiple predictors or stages.
joint_selection_ratio = overall conjunctive selection
ratio under multiple cutoffs.
validity = r_xy; validities =
vector of predictor-criterion correlations.
sdy = SD_y, the monetary or criterion-unit
standard deviation of job performance.
baseline_validity = validity of the operating system
used as comparator (Sturman, 2000, 2001).
n_applicants = number of applicants assessed;
n_selected = number selected.
tenure = expected number of periods.
n_by_period, cost_by_period = preferred
names in boudreau_utility() for time-varying
parameters.
range_restriction_ratio = the literature’s
u, the ratio of unrestricted to restricted predictor
standard deviations.
The package is organised around two decisions that should be made explicit before computing utility:
| Criterion scale | Compensatory selection | Multiple-hurdle selection |
|---|---|---|
| Continuous / monetary | bcg_utility(),
boudreau_utility(),
shp_utility(),
restricted_canonical_validity(),
sturman_comprehensive() |
multiple_hurdle_selection_staged(),
compare_selection_systems_staged(),
selection_utility_from_z() |
| Dichotomised / classificatory | tr_classic(),
tr_solve() |
tr_multivariate(),
tr_multivariate_equal_cutoff(),
group_tr_multivariate() |
library(personnelSelectionUtility)
# Brogden-Cronbach-Gleser utility with an operating baseline
bcg_utility(
validity = .35,
selection_ratio = .20,
sdy = 50000,
n_selected = 100,
tenure = 3,
cost = 75000,
baseline_validity = .20
)
# Taylor-Russell, univariate
tr_classic(base_rate = .50, selection_ratio = .20, validity = .35)
# Thomas-Owen-Gunst multivariate Taylor-Russell with specified marginal cutoffs
R <- matrix(c(
1.00, .30, .40,
.30, 1.00, .35,
.40, .35, 1.00
), nrow = 3, byrow = TRUE)
tr_multivariate(selection_ratios = c(.50, .50), base_rate = .50, R = R)
# Thomas-Owen-Gunst equal-cutoff design indexed by a target joint selection ratio
R_tog <- matrix(c(
1.00, .50, .70,
.50, 1.00, .70,
.70, .70, 1.00
), nrow = 3, byrow = TRUE)
tr_multivariate_equal_cutoff(joint_selection_ratio = .20, base_rate = .60, R = R_tog)
# AUC effect-size conversions for algorithmic or classificatory summaries
auc_to_rank_biserial(.75)
auc_to_d_equal_variance(.75)
auc_to_point_biserial(.75, base_rate = c(.50, .30, .20))
# Sturman (2001) integrated comprehensive utility model
S11 <- matrix(c(1, .30, .30, 1), 2, 2)
S12 <- matrix(c(.30, .10, .15, .25), 2, 2, byrow = TRUE)
S22 <- matrix(c(1, .40, .40, 1), 2, 2)
sturman_comprehensive(
validity = .35,
baseline_validity = .20,
selection_ratio = .20,
sdy = 50000,
n_year_one = 100,
tenure = 5,
fixed_cost = 75000,
hires_per_period = c(100, 15, 15, 15, 15),
losses_per_period = c(0, 15, 15, 15, 15),
tax_rate = .25,
discount_rate = .08,
predictor_cor = S11,
predictor_criterion_cor = S12,
criterion_cor = S22,
criterion_weights = c(.7, .3),
probation_cutoff_z = -1,
acceptance_rate = .70,
quality_acceptance_correlation = -0.20
)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.
Brogden, H. E. (1949). When testing pays off. Personnel Psychology, 2, 171-183.
Cronbach, L. J., & Gleser, G. C. (1965). Psychological tests and personnel decisions (2nd ed.). University of Illinois Press.
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36.
Holling, H. (1998). Utility analysis of personnel selection: An overview and empirical study based on objective performance measures. Methods of Psychological Research Online, 3(1), 5-24.
Kerby, D. S. (2014). The simple difference formula: An approach to teaching nonparametric correlation. Comprehensive Psychology, 3, 11.IT.3.1.
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.
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.
Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615-620.
Salgado, J. F. (2018). Transforming the area under the normal curve (AUC) into Cohen’s d, Pearson’s r_pb, odds-ratio, and natural log odds-ratio: Two conversion tables. The European Journal of Psychology Applied to Legal Context, 10(1), 35-47.
Schmidt, F. L., Hunter, J. E., McKenzie, R. C., & Muldrow, T. W. (1979). Impact of valid selection procedures on work-force productivity. Journal of Applied Psychology, 64, 609-626.
Sturman, M. C. (2000). Implications of utility analysis adjustments for estimates of human resource intervention value. Journal of Management, 26, 281-299.
Sturman, M. C. (2001). Utility analysis for multiple selection devices and multiple outcomes. Journal of Human Resource Costing and Accounting, 6(2), 9-28.
Taylor, H. C., & Russell, J. T. (1939). The relationship of validity coefficients to the practical effectiveness of tests in selection. Journal of Applied Psychology, 23, 565-578.
Thomas, J. G., Owen, D. B., & Gunst, R. F. (1977). Improving the use of educational tests as selection tools. Journal of Educational Statistics, 2(1), 55-77.