The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.