Back Hiring Decisions with Numbers (Assessment Utility Calculator)

Hommel, Björn E.

Blog Utility Models 4 min read Oct 2, 2026 Auf Deutsch lesen

Back Hiring Decisions with Numbers (Assessment Utility Calculator)

What is a better selection procedure worth? Our tool helps you play through different scenarios and calculate how higher accuracy in the hiring process adds value to your organization.

BEH

Dr. Björn E. Hommel Senior Scientist

What to calculate

Interactive Module 1. Assessment utility calculator. Click a result row for its details, or two rows to compare them; the tabs below the table switch between figures and an explanation in words. In the rings, indigo marks correct decisions and grey wrong ones; when comparing, the outer ring is the earlier scenario and the inner ring the newer one (the swap button reverses them). The first scenario is the reference: its value is measured against hiring at random, and every later scenario shows its gain or loss against it, so ideally start with your current procedure. Select two rows to see what switching from the earlier to the newer one is worth. Abbreviations: SR = selection ratio (hires/applicants), BR = base rate, r = validity, Δ = difference (newer minus earlier scenario). Amounts use the currency you pick and are not converted.

What the Calculator Does

The calculator starts with the Taylor-Russell model^[1]: how many of your hires will succeed, given the applicant pool, the number of hires, the base rate and the validity of your selection tool. The validity benchmarks come from the meta-analysis by Sackett et al.^[2] Four optional extensions build on this baseline:

Monetary value

Expresses the better hires in money (in a currency of your choice), following the utility model of Brogden, Cronbach and Gleser^{[3, 4]}. The key input is how much performance differs between people in the same position. A common, conservative rule of thumb is 40% of salary^{[5, 6]}, and the differences grow with job complexity^[7]. Each scenario describes one procedure. The first scenario is valued against hiring at random and serves as the reference for every later one, so it is best to start with your current procedure. Selecting two rows shows what switching from one to the other is worth, including the minimum validity at which the new procedure pays off.

Taxes, costs and turnover

Apply the economic adjustments proposed by Boudreau^{[8, 9]}: variable costs, taxes, discounting, and staff leaving over the planning horizon. Sturman^[10] showed that such adjustments can shrink utility estimates considerably, so they are worth switching on before you present a figure.

Declined offers and probation

Account for candidates turning offers down, which pushes offers further down the ranking^[11], and count the hires who fail their probation and what that costs^[12].

Combining two tools

Compares one overall score with a step-by-step (multiple-hurdle) selection^{[13, 14]}, using the correlations between instruments reported by Berry et al.^[15] where available.

Every calculation is added as a row to the results table, so you can compare scenarios side by side. The job-position presets use median salaries by skill level from the German Federal Employment Agency^[16]; their applicant numbers, base rates and costs are illustrative. The structure and several default values follow the BCG utility tool by Horstmann and Ziegler^[17]. All computations use the R package personnelSelectionUtility^[18] and run locally in your browser.

References

Taylor, H. C., & Russell, J. T. (1939). The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables. Journal of Applied Psychology, 23(5), 565–578. https://doi.org/10.1037/h0057079
Sackett, P. R., Zhang, C., Berry, C. M., & Lievens, F. (2022). Revisiting meta-analytic estimates of validity in personnel selection: Addressing systematic overcorrection for restriction of range. Journal of Applied Psychology, 107(11), 2040–2068. https://doi.org/10.1037/apl0000994
Brogden, H. E. (1949). When testing pays off. Personnel Psychology, 2(2), 171–183. https://doi.org/10.1111/j.1744-6570.1949.tb01397.x
Cronbach, L. J., & Gleser, G. C. (1965). Psychological tests and personnel decisions (2nd ed.). University of Illinois Press.
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(6), 609–626. https://doi.org/10.1037/0021-9010.64.6.609
Schmidt, F. L., & Hunter, J. E. (1983). Individual differences in productivity: An empirical test of estimates derived from studies of selection procedure utility. Journal of Applied Psychology, 68(3), 407–414. https://doi.org/10.1037/0021-9010.68.3.407
Hunter, J. E., Schmidt, F. L., & Judiesch, M. K. (1990). Individual differences in output variability as a function of job complexity. Journal of Applied Psychology, 75(1), 28–42. https://doi.org/10.1037/0021-9010.75.1.28
Boudreau, J. W. (1983). Economic considerations in estimating the utility of human resource productivity improvement programs. Personnel Psychology, 36(3), 551–576. https://doi.org/10.1111/j.1744-6570.1983.tb02235.x
Boudreau, J. W. (1983). Effects of employee flows on utility analysis of human resource productivity improvement programs. Journal of Applied Psychology, 68(3), 396–406. https://doi.org/10.1037/0021-9010.68.3.396
Sturman, M. C. (2000). Implications of utility analysis adjustments for estimates of human resource intervention value. Journal of Management, 26(2), 281–299. https://doi.org/10.1177/014920630002600206
Murphy, K. R. (1986). When your top choice turns you down: Effect of rejected offers on the utility of selection tests. Psychological Bulletin, 99(1), 133–138. https://doi.org/10.1037/0033-2909.99.1.133
De Corte, W. (1994). Utility analysis for the one-cohort selection-retention decision with a probationary period. Journal of Applied Psychology, 79(3), 402–411. https://doi.org/10.1037/0021-9010.79.3.402
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. https://doi.org/10.1027/1866-5888/a000205
Sturman, M. C. (2001). Utility analysis for multiple selection devices and multiple outcomes. Journal of Human Resource Costing & Accounting, 6(2), 9–28. https://doi.org/10.1108/eb029072
Berry, C. M., Lievens, F., Zhang, C., & Sackett, P. R. (2024). Insights from an updated personnel selection meta-analytic matrix: Revisiting general mental ability tests’ role in the validity–diversity trade-off. Journal of Applied Psychology, 109(10), 1611–1634. https://doi.org/10.1037/apl0001203
Statistik der Bundesagentur für Arbeit. (2026). Blickpunkt Arbeitsmarkt: Analyse zur Entgeltstatistik 2025. Bundesagentur für Arbeit. https://statistik.arbeitsagentur.de/DE/Statischer-Content/Statistiken/Fachstatistiken/Beschaeftigung/Generische-Publikationen/Blickpunkt-Arbeitsmarkt-Analyse-zur-Entgeltstatistik.pdf
Horstmann, K. T., & Ziegler, M. (2026). BCG Utility-Analyse: Brogden–Cronbach–Gleser [Web application]. https://kthorstmann.github.io/bcg_utility/
Gempp, R. (2026). personnelSelectionUtility: Utility analysis methods for personnel selection (R package version 1.0.2) [Computer software]. https://CRAN.R-project.org/package=personnelSelectionUtility

Author: BEH

Dr. Björn E. Hommel Senior Scientist

Björn has worked in psychological assessment for more than a decade, in both practice and research. In his academic work, he studies how language models and AI can improve the quality of psychological measurement. At magnolia psychometrics, he builds these methods into practical tools that help organizations conduct more reliable, data-driven assessments.
Topics: Tools Personnel Selection
Cite as: Hommel, B. E. (2026, October 2). Back hiring decisions with numbers (assessment utility calculator). magnolia psychometrics. https://magnolia-psychometrics.com/blog/assessment-utility-calculator

@misc{Hommel2026Back, author = {Hommel, Bj{\"o}rn E.}, title = {{Back Hiring Decisions with Numbers (Assessment Utility Calculator)}}, howpublished = {magnolia psychometrics}, year = 2026, month = oct, url = {https://magnolia-psychometrics.com/blog/assessment-utility-calculator}, note = {Blog post} }
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