Weighting admission scores to balance predictiveness‐diversity: The Pareto‐optimization approach.
Context Although many medical schools seek to improve diversity, they grapple with the challenge of how to weight the scores of different admission methods to achieve a balance between obtaining high predictiveness and ensuring diversity in the selected student pool. Yet, in large‐scale employment s...
| Publicado en: | Medical Education Vol. 56; no. 2; pp. 151 - 159 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154688794&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154688794 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03080110 ESF jtl: Medical Education issn: 03080110 maglogo: Y pubinfo: dt: Feb2022 vid: 56 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154688794 151981778 154688794 154688794 10.1111/medu.14606 154688794 ppf: 151 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Weighting admission scores to balance predictiveness‐diversity: The Pareto‐optimization approach. aug: au: Lievens, Filip Sackett, Paul R. De Corte, Wilfried affil: Lee Kong Chian School of Business, Singapore Management University,, Singapore sug: subj: Schools, Medical School Admissions Cultural Diversity Models, Statistical Human Regression Mathematics ab: Context Although many medical schools seek to improve diversity, they grapple with the challenge of how to weight the scores of different admission methods to achieve a balance between obtaining high predictiveness and ensuring diversity in the selected student pool. Yet, in large‐scale employment settings, substantial progress has been made on this front: Pareto‐optimization has been introduced as an elegant statistical tool to assist decision makers in determining the weights assigned to selection methods in advance (before the selection has taken place) so that a selection system is designed to achieve an optimal balance as reflected by the trade‐off that one outcome (e.g., predictiveness) cannot be improved without harm to the other outcome (e.g., diversity). Aims: This paper reviews the theory and research evidence about Pareto‐optimization and explains how Pareto‐optimization permits medical schools to better balance predictiveness and diversity in medical admission systems. Methods: After reviewing common weighting schemes (unit, regression‐based and ad hoc weighting) and their drawbacks, we introduce the theory and logic of Pareto‐optimization for better balancing predictiveness and diversity. To this end, we also offer an illustrative example. Next, we review the mathematical basis and available research evidence regarding Pareto‐optimization. Finally, we discuss potential criticisms (i.e., complexity and legal concerns). Conclusions: Compared to traditional unit weighting, regression‐based weighting and ad hoc weighting, Pareto‐optimization leads to substantial increases in diversity intake (up to three times more), while keeping the predictiveness of the selection methods at the same level. Moreover, the Pareto‐optimization is robust to sampling variability and variability of the input selection parameters. This paper explains how Pareto‐optimization permits medical schools to better balance predictiveness and diversity in medical admissions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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