Designing pareto-optimal selection systems for multiple minority subgroups and multiple criteria.
扩展了帕累托最优选拔系统设计方法，使其能同时处理多个少数群体和多个选拔标准，并通过实例和交叉验证展示了新方法的优势。
Currently used Pareto-optimal (PO) approaches for balancing diversity and validity goals in selection can deal only with one minority group and one criterion. These are key limitations because the workplace and society at large are getting increasingly diverse and because selection system designers often have interest in multiple criteria. Therefore, the article extends existing methods for designing PO selection systems to situations involving multiple criteria and multiple minority groups (i.e., multiobjective PO selection systems). We first present a hybrid multiobjective PO approach for computing selection systems that are PO with respect to (a) a set of quality objectives (i.e., criteria) and (b) a set of diversity objectives where each diversity objective relates to a different minority group. Next, we propose three two-dimensional subspace procedures that aid selection designers in choosing between the PO systems in case of a high number of quality and diversity objectives. We illustrate our novel multiobjective PO approaches via several example applications, thereby demonstrating that they are the first to reveal the complete gamut of eligible PO selection designs and to faithfully capture the Pareto trade-off front in case of more than two objectives. In addition, a small-scale cross-validation study confirms that the resulting PO selection designs retain an advantage over alternative designs when applied in new validation samples. Finally, the article provides a link to an executable code to perform the new multiobjective PO approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).