Department of Mathematics and Statistics, Tshwane University of Technology, South Africa; Department of Statistics and Operations Research, Sefako Makgatho Health Sciences University, South Africa; School of Public Administration, University of New Mexico, Albuquerque, NM, United States
Malebye, W.P.R., Department of Mathematics and Statistics, Tshwane University of Technology, South Africa; Seeletse, S.M., Department of Statistics and Operations Research, Sefako Makgatho Health Sciences University, South Africa; Rivera, M.A., School of Public Administration, University of New Mexico, Albuquerque, NM, United States
Applicants for employment are usually compared subjectively in the selection process, and the selections made are typically not reliable, if only because they are seldom verifiable empirically. The present study describes a process of much more objective selection sequence, one that involves a quantitative/mathematical measure that may be used in selecting a candidate applying for a job, in a process then adds two other independent measures to validate the decision taken. The approach followed is a stepwise combination of SToR methods (Statistics and Operations Research, incorporating SAW, TOPSIS, and WP). In this analysis, SAW (simple additive weighting) is used in the first-cut selection process, and TOPSIS (technique for order preference by similarity to ideal solution) and WP (weighted product) are used to validate selections. A practical exercise was developed from an actual selection problem, part of a real-world recruitment task undertaken in an organization for which the authors consulted, and in which the human resources (HR) department wanted to check if their selection was justifiable, and demonstrably valid. The resulting analytical approach was clearly valid, consistent, reliable, and replicable, and convincing to that HR department, since it considered the determinations made by our system quite satisfactory, while theirs could not stand up to empirical testing or corroboration. © Wilson P.R. Malebye, Solly M. Seeletse, Mario A. Rivera, 2015.