Reference no: EM132883399

Multiple Hurdles Model

With a multiple hurdles approach, an applicant must earn a passing score on each predictor before advancing in the selection process. Such an approach is taken when each requirement measured by a predictor is critical to job success. The decision to move a candidate forward is based on either a top-down selection from early predictor scores (or through the use of cut scores at some stages) or a top-down selection for later stages (discussed in the next section). Unlike the compensatory model, the multiple hurdles model does not allow a high score on one predictor to compensate for a low score on another predictor, because some individuals have no scores on some predictors. Many organizations use multiple hurdles selection systems to both reduce the cost of selecting applicants and make the decision-making process more tractable in the final selection stage. It would be very inefficient to process all the possible information the organization might collect on a large number of candidates, so some candidates are screened out relatively early in the process. Typically, the first stage of a selection process screens the applicant pool down to those who meet some minimal educational or years-of-experience requirement. Collecting information on such requirements is fairly inexpensive for organizations and can usually be readily quantified. After this stage, the pool of remaining applicants might be further reduced by administering relatively inexpensive standardized tests to those who passed the initial screen. Further reducing the pool of potential candidates allows the organization to devote more resources to interviewing finalists and having them meet with managers at the organization's headquarters. This is the selection stage. There are many variations in how the multiple hurdles model can be implemented, and the exact nature of the "screen" versus "select" measures will vary based on the job requirements.

Describe the Multiple Hurdles predictor method of selection - What advantage do you see in this method compared to those given in the compensatory methods explained in the answer?

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