In this strategy, each applicant is measured on each predictor. An applicant with any predictor score below a minimum cutoff is rejected. Thus the combination method is identical to the multiple-cutoff procedure to this point. Next, multiple regression is used to calculate overall scores for all applicants who pass the cutoffs. Then the applicants who remain can be rank-orded based on their overall scores calculated by the regression equation. This part of the procedure is idential to the multiple regression approach.
Consequently, the combination method is a hybrid of the multiple-cutoff and multiple regression approaches. The combination method has two major assumptions. The more restrictive assumption is derived from the multiple-cutoff approach. That is, a minimal level of each predictor attribute is necessary to perform the job. After that level has been reached, more of one predictor attribute can compensate for less of another in predicting overall success of the applicants. This assumption is derived from the multiple regression approach.
The combination method has the advantages of the multiple cutoff approach. But rather than merely identifying a pool of acceptable candidates, which is what happens when using the multiple cutoff approach, the combination approach additionally provides a way to rank-order acceptable appliacants. The majore disadvantage of the combination method is that it is more costly than the multiple hurdle approach, because all applicants are screened on all predictors. Consequently the cost savings are not afforded by the multiple hurdle approach's reduction of the applicant pool.
The combination method is most appropriate when the assumption of multiple cutoffs is reasonable and more of one predictor attribute can compensate for another above the minimum cutoffs. It is also more appropriate to use this approach when the size of the applicant pool is not too large and costs of administering selection procedures do not vary greatly amon the procedures.