Question

Match each of the following statements with the appropriate selection decision making strategy. BE SURE TO EXPLAIN BRIEFLY WHY YOU TAKE A PARTICULAR STAND.

a.	Multiple regression	d.	Combination method
b.	Multiple cutoffs	e.	profile matching
c.	Multiple hurdle

       

9.         A "double-stage strategy" is a variation of this approach

10.       A hybrid of multiple cutoff and multiple regression approaches

11.       A nonsequential procedure

12.       A sequential procedure

13.       Applicants with different individual predictor scores can have identical overall predicted job success.

14.       Appropriate when there is a clearly best type of employee for the job

15.       When relatively small samples are used to determine weights, the weights may not be stable from one sample to the next

16.       Assumes predictors are additive

17.       Assumes predictors are linearly related to criterion

18.       Is appropriate when the applicant pool is large and when some of the selection procedures are expensive to administer

19.       Most appropriate in situations where subsequent training is long, complex, and expensive.

20.       Most appropriate when tradeoffs among predictors do not affect overall job performance

21.       Most useful when physical abilities are essential for job performance

22.       Uses a d2 statistic

23.       Only identifies those applicants minimally qualified for the job

24.       Predictors are not additive

25.       Sometimes referred to as a compensatory method

Answer 1

9.         A "double-stage strategy" is a variation of this approach – multiple hurdle because it uses two sets of cut-off scores for e.g. C1 & C2. Candidates getting above C2 are unconditionally accepted, below C1 are blindly rejected and in-between C1 and C2 are provisionally accepted, subjected to further testing.

10.       A hybrid of multiple cut-off and multiple regression approaches – Combination Method because in this method uses a two-step approach. Firstly, each applicant is measured on each predictor and any applicant who has a score below the minimum cutoff in that predictor is rejected. This is same as multiple cut-off method. Secondly, multiple regression is used to calculate overall scores of the applicants who pass the cut-off and ranked according to their scores calculated by regression equation.

11.       A non-sequential procedure – multiple cut-off as predictor assessments are not conducted based on any specific order.

12.       A sequential procedure – multiple hurdle because only after clearing the minimum cutoff for one predictor the successful candidate can move onto the next predictor in sequence.

13.       Applicants with different individual predictor scores can have identical overall predicted job success – multiple regression as predictor scores are simply added in the regression equation irrespective of high or low score in any predictor.

14.       Appropriate when there is a clearly best type of employee for the job – multiple hurdle it minimizes the candidate pool to only the best that can do the job.

15.       When relatively small samples are used to determine weights, the weights may not be stable from one sample to the next – profile matching uses filters to determine the matching values.

16.       Assumes predictors are additive – multiple regression as all predictor scores are added together in a linear regression equation.

17.       Assumes predictors are linearly related to criterion – multiple regression because it uses a simple linear regression equation to calculate the overall predictor score. Therefore multiple applicants can show same value of predicted job performance irrespective of different high or low score in each predictor.

18.       Is appropriate when the applicant pool is large and when some of the selection procedures are expensive to administer – multiple hurdle as at each stage of selection the number of candidates will reduce thereby reducing the cost and more expensive procedures like interview can be conducted later with final few candidates.

19.       Most appropriate in situations where subsequent training is long, complex, and expensive – multiple hurdle so that it can be administered only to the high end of the most capable candidates.

20.       Most appropriate when tradeoffs among predictors do not affect overall job performance – multiple regression because it calculates the overall score of all predictors and does not give importance to high or low score in any particular criterion.

21.       Most useful when physical abilities are essential for job performance – multiple cut-off as identifies only those candidates who minimally qualify for the job with the most essential attributes.

22.       Uses a d2 statistic – combination method as 2 criteria used to determine the best.

23.       Only identifies those applicants minimally qualified for the job – multiple cut-off as each applicant should score above a minimum cut-off for each predictor attribute and high score in one predictor cannot compensate for low score in another.

24.       Predictors are not additive – multiple cut-off because each predictor has a minimum cut-off score which should be acquired to qualify for the next step.

25.       Sometimes referred to as a compensatory method – Multiple Regression because it is possible to compensate for low scores on one predictor by high scores on another.