Chapter 11 Choosing and Hiring Candidates

Chapter 11 Choosing and Hiring Candidates - Chapter 11...

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Chapter 11 Choosing and Hiring Candidates Combining Assessment Scores When using more than one assessment method, as is usually the case because the validity of the candidate’s assessment is more valid when using multiple predictors, a candidate’s scores must be meaningfully combined to calculate an overall score that can be compared across candidates or to a minimum hiring standard. (pg. 296) Choosing Candidates Combining Candidates’ Scores (pg. 296) There are two ways of combining candidates assessment scores so that they can be compared with one another (pg. 296) o Multiple hurdles approach (pg. 296) o Compensatory approach (pg. 296) Multiple Hurdles Approach (pg. 296) Multiple hurdles approach : a scoring approach whereby candidates must receive a passing score on an assessment before being allowed to continue on in the selection process. (pg. 296) If candidates lack the physical abilities they need, there is no point in wasting their time as well as the company’s time and resources by continuing to evaluate them. (pg. 296) Costly and take more time due to the need for candidates to make repeated visits for the different assessments. (pg. 297) Generally used when the cost of an employee’s poor performance is high. (pg. 297) The Compensatory Approaches Compensatory approach : an approach whereby high scores on some assessments can compensate for low scores on other assessments. (pg. 297) o This approach is less useful for jobs in which specific talents must exist at a minimum level. (pg. 297) Unit weighting : giving multiple assessments equal weight in computing an overall score. (pg. 297) Rational weighting : experts assign a different subjective weight to each assessment score. (pg. 298)
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Statistical weighting : using a statistical technique, such as multiple regression, to assign a different weight to each assessment score (pg. 298) o Multiple regression : the most scientific approach to determining how to weight each assessment in computing a candidate’s overall score (pg. 298) o The output of a multiple regression includes a formula that looks like: o Overall score = c + (b1 * a1) + (b2 * a2) + (b3 * a3)… o In this formula, c is a constant, the b’s are the statistical weights applied to each assessment method to maximize the validity of the group of assessment methods, and the a’s are a candidate’s scores on each of the assessment methods. Any number of assessment methods can be used. (pg. 298) o For example, if the regression equation for a salesperson looked like this: o Overall score = 24 + (.20 * Cognitive Ability) + (.25 * Interview) + (.15 * Personality) (pg. 298) o And the candidate’s cognitive ability score was 70, interview score was 75, and personality score was 50, then the candidate’s overall score would be 64.25: o 64.25 = 24 + (.2*70) + (.25*75) + (.15*50) (pg. 298) o To be most accurate, the statistical approach requires a sample of several hundred or more hires, a low inter-correlation among assessment methods,
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This note was uploaded on 11/01/2011 for the course MGT 4481 taught by Professor Hunter during the Fall '11 term at Troy.

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Chapter 11 Choosing and Hiring Candidates - Chapter 11...

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