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Chapter 10.docx

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Aaron Schat

Chapter 10: Decision Making - implicit theories: personal beliefs that are help about how people or things function, w/out objective evidence & often w/out conscious awareness - true positive: an employer hires an applicant who turns out to be successful - true negative: the employer did NOT hire an applicant who would have been considered a failure if hired - false positive error: when an applicant who is assessed favourably turns out to be a poor choice - false negative error: when an applicant who is rejected would have been a good choice - pure judgmental approach: judgmental data are combined in a judgmental manner - trait rating approach: judgmental data are combined statically - profile interpretation: statistical data are combined in a judgmental manner - pure statistical approach: data are combined statistically - judgmental composite: judgmental & statistical data are combined in a judgmental manner - statistical composite: judgmental & statistical data are combined statistically o rating scores are given or obtained from each component – interview, reference check, personality test… o ratings/scores are combined in a formula or regression equation to produce an overall score for each applicant o ADV = this model is superior to other methods in predicting performance as all applicant information is taken into consideration in a systematic manner - Incremental validity: the value in terms of increased validity of adding a particular predictor to an existing selection system - Cut-off scores: a threshold; those scoring at or above the cut-off score pass, those scoring below fail - Selection ratio: the proportion of applicants for 1+ positions who are hired Decision-Making Models 1. Multiple regression model: the applicant’s scores on ea. predictor (tests, interviews…) are weighted & summed to yield a total score 2. Multiple cut-off: scores on all predictors are obtained for all applicants & are rejected if their scores on any of the predictors fall below the cut-off scores  non-compensatory = it is NOT possible to compensate for a low score on one predictor w/ a high score on another predictor 3. Multiple-hurdle: applicants must pass the min. cut-off for ea. predictor before being assessed on the next predictor  applicants are slowly ceased/continue from the selection process as they pass/fail each predictor (test, interview etc.) 4. Combination: all applicants are measured on all predictors & those falling below the cut-off on any of the predictors are rejected, THEN the multiple regression is used to calculate the total scores of those applicants who surpass the cut-off scores applicants are ranked by total score & selected on a TOP-DOWN basis 5. Profile matching: current employees who are considered successful on the job are assessed on several predictors, their avg. scores on ea. predictor are used to form an ideal profile of scores, applicants predictor scores then are compared with the ideal profile = those most si
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