MOS 3384 – Chapter 9.docx

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Western University
Management and Organizational Studies
Management and Organizational Studies 3384A/B
Cristin Keller

MOS 3384 – Chapter 9 c pen APPROACHES TO COMBINING PREDICTOR SCORES 1. Subjective vs. Objective Approaches Sbjective predictors are items such as interview scores (in which data was collected through human perception and judgment). Objective information can be found in test scores Four major nethods of collecting and combining information show the range between subjective and objective perspectives: --pure judgmental approach, a decision maker selects and uses predictors based on his or her own judgments based on interview results and his or her intuitive awareness of organizational standards hard to justify in the event of legal or human rights complaints. --trait ratings, decision makers rate job applicants based on judgmental data, such as interviews and application blanks then use formula to calculate score automatic decision (easy score comparison) --Profile interpretation approach: objective data about applicants but judges and interprets the data without statictical tools (different decision makers different interpretations of same results) --Statistical approach: decision makers collect large amounts of data and combine them mechanically to come to a decision “judgmental composite approach t both objective and subjective data an be collected and combined judgementally” Why do more objective approaches generate better results? -Limits on cognitive capacity: even the best decision makers have obvious constraints on their computational capabilities. (bounded rationality: finite cogniticve capacity, environmental contraints, uncertainty of future) No decision maker is aware of all alternatives open to him or her or all the consequences of each alternative -Insensitivity to sample size: jump to conclusions based on small samples -Assigning weights depend on the accuracy of the weights assigned to predictor scores. Difficulty increases as number of predictors and sample size do -Illusory correlation, people tend to overestimate the probability that two events will occur together if they have found that these events oCurred together in the past. Even if they are erroneous theories (eat or walk fast = work hard) -Personal biases: people believe that events they can recall more easily happen more frequently than those thy cannot. Gut feeling=more weight than established criteria decision maker reduces complexity of situation by simplifying reality, limiting factors to consider as well as consequences of them (stop looking at alternatives when workable model is discovered) -Continuous revision of the model : many managers rely on models developed early in their careers and never change them primarily because they know little about new ones 2. Assigning Weights to Predictors Approaches -Multiple regression: predictor scores are regressed against the criterion scores to develop a regression equuationn that predicts the criterion. The regression weights indicate the relative importance of each predictor determining the final score (candidates can arrive at same overall score with different combinations of predictor scores, and scores can balance eachother) most effective when no trade off between predictor scores -Multiple cut-off approach: sets cut off score for every predictor and rejects applicants that score below cut off (deficiency in one predictor cannot be compensated for by another) comprehensible=accepted method acceptable when minimum abilities are essential to job performance -multiple hurdle approach, applican1 must submit to and pass several predictors SEQUENTIALLY and are not permitted to pro:eed until they have satisfactory scores at each level. (minimum level of performance necessary on each predictor, poor performance cannot be compensated) save time and money by eliminating applicants before they perform all tests, can eliminate good applicants best for hires needing long expensive training program combination approach uses a combination of the multiple cut-off and regression approaches to select* job candidates. all are assessed on all predictors ipplicant with any predictor score below the set minimum is rejected. Decision maker then uses the multiple regression approach to calculate the total predicted scores of remaining applicants most costly approach assumes liminal level of performance on each predictor is vital, and a relatively kr low score )n one can be made up by superior performance on the other. -Profile matching: predictoi scores of current, successful employees are used to generate an ideal profile, to which all appli cant scores are compared :hen measures job applicants on these predictors and compares their score profiles 1.Distance approach: computes the differences between the appli cant’s score and the ideal profile on each predictor and then squares and sums the differences 2.Correlation approach: calculating the correlation between applicant predictor scores and the ideal predictor scores. Higher correlation=better match profile matching assumes significantly different profiles exist among successful and unsuccessful employees tech/job demands
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