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DECISION-MAKING AND UTILITY METHOD SELECTION OBTAINING ACCEPTANCE

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MULTIPLE PREDICTORS ONE PREDICTOR – REGRESSION ^ y = a + b(x) > 1 – MULTIPLE REGRESSION ^ y = a + b 1 (x 1 )+ b 2 (x 2 ) IF a = 2, bx 1 =.4, bx 2 =.7 IF X 1 = 30 and X 2 = 40 y = 2 +.4x 1 +.7x 2 y = 2 +.4(30) +.7(40) = 42 NO THEORY TO GUIDE APPLICATION GUIDES SELECTION

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SELECTION STRATEGIES #1 MULTIPLE REGRESSION MINIMIZES ERROR COMPENSATORY MULTIPLE CUTOFF CUT FOR EACH SET 10 FOR INTERVIEW 25 FOR CA TEST DIFFICULT TO VALIDLY SET

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SELECTION STRATEGIES #2 MULTIPLE HURDLE ADMINISTERED OVER TIME + DON’T ALL TESTS - TIME AND COST DOUBLE STAGE TWO CUT SCORES PROFILE MATCHING PLOT TO AVG SET VALID PRED

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SELECTION OUTCOMES OUTCOMES TRUE POSITIVES (1) FALSE POSITIVES (2) TRUE NEGATIVES (3) FALSE NEGATIVES (4) HOW ACCURATE ARE DECISIONS?

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PROPORTION OF CORRECT DECISIONS 1 + 3 PC TOT ---------------- 1 + 2 + 3 + 4 ALL OUTCOMES EQUAL PROPORTION OF ACCEPTED ARE SATISFACTORY 1 PC ACC ---------------- 1 + 2

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UTILITY #1 INDEX OF FORECASTING EFFICIENCY e = 1 - (1-r xy 2 ) 1/2 COEFFICIENT OF DETERMINATION r xy 2 TAYLOR-RUSSELL TABLES Brogden-Cronbach-Gleser __ U = N T r xy sd y Z - Nt(Cp)

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UTILITY #2 COMPARE TWO TESTS Unew - Uold PER SELECTEE _ U/selectee = T r xy sdy Z – Cp HIGH ULTILITY WITH LOW VALIDITY r xy Z x sdy U/selectee MID LEVEL JOB (systems analyst).20 1.00 $25,000 $5,000 LOW LEVEL JOB (janitor).60 1.00 $2,000 $1,200

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SCHMIDT & HUNTER (1998) PURPOSE EXAMINES 19 MEASURES WITHOUT PRIOR EXPERIENCE META ANALYSIS JOB PERFORMANCE WORK SAMPLE – GMA -STRUC INT TRAINING GMA – INTEGRITY

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MURPHY (1986) DISTINGUISH OFFER & ACCEPTED CASE 1 REJECTED AT RANDOM CASE 2 BEST REJECT CASE 3 NEG r ABILITY/ACCEPT

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