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DECISION-MAKING AND UTILITY

DECISION-MAKING AND UTILITY. METHOD SELECTION OBTAINING ACCEPTANCE. MULTIPLE PREDICTORS. ONE PREDICTOR – REGRESSION ^ y = a + b(x)

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DECISION-MAKING AND UTILITY

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  1. DECISION-MAKING AND UTILITY • METHOD SELECTION • OBTAINING ACCEPTANCE

  2. MULTIPLE PREDICTORS • ONE PREDICTOR – REGRESSION^ y = a + b(x) • > 1 – MULTIPLE REGRESSION^y = a + b1(x1)+ b2(x2) IF a = 2, bx1 =.4 , bx2 = .7 IF X1 = 30 and X2 = 40 y = 2 + .4x1 + .7x2 y = 2 + .4(30) + .7(40) = 42 • NO THEORY TO GUIDE • APPLICATION GUIDES SELECTION

  3. 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

  4. 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

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

  6. HOW ACCURATE ARE DECISIONS? PROPORTION OF CORRECT DECISIONS 1 + 3 PCTOT ---------------- 1 + 2 + 3 + 4 ALL OUTCOMES EQUAL PROPORTION OF ACCEPTED ARE SATISFACTORY 1 PCACC ---------------- 1 + 2

  7. UTILITY #1 • INDEX OF FORECASTING EFFICIENCY e = 1 - (1-rxy2)1/2 • COEFFICIENT OF DETERMINATION rxy2 • TAYLOR-RUSSELL TABLES • Brogden-Cronbach-Gleser __ U = N T rxy sdy Z - Nt(Cp)

  8. UTILITY #2 • COMPARE TWO TESTSUnew - Uold • PER SELECTEE _U/selectee = T rxy sdy Z – Cp • HIGH ULTILITY WITH LOW VALIDITYrxy Zx 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

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

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

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