# DECISION-MAKING AND UTILITY METHOD SELECTION OBTAINING ACCEPTANCE.

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DECISION-MAKING AND UTILITY METHOD SELECTION OBTAINING ACCEPTANCE

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

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

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

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

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

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)

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

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

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