# DECISION-MAKING AND UTILITY by jcu17225

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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 + 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
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
+ 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?
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
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)
UTILITY #2

• COMPARE TWO TESTS
Unew - Uold
• PER SELECTEE
_
U/selectee = T rxy sdy Z – Cp

• HIGH ULTILITY WITH LOW VALIDITY
rxy 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
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

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