1. MANA 4328 Dr. George Benson benson@uta.edu
Combining Test Data
MANA 4328
Dr. George Benson 1

2. Selection Decisions First, how to deal with multiple predictors?
Second, how to make a final decision?

3. Interpreting Test Scores
Norm-referenced scores
Test scores are compared to applicants or comparison group.
Raw scores should be converted to Z scores or percentiles
Use “rank ordering”
Criterion-referenced scores
Test scores indicate a degree of competency
NOT compared to other applicants
Typically scored as “qualified” vs. “not qualified”
Use “cut-off scores”

4. Setting Cutoff Scores
Based on the percentage of applicants you need to hire (yield ratio). “Thorndike’s predicted yield”
You need 5 warehouse clerks and expect 50 to apply.
5 / 50 = .10 (10%) means 90% of applicants rejected
Cutoff Score set at 90th percentile
Z score 1 = 84th percentile
Based on a minimum proficiency score
Based on validation study linked to job analysis
Incorporates SEM (validity and reliability)

5. Selection Outcomes PERFORMANCE No Pass Pass PREDICTION Regression Line
Cut Score
PERFORMANCE
90% Percentile
No Pass
Pass
PREDICTION

6. Selection Outcomes PERFORMANCE Type 1 Error False Negative True
Positive
High Performer
Type 2 Error
False
Positive
True
Negative
Low Performer
No Hire
Hire
PREDICTION

7. Selection Outcomes PERFORMANCE High Performer Low Performer
Prediction Line
Cut Score
High Performer
Low Performer
Unqualified
Qualified
PREDICTION

8. Banding Grouping like test scores together Standard Error of Measure
Function of test reliability
Standard Error of Measure
Band of + or – 2 SEM
95% Confidence interval
If the top score on a test is 95 and SEM is 2, then scores between 95 and 91 should be banded together.

9. Selection Outcomes PERFORMANCE Acceptable Unacceptable Unqualified
Prediction Line
Cut Score
Acceptable
Unacceptable
Unqualified
Qualified
PREDICTION

10. Dealing With Multiple Predictors
“Mechanical” techniques superior to judgment
Combine predictors
Compensatory or “test assessment approach”
Judge each independently
Multiple Hurdles / Multiple Cutoff
Hybrid selection systems

11. Compensatory Methods Unit weighting Rational weighting Ranking
P1 + P2 + P3 + P4 = Score
Rational weighting
(.10) P1 + (.30) P2 + (.40) P3 + (.20) P4 = Score
Ranking
RankP1 + RankP2 +RankP3 + RankP4 = Score
Profile Matching
D2 = Σ (P(ideal) – P(applicant))2

12. Combined Selection Model
Selection Stage
Selection Test
Decision
Model
Applicants  Candidates
Application Blank
Minimum Qualification
Hurdle
Candidates  Finalists
Four Ability Tests
Work Sample
Rational Weighting
Finalists 
Offers
Structured Interview
Unit Weighting
Rank Order
Offers 
Hires
Drug Screen
Final Interview

13. Final Selection Top Down Selection (Rank) vs. Cutoff scores
Is the predictor linearly related to performance?
How reliable are the tests?
Top-down method – Rank order
Minimum cutoffs – Passing Scores

14. Final Decision Random Selection Ranking Grouping
Role of Discretion or “Gut Feeling”