1. Week 10 Slides

2. Assigned Readings Kacmar et al. (2013) Organizational Politics
Kuncel & Sackett (2013) Assessment Centers

3. Utility
Selection ratio: number of people hired / number of people available to hire
Baserate: ratio of people that would be successful compared to the total available.
Either compared to
Those that are hired
Those that are available

4. Cut-scores Relative Cut Scores (Norm-Referenced)
Distributive comps
Known-Groups Method
Fixed Cut Scores (Absolute Cut scores)
Angoff Method
Multiple Hurdles vs. Compensatory
More expensive last

5. Cutoff Scores Predictive Yield Discriminant Analysis Book Mark Method
Takes into account selection ratio and base rate
Discriminant Analysis
Book Mark Method

6. Overall Assessment Cutoffs
Clinical Prediction
Subjective
Unit Weighting
Total score
Rational Weighting
Multiple predictors by a predetermined weight
Multiple regression
Weights are set by a statistical procedure

7. Expectancy data Hit: correct classification (Hit Rate)
Those that do well on this test do well on the job
Miss: incorrect classification (Miss Rate)
False Positive: Those that do well on this test do poorly on the job
False Negative: Those that do poorly on this test do well on the job

11. Content & Face Validity
Do the items match up with the definition?
Do they adequately assessing the testing universe
Is the content validity ratio high enough?
If there is an issue with content validity, what are the implications for the organization in regards to using it?

12. Factorial Validity What is our expected factor structure?
Is the construct homogenous?
Exploratory Factor Analysis
Interpret Scree Plot
Is the reliability estimate high enough?
Is it a problem if the factor structure is not supported?

13. Content Validity Ratio
Judges rate item on scale of importance
Essential, important, not-essential
CVRi = (ne – (N/2))/ (N/2)
CVR = value of the item
n = number of experts saying the item is essential
N = total number of experts

14. Construct Validity Convergent vs. Discriminant Two Ways to assess
Multi-trait Multi-Method matrix
Correlation matrix with theoretically related, unrelated, and criterion-related variables

15. Criterion Validity Two ways
Correlation Table
Multiple Regression Results
R2: How much variance accounted for by predictors
Beta values: How much unique variance accounted for
Is multicollinearity and issue and is the measure redundant with the current battery of tests?

16. Additional Issues Adverse Impact Face Validity and Faking
Determine if it is an issue and what can be done, if anything, about it.
Face Validity and Faking
Is the method and cost of administration feasible for the current organization?