Possible New Approaches to Reduce Adverse Impact
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1 Possible New Approaches to Reduce Adverse Impact Joel P. Wiesen, Ph.D. 31th Annual IPMAAC Conference St. Louis, MO June 11, 2007 Wiesen (2007), IPMAAC Conference 1
2 Adverse Impact Remains Greatest single challenge to testing today 1 standard deviation b-w mean score difference on many cognitive ability tests Much effort to reduce adverse impact Much anguish, modest progress Even well developed tests have adverse impact Wiesen (2007), IPMAAC Conference 2
3 Greatest Challenge to Testing Adverse impact results in: Pressure to abandon testing Pressure to lower standards Legal exposure Wiesen (2007), IPMAAC Conference 3
4 One S.D. Mean Score Difference 1 S.D. difference in mean test score is huge Even.25 S.D. difference drives ranking Wiesen (2007), IPMAAC Conference 4
5 Reasons for Adverse Impact Tests may be the thermometer Many possible reasons/sources educational financial medical See previous IPMAAC presentation (web site) Tests may be a source of adverse impact Wiesen (2007), IPMAAC Conference 5
6 If Tests Are Fair Why strive to reduce adverse impact? Tests are fair No slope or intercept bias No bias in prediction of job performance Wiesen (2007), IPMAAC Conference 6
7 Criteria May Be Biased Consider pay as one universal criterion short people paid less than tall attractive people (male and female) paid more women paid less than men Does pay reflect job performance? If criteria are biased, then tests may be too Wiesen (2007), IPMAAC Conference 7
8 What Can We Do? Maintain the status quo Try new test item types and test areas Difficult to do Will meet with resistance May increase validity Try new ways to use test data Strive to reduce adverse impact Wiesen (2007), IPMAAC Conference 8
9 One New Test Item Type Face Recognition Questions Used for years in police officer exams Room for improvement Wiesen (2007), IPMAAC Conference 9
10 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 10
11 Content Analysis/Critique Measures: Perceptual speed Adam s apple missing in choice B Lips different in choice C Ear different in D Analytical ability or attention to detail Look for small changes in lips, eyes, chin, etc. May become a one item test Wiesen (2007), IPMAAC Conference 11
12 Content Analysis/Critique Unrealistic task Line drawings Cloned (almost identical) faces Same expression for all choices Black and white images Choose one face from a few choices No long-term memory involved Wiesen (2007), IPMAAC Conference 12
13 Content Analysis/Critique Confounded with reading ability Consider these words in the question: represents the face alleged appearance assume no surgery Wiesen (2007), IPMAAC Conference 13
14 Face Recognition Origins Police line-up artists Choose basic face shape Change hairdo, jewelry, hats Use convenient, available technology Mug books Wanted poster approach But missing the side view! Wiesen (2007), IPMAAC Conference 14
15 Possible Improvements Use photos rather than line drawings Choose a face from a large group of faces Use different perspectives/expressions Require aging of person Require long-term memory Wiesen (2007), IPMAAC Conference 15
16 Two Possible New Face Items Use many faces Vary perspectives/expressions Wiesen (2007), IPMAAC Conference 16
17 Question 1 Study the mug shots on page one Identify corresponding people on page two Wiesen (2007), IPMAAC Conference 17
18 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 18
19 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 19
20 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 20
21 Question 2 Study the photos on page one Identify corresponding people on page two Wiesen (2007), IPMAAC Conference 21
22 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 22
23 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 23
24 Test Item Content Omitted Test item images omitted from the public version, for the sake of security. Professionals and jurisdictions may contact the author for copies. Wiesen (2007), IPMAAC Conference 24
25 New Face Items Are Feasible Challenges: High quality photos Printing photos (b&w vs. color) Measure memory not comparison Potential to measure important ability Currently untapped Face memory Wiesen (2007), IPMAAC Conference 25
26 Research Needed Content validity Criterion-related validity Relationship to memory for names Relationship to other cognitive abilities Wiesen (2007), IPMAAC Conference 26
27 Call for Research Partners Offer use of items in exchange for data Wiesen (2007), IPMAAC Conference 27
28 What Can We Do? Maintain the status quo Try new test item types and test areas Try new ways to use test data Strive to reduce adverse impact Wiesen (2007), IPMAAC Conference 28
29 One New Way to Use Test Data Greatest Strength Model (GSM) Use a battery of tests Screen on all tests Rank on highest test Wiesen (2007), IPMAAC Conference 29
30 Team Orientation Team members not all equal Some can rebound (e.g., Marcus Camby) Some can score, but have some weaknesses Build on strengths Compensate for weaknesses Wiesen (2007), IPMAAC Conference 30
31 Greatest Strength Model Step 1. Give several tests Step 2. Put tests on common metric Step 3. Determine highest score Step 4. Fail candidates with any low score Step 5. Rank candidates based on their highest scores Wiesen (2007), IPMAAC Conference 31
32 Firefighter Example Written (M/C) test of cognitive ability Structured oral interview Physical performance test (PPT) Wiesen (2007), IPMAAC Conference 32
33 Greatest Strength Model: Firefighter Example Person Written Oral PPT Highest Grade A B C Wiesen (2007), IPMAAC Conference 33
34 Evaluating the GSM Adverse Impact Validity Wiesen (2007), IPMAAC Conference 34
35 Simulation Study Create imaginary applicants Create test/job data with known correlations Evaluate data two ways: - Greatest Strength Model (GSM) - Conventional uses of test data Wiesen (2007), IPMAAC Conference 35
36 Simulation Study Methodology Specify intercorrelations Generate data with these intercorrelations Create gender and EEO groups Create mean score differences Evaluate adverse impact in appointments Evaluate validity Wiesen (2007), IPMAAC Conference 36
37 Firefighter Example Written (M/C) test of cognitive ability Structured oral interview Physical performance test (PPT) Wiesen (2007), IPMAAC Conference 37
38 Specify Intercorrelations Oral PPT Gender EEO Gp. Job Perf. M/C Cog Oral PPT Gender 0 0 EEO Gp. 0 Key: PPT stands for Physical Performance Test Wiesen (2007), IPMAAC Conference 38
39 Create Mean Score Differences Gender: 1.25 s.d. on PPT EEO Group: 1 s.d. on written cognitive test EEO Group:.5 s.d. on job performance Wiesen (2007), IPMAAC Conference 39
40 Adverse Impact in Appointments Will vary by selection ratio Lower selection ratios yield higher impact Assume we hire top 20% of applicants - Realistic for some jurisdictions Wiesen (2007), IPMAAC Conference 40
41 Adverse Impact in Appointments M/C Cog Only EEO Gender Note: Based on 50,000 cases, hiring top 20%. Wiesen (2007), IPMAAC Conference 41
42 Adverse Impact in Appointments M/C Cog Only EEO Gender Composite EEO Gender Note: Based on 50,000 cases, hiring top 20% Wiesen (2007), IPMAAC Conference 42
43 Adverse Impact in Appointments M/C Cog Only EEO Gender Composite EEO Gender GSM EEO Gender Note: Based on 50,000 cases, hiring top 20%. Key: GSM stands for Greatest Strength Model. Wiesen (2007), IPMAAC Conference 43
44 Evaluate Adverse Impact Much lower adverse impact with GSM Potential for even greater reduction Use more tests (e.g., work-style) Wiesen (2007), IPMAAC Conference 44
45 Evaluate Validity We have job performance data! We have GSM grade We can calculate a composite score based on M/C cognitive, oral, and PPT Can compute criterion-related validity Wiesen (2007), IPMAAC Conference 45
46 Evaluate Validity M/C Cog Only.41 Note: Based on 50,000 cases, hiring top 20% Wiesen (2007), IPMAAC Conference 46
47 Evaluate Validity M/C Cog Only.41 GSM.45 Note: Based on 50,000 cases, hiring top 20% Key: GSM stands for Greatest Strength Model. Wiesen (2007), IPMAAC Conference 47
48 Evaluate Validity M/C Cog Only.41 Composite.56 GSM.45 Note: Based on 50,000 cases, hiring top 20% Key: GSM stands for Greatest Strength Model. Wiesen (2007), IPMAAC Conference 48
49 Evaluate Validity Higher validity for GSM than M/C cognitive M/C cognitive was the standard for generations Wiesen (2007), IPMAAC Conference 49
50 Greatest Challenge to Testing Adverse impact results in: Pressure to abandon testing Pressure to lower standards Legal exposure So reduce adverse impact! Innovative approaches show great promise Wiesen (2007), IPMAAC Conference 50
51 What Can We Do? Try new test item types and test areas Try new ways to use test data Strive to reduce adverse impact Wiesen (2007), IPMAAC Conference 51
52 Summary New item types: Feasible May increase validity New ways of using data: Feasible Will reduce adverse impact Will maintain validity Wiesen (2007), IPMAAC Conference 52
53 Final Thoughts Our field is still young Continue to look for new approaches Call for research partners Use of items in exchange for data Copies of this presentation are available at: Wiesen (2007), IPMAAC Conference 53
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