2012 Industry Liaison Group National Conference EEO Analysis Bootcamp August 28, 2012 9:00am -...

2012 Industry Liaison Group National Conference

EEO Analysis Bootcamp

August 28, 2012 9:00am - 12:00 p.m.

Drill Instructors: Dan A. Biddle, Ph.D.

Patrick M. Nooren, Ph.D.Biddle Consulting Group, Inc.

Biddle Consulting Group, Inc.193 Blue Ravine, Suite 270Folsom, CA 95630916.294.4250www.biddle.com | www.bcginstitute.org

Dan A. Biddle, Ph.D. ([email protected])Patrick M. Nooren Ph.D. ([email protected])

Copyright © 2012 BCG, Inc. 2

Contact Information

Overview of Biddle Consulting Group, Inc.

Affirmative Action Plan (AAP) Consulting and

Fulfillment

• Thousands of AAPs developed each year• Audit and compliance assistance• AutoAAP™ Enterprise software

HR Assessments

• AutoGOJA™ online job analysis system• TVAP™ test validation & analysis program• CritiCall™ pre-employment testing for 911 operators• OPAC™ pre-employment testing for admin professionals• Video Situational Assessments (General and Nursing)

EEO Litigation Consulting /Expert Witness Services

• Over 200+ cases in EEO/AA (both plaintiff and defense)• Focus on disparate impact/validation cases

Compensation Analysis• Proactive and litigation/enforcement pay equity studies• COMPare™ compensation analysis software

Publications/Books• EEO Insight™: Leading EEO Compliance Journal• Adverse Impact (3rd ed.) / Compensation (1st ed.)

BCG Institute for Workforce Development

• 4,000+ members• Free webinars, EEO resources/tools

Nation-Wide Speaking and Training

• Regular speakers on the national speaking circuit

BCGi Standard Membership (free)Online communityMonthly webinars on EEO compliance topicsEEO Insight Journal (e-copy)

BCGi Platinum Membership (paid)Fully interactive online community Includes validation/compensation analysis booksEEO Tools including validation surveys and AI calculatorEEO Insight Journal (e-copy and hardcopy)Members only webinars, training and much more…

www.BCGinstitute.org

BCG Institute for Workforce Development

We have four (4) primary topics to cover in 90 minutes, each capable of being it’s own full-day of training . . . you did sign-up for a bootcamp!!

5Copyright © 2012 BCG, Inc.

Comparison of Incumbency to Availability: What We Do Look Like Compared to What We “Should” Look Like

Comparison of Selection Rates: How We Got to Where We Are Today

Logistic Regression and Hiring: I Understand There is a Significant Difference in Hiring Rates . . . But It’s Based on Job-Related Practices, Procedures and/or Tests (PPTs)

Multiple Regression and Compensation: I Understand There is a Significant Difference in Pay, But It’s Based on Job-Related Criteria


Agenda


Comparison of Incumbency to Availability (i.e., What We Do Look Like v What We “Should” Look Like)

• Availability analyses are conducted for each location (or FAAP) by “job group”

• Job groups are aggregations of jobs that are similar in “content, wage rate, and opportunity”

• Job groups are used to:― Increase sample size to yield meaningful results

― Reduce the number of analyses conducted

• Job groups should never cross EEO categories

Important Note: Be thoughtful when creating job groups. You could be artificially creating problems!


What We Do Look Like . . .


Job Group Analysis

• What we “should” look like is referred to as the “final availability.”

– It is an estimate of the number of qualified minorities or women available for employment in a given job group

– It’s a combination of both internal and external comparison data (i.e., factors) used to identify what the composition of those qualified to work in the job group is “supposed” to look like

– In the “comparison of incumbency to availability” analysis it will be compared to the job group headcounts to determine the existence of underutilization

What We “Should” Look Like . . .


• External Factor:– Step 1: Define local labor area

– Step 2: Identify/select census occupation codes (472)

– Step 3: Mathematically weight census codes based upon representation within each job group

– Step 4: Identify relevant data other than local (e.g., state/national – if any)

Important Note: Results are only as good as the amount of effort put into this process!





• Internal Factor:– Positions are not always filled via external

sources– Necessary to identify internal sources of

availability information– Step 1: Identify “Feeders” for all jobs/job groups– Step 2: Weight feeders based on historical

promotions data (for starters . . . with a heavy dose of personal review and approval)

Target Job Group Weight Feeder Job Group

1A – Management 75.0 1B – Middle Management (Directors)

25.0 1C – Managers/Supervisors





• Factor Weights:– The weight given to the internal and external

availability data (i.e., factors) for each job group

– Identifies the relative “importance” of each set of data

• Assigning factor weights requires the user ask the following question:– “Out of 100 hypothetical movements into this

job group, what number do I expect to come from a local recruitment area, reasonable recruitment area, or an internal pool?”





External Census Data

Actual Representation (Incumbency/Headcount) Data

Internal Availability Data

Assign Factor Weights

Compare Representation (Incumbency) to Availability

Create Goal/Action-Oriented Program

If Potential Problem Area Exists


Final Availability Data

Comparison of Incumbency to Availability (Overview)

• Regulations require contractors to compare the percentage of minorities and women in each job group with the availability for those job groups determined in the availability analysis

• When the percentage of minorities or women employed in a particular job group is less than would reasonably be expected . . . the contractor must establish a placement goal and create action-oriented programs associated with that goal


Comparison of Incumbency to Availability


Exact Binomial!


Statistical Significance•Least proactive•Legally-oriented•Least goals

Any Difference•Proactive•Diversity-Oriented•Most goals/misleading?

Whole Person•Focus on tangible issues•Good with small orgs/job groups•Balanced

80% Test•Has historical value•Misleading?

Important Note: Identifying underutilization is NOT a declaration of discrimination. Choose a rule that best represents your organizational size/structure and how it views/perceives affirmative action.




Comparison of Selection Rates: How We Got to Where We Are Today


or

the complaining party makes the demonstration described above with respect to an alternate employment practice, and the respondent refuses to adopt such alternative employment practice.


DISPARATE IMPACT An unlawful employment practice based on disparate

impact is established only if:

and

the respondent fails to demonstrate that the challenged practice is job-related for the position in question and consistent with business necessity

A complaining party demonstrates that a respondent uses a particular employment practice that causes an adverse impact

Adverse/Disparate Impact:Legal Overview

Title VII Disparate ImpactDiscrimination Flowchart

PlaintiffBurden

DefenseBurden

PlaintiffBurden

How selection processes are challenged . . .



Does a practice, procedure or test (PPT) results in disproportionate selection rates by gender, race/ethnicity, or age group?

Selection Rate Comparison

26

MenPass (50)

MenFail (50)

Men Passing Rate (50%)

Women Pass (25)

WomenFail (75)

Women Passing

Rate (25%)

•2 X 2 Table Comparison•Impact Ratio Analysis (IRA)•Fisher Exact / Chi-Square / 80% Test

Results in a value indicating if the observed difference in rates is due to chance (i.e., statistically significant).

Note: The chi-square estimates the probability and becomes more accurate as the sample size increases. This is why Fisher’s Exact (adj) is required with small sample sizes. However, BCG recommends Fisher’s Exact (adj) at all times.

Copyright © 2012 BCG, Inc.

Selection Rate Comparison

• Statistical Significance (Thresholds):― 5%― 0.05― 1 chance in 20― 2.0 Standard Deviations (actually 1.96)

• Statistical Significance (Outputs)―Lower p-values=higher SD (or “Z”) values―For example:

• P-value: .05 = 1.96 SDs• P-value: .01 = 2.58 SDs


Statistical Significance

Adverse Impact:The Typical Approach


• Statistical significance: The point at which differences become large enough that one can claim a trend exists.

• Statistical power: The ability to see those trends if, in fact, they do exist.

• Statistical power is directly related to effect size and sample size:― Effect size: The size of the difference in selection rates

between two groups . . . the larger the difference the less number of transactions necessary to detect statistical significance

― Sample size: With larger numbers of transactions it becomes much easier to detect statistical significance


Statistical Significance and Power

• Enforcement agencies have no control over effect size (i.e., the difference in selection rates), but they do have some control over sample size . . . which is why they often request two (2) years worth of data to analyze.

• However, simply aggregating all applicants and all hires across strata (as is typically done), can sometimes result in incorrect/misleading findings.



31

CompanCompanyy

AA

CompanyCompanyCC

CompanyCompanyBB

CompanCompanyy

DD

0.343

0.168

0.088 0.048


• Analyses by AAP job group regardless of different:―Job titles―Selection processes―Hiring managers―Basic qualifications―Locations (perhaps)―Applicant pools for separate requisitions (perhaps)

• Typically an aggregation of 12 months (sometimes 18/24 months) worth of transactions into a single 2x2 table

• Considers everyone who applied throughout the year as available for every hire throughout the year



33

MenPass

MenFail

Women Pass

Women Fail

ALL applicants and ALL hires for a 12-month period

There is nothing wrong with this approach . . . as an initial inquiry only. Sometimes this approach is used as the basis for a Notice of Violation (NOV) or plaintiff class action litigation; however, it is up to the employer to provide rebuttal analyses that may reflect reality more accurately (e.g., accounting for recordkeeping issues, etc.)



Testing Year Group

Applicants

(#)

Selected

(#)Selection Rate (%)

2010 TestMen 400 200 50.0%

Women 100 50 50.0%

2011 TestMen 100 20 20.0%

Women 100 20 20.0%

•Fisher Exact Test: SD = 2.16 (Significant)•Mantel-Haenszel: SD = .024 (NOT

Significant)

Simpson’s Paradox


2010 + 2011 Tests

Combined

Men 500 220 44.0%

Women 200 70 35.0%

Adverse Impact:The Right Way


36

MenPass

Men Fail

Women

Pass

Women Fail

++ ++Req. 1 Req. 2 Req. 3

ALL applicants and ALL hires throughout the time period

= Chi-Square or Fisher’s Exact (adj)


Single Event v. Multiple Events

MenPass

Men Fail

Women

Pass

Women Fail

MenPass

Men Fail

Women

Pass

Women Fail

MenPass

Men Fail

Women

Pass

Women Fail

Mantel-Haenszel (MH) Defined• In the context of selection rate analyses, the MH:

– is a statistical tool that allows researchers to appropriately combine separate and distinct selection processes (e.g., requisitions) into a single analysis

– appropriately allows for the statistical benefits of increased sample size while controlling for Simpson’s Paradox

– is a useful tool for evaluating whether the employer has a “pattern and practice” that is possibly discriminatory

– may (if appropriate) be used to rebut allegations based on single aggregated analyses over time


• The Mantel-Haenszel is not for every situation. It requires separate and distinct pools of applicants/test takers, etc. For example, combining:

o Requisitionso Locationso Different jobs in same job groupo Different hiring seasons and/or groups

• Not applicable for “pooled requisitions” where the requisition stays “open” and applicants are just regularly selected from the pool.


Mantel-Haenszel (MH) Defined

Component “Step” Analyses


Important Note: Enforcement agencies have every right to investigate the practices, procedures, and tests contractors use to screen applicants. However, in the past, due to resource constraints they wouldn’t typically do so unless there was adverse impact in the overall hiring process.Times have changed!

Title VII of 1964/1991 Civil Rights Act

An unlawful employment practice based on disparate impact is established under this title only if a

complaining party demonstrates that a respondent uses a particular employment practice that causes a disparate

impact . . .



Male v. Female

Steps Starting Completing Result

Overall (App vs. Hired)

Male - 100Female - 100


2.81 SD

1. Basic Qualifications

Male - 100Female -100

Male – 79Female - 77

0.25 SD

2. Test Male - 79Female - 77


4.80 SD

3. Interview Male - 65Female - 35


0.18 SD

4. Final Selection



0.00 SD


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Adverse Impact Toolkit™

Logistic Regression and Hiring: I Understand There is a Significant Difference in Hiring Rates . . . But It’s Based on Job-Related Practices, Procedures, or Tests


• The goal is not to instruct how to perform proper logistic regression analyses.

• The goal is to inform you that there is a way to analyze whether applicant differences in job-related criteria are the “real” reason why there is a disparity.

• Particularly useful when/if the OFCCP claims discrimination but you know that there is a legitimate reason for the disparity.


Logistic Regression

• Classic adverse impact analyses can only determine if the numerical difference in passing rates between two groups is significantly different.

• Logistic Regression (LR) can identify if that numerical difference in passing rates is due to applicant differences in job-related criteria (e.g., experience or education).


Logistic Regression


Logistic Regression

• LR needs to be applied to job-related factors that were actually used or considered in the selection process

• LR is useful for weighing the practical importance of job-related factors in the hiring or promotion process

• LR can potentially “pin” the impact on specific job-related criteria

• LR can also be useful for determining “shortfall” calculations

― For example, how many women would have been hired “but for” the possible discrimination?

― What is the total shortfall for women, given what the model can explain?Copyright © 2012 BCG, Inc. 47

Logistic Regression

Gender and Qualification Factors

B S.E. Wald df Sig. Exp(B)

GENDER 0.36 0.45 0.69 1 0.414 1.24

QUALIFICATION 1 0.47 0.36 1.68 1 0.194 1.59

QUALIFICATION 2 0.915 0.68 1.78 1 0.18 2.49QUALIFICATION 3 1.87 0.93 4.04 1 0.04 6.51QUALIFICATION 4 0.21 0.34 0.37 1 0.53 1.23

Constant -5.07 0.76 43.71 1 0.00 0.006

Gender Alone

B S.E. Wald df Sig. Exp(B)

GENDER 1.39 .41 11.61 1 0.0006 4.03

Constant -3.23 0.38 70.26 1 0.00 0.039

• Gender is significant in the absence of any job-related explanatory variables.

• After controlling for gender differences in the job-related variables, gender is no longer significant.

Multiple Regression and Compensation: I Understand There is a Significant Difference in Pay . . . But It’s Based on Job-Related Criteria



Multiple RegressionUsed to create a “model” to determine whether differences in compensation are due to “legitimate job-related factors” or (perhaps) an employee’s gender or ethnicity.

Multiple Linear Regression


Com

pen

sat

ion

Time with Company

Correlation Coefficient (r = .35)

Customer Service Representative

Correlation


Percent of compensation explained by gender (r2 = .35 x .35 = 12.3%)

r = .35

Correlation


The Correlation Coefficient


Com

pen

sat

ion

Time with Company

Regression/PredictionLine

8 years

$55,000

r = .35

Customer Service Representative

Correlation and Multiple Regression

• All variables together become the basis for a prediction “model” known as a regression model.

• The regression model predicts a certain percentage of what makes up an employee’s compensation.

R = .67R2 = 45%


The Regression “Model”

Q: So how does regression help to identify discrimination in pay?

R2 = 45%without gender

R2 = 51%with gender

A: If the prediction model becomes significantly better after including the protected variable.


The Regression “Model”

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COMPare™


It is imperative that practitioners (at least conceptually) “know” these four statistics:

1. Comparison of Incumbency to Availability

― What is availability and how is it calculated?

2. Adverse Impact Analyses

― Typical v. how to do it right

― Component “step” analyses

3. Logistic Regression – Can job-related factors justify significant differences in selection?

4. Multiple Regression – Can job-related factors justify significant differences in compensation?

Summary and Conclusion


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