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Mike Aamodt

Michael Aamodt, Ph.D.

Principal Consultant
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Michael G. Aamodt, Ph.D., an Industrial-Organizational Psychologist, is a Principal Consultant at DCI. He provides consulting services to employers and management law firms on a wide variety of human resource risk management issues, particularly in the areas of compensation analysis, employee selection, and test validation. For 26 years, Dr. Aamodt was a professor of Industrial and Organizational Psychology at Radford University in Virginia, where he taught courses in employee selection, job analysis, employee training and development, organizational psychology, and forensic psychology. He also provides staff services to The Center for Corporate Equality (CCE), a national nonprofit association dedicated to promoting affirmative action, equal employment regulatory compliance and other human resource management strategies to create diverse organizations free from workplace bias.



Dr. Aamodt has published over 50 articles in professional journals and presented over 120 papers at professional conferences. He is also the author of “Industrial/Organizational Psychology: An Applied Approach,” the author of “Research in Law Enforcement Selection”, the coauthor of “Human Relations in Business,” and the coauthor of “Understanding Statistics: A Guide for I/O Psychologists and Human Resource Professionals.” He has extensive editorial experience, having served on the editorial boards of Applied HRM Research, Assessment Council News, Criminal Justice and Behavior, Journal of Business and Psychology, Public Personnel Management, and Journal of Police and Criminal Psychology.



Dr. Aamodt is a past President of the New River Valley SHRM chapter and a member of many professional organizations including SIOP, SHRM, and IPAC. Dr. Aamodt has a Ph.D. and M.A. degree in Psychology from the University of Arkansas. He received his B.A. degree in Psychology at Pepperdine University.

Mike Aamodt ’s Recent Posts

As most of our blog readers are aware, the final FAR rule and DOL guidance was published in the Federal Register on August 25, 2016 implementing President Obama’s issued Executive Order 13673: Fair Pay and Safe Workplaces (FPSW). Although the goal behind this executive order was to “blacklist” organizations with a history of violating labor and employment laws from obtaining new government contracts, it appears, based on the final rule, that the FPSW was designed more to allow OFCCP to coerce current contractors into signing conciliation agreements than it was to prevent actual discrimination.

Perhaps the best example of this motivation is the difference in how the FPSW classifies conciliation agreements as non-arbitral decisions compared to show cause notices (SCNs) which are arbitral decisions: conciliation agreements do not count against a contractor but SCNs do.  Let’s compare two hypothetical contractors. Contractor A has engaged in intentional discrimination against women at five of its establishments.  Rather than go to court, it enters into five separate conciliation agreements with OFCCP, each for $250,000.

Contractor B is accused by OFCCP of pay discrimination against women at two of its AAP establishments. The company vehemently denies the allegation and provides statistical and anecdotal proof that the group differences in pay at both establishments can be explained by variables such as time-in-job and prior related experience. OFCCP refuses to accept the contractor’s analysis and issues an SCN at each establishment. Two years later, OFCCP and Solicitor of Labor’s office determine that the case is without merit and OFCCP closes the review with a closure letter of compliance. Eighteen months after the conciliation agreements and SCNs, both contractors bid for the same contract.  Contractor A, which entered into five separate conciliation agreements, is considered by the contracting officer to have no reportable violations in its record whereas Contractor B, which denies any wrongdoing and was never found in violation, has two reportable violations on its record.

This policy also appears inconsistent with the criminal history guidelines issued by both EEOC and OFCCP.  That is, an applicant’s arrest record (e.g., a contractor’s SCN) should not be used in a hiring decision, but convictions or confessions (e.g., a contractor’s conciliation agreement) can.  The rationale behind not using an arrest record is that an arrest does not account for due process; in other words, it is not a final determination by a neutral judge or jury of guilt or innocence.  An SCN is almost equivalent to an arrest; it does not necessarily mean the contractor has been granted due process with a final determination of a neutral party.

By David Cohen, President; Mike Aamodt, Principal Consultant; and Joanna Colosimo, Associate Principal Consultant at DCI Consulting Group 

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Based on feedback at NILG, we thought our clients may have interest in some background research that we shared in our NILG session: We Might be Paid Differently, But Are Our Jobs Really the Same? A significant portion of the presentation focused on the results of an Equal Pay Act and Title VII case law review. In particular, we presented a summary of court rulings related to what constitutes similarly situated with respect to the work performed, responsibility level, and the skills and qualifications required of a job. (Our readers may recognize this language as the definition of similarly situated as defined by Title VII of the Civil Rights Act of 1964.) The results below may come as a surprise:

  • Employees in different job titles have been found to not be similarly situated. No surprise there.
  • Employees in the same job title have been found to not be similarly situated. Similarly situated cannot be directly inferred from job title. A job title would have to reflect true similarity in work performed, responsibility, skills, and qualifications.
  • Employees in different, but similar, job titles have been found to be similarly situated. As noted above, if job titles do not meaningfully differentiate positions in terms of work performed, responsibility, skills, and qualifications, individuals across titles may be combined. This is the only instance in which individuals from different titles were allowed to be combined in an analysis.

The take home conclusion? Under a Title VII standard, the basis for combining individuals into a group for pay analysis must be similarity in work performed, responsibility, skills, and qualifications. Pay Analysis Groups that contain such dissimilar jobs as accountants and engineers are not likely to be considered by the courts to be considered similarly situated.

By Kayo Sady, Senior Consultant, and Mike Aamodt, Principal Consultant, at DCI Consulting Group

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Today, July 14, the EEOC published an update of the EEO-1 pay data reporting proposal in the Federal Register, announcing a second public comment period.  The second round of public comments are due on August 15, 2016.

Unfortunately, many of the criticisms and concerns raised by the employer community where largely ignored by EEOC.  In a nutshell, EEOC is still proposing that employers submit W-2 compensation data instead of base pay and will be required to submit hours worked for both exempt and non-exempt employees.

Although the EEOC rejected most of the concerns expressed by commenters, the revised proposal does contain one major change and one clarification.

The Major Change: March 31 Deadline

The major change involves the reporting period. One of the major criticisms of the initial proposal was that employers would have to prepare W-2 data twice: once during the normal tax period and again during the EEO-1 reporting period.

In the revised proposal, the filing deadline has been changed to March 31, so that employers can use the W-2 information compiled for tax purposes.  Under the revised proposal, the employer would report the annual W-2 earnings only for those employees employed by the company from October 1 through December 31. The first report would be filed during the 1st quarter of 2018 with a due date of March 31, 2018.

Employers would also provide the total number of hours worked during the year for each employee.

The Clarification: W-2 

The initial proposal did not define the term, “W-2 Earnings.”

The revised proposal clarified that employers will provide earnings from Box-1 of the W-2. The amount reported in Box 1 (Wages, Tips and Other Compensation) is an employee’s “taxable compensation”, not gross wages. Taxable compensation is gross wages (the total amount of earnings on your earnings statement) less those items the IRS considers “non-taxable.”

Things That Remain Unchanged
  • All employers with 100 employees or more would be required to submit an EEO-1 report with Component 2 (compensation data).
  • Reporting by pay bands as proposed in the original proposal remains the same.
  • EEOC continues to propose that “hours worked” be reported on the form.
    • As DCI has indicated previously, this would be required of both exempt and non-exempt employees.
    • EEOC remains committed to periodically publishing results pay disparities by race, sex, industry, occupational groupings, and Metropolitan Statistical Area (MSA).

You can find DCI’s quick analysis of the original proposal here.

Please stay tuned as DCI continues to inform our clients and other stakeholders on the proposals.  Should you want to have your voice heard in the next public comment period, please reach out to your DCI representative.

By Joanna Colosimo, Senior Consultant; Mike Aamodt, Principal Consultant; and David Cohen, President at DCI Consulting Group 

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Medicis Pharmaceutical Corporation, owned by Valeant, will pay $7.2 million to settle a gender discrimination class action claim.  The allegations include disparate compensation and bonuses on the basis of sex, and also include a sexually hostile work environment.  The class includes 225 women in sales or sales managerial positions.

The complaint alleged lower pay for female employees, lower bonuses and stock option, and included 100 claims of sexual harassment.  Additionally, allegations highlighted that male management suppressed women from holding senior and executive level positions.

This case highlights the importance of organizations going beyond base pay when conducting salary equity analyses.

The case is Bonnie Brown v. Medicis Pharmaceutical Corporation, settled in July, 2016.  The complaint and other information about the case can be found here.

By Joanna Colosimo, Senior Consultant, and Mike Aamodt, Principal Consultant, DCI Consulting Group 

 

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In our previous blog on the topic of statistical significance, we discussed how to interpret the meaning of “statistically significant.”  In this blog, we want to expand on the topic by discussing the difference between statistical and practical significance.

As mentioned in the previous blog, when a group difference is statistically significant, it only indicates that it is unlikely, but not impossible, that the difference occurred by chance.  A larger standard deviation is not an indication of the magnitude of the group difference. However, it is an indicator of the probability that the difference observed may not be due to chance.

Because the values of many statistical tests are driven in part by sample size, it is common that a very small difference between two groups is statistically significant, merely because of a large sample size.  For example, a few years ago, we conducted a proactive analysis in which a $36 per year difference in base pay between men and women was statistically significant – partially due to the sample size (3,000 people in the position) and partially due to the low variability in salaries (the difference between the highest and lowest salary was only $2,500).  Conversely, we have seen group differences of $20,000 not be statistically significant due to small sample sizes and high variability.

This is where practical significance comes into play.  Statistical significance allows one to try and interpret a difference, whereas practical significance determines whether the difference is big enough to be of concern.  Using our previous example, a $36 annual difference in salary, although statistically significant, is hardly of a magnitude that one would suspect sex discrimination.

In adverse impact analyses, statistical significance is often determined by the number of standard deviations or a probability level (i.e. Fisher’s exact test), whereas practical significance is determined by effect sizes like impact ratios  and practical swap rules (e.g., if only two more women were hired, the difference would not have been statistically significant).

The moral to our story is that measures of practical significance should always follow any statistically significant finding.

Mike Aamodt, Principal Consultant, and Yevonessa Hall, Consultant at DCI Consulting Group 

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DCI reported on Friday the release of the EEOC’s revision to the Employer Information Report (EEO-1), which was officially published in the Federal Register today, February 1, 2016.  The EEO-1 report will continue to collect the race/ethnicity and sex data by EEO-1 category, but will now add total “W-2” earnings and hours worked by salary range within an EEO-1 category.  A copy of the new form can be found here.  Here is a quick summary of what we know about the EEOC’s Notice:

Who Must Submit?

  • All employers with 100 or more employees must submit the new data.
  • Employers with 50-99 employees would only need to file the current EEO-1 data (race/ethnicity and gender reported by EEO-1 category).  This includes federal contractors.

When Does this Start?

  • Starting in 2017.
  • Must be filed by September 30 each year, which is the current deadline for the EEO-1 filings prior to this addition.  This date coincides with the last day of the government fiscal year.

What is Being Collected?

  • Race/ethnicity and gender of employees by EEO-1 job category (current requirement).
  • This data by 12 salary ranges (e.g. under $19,240; $19,240-$24,439).  These salary ranges are currently used by the BLS in the Occupational Employment Statistics (OES) survey. The new bands are different than the EEO-4 report salary bands.
  • The salary data is described as “W-2” data, although “actual earnings” may be a more appropriate semantic. As a note, the W-2 is a report generated for the IRS and is based on an employee’s actual earnings in a calendar year, including such pay factors as overtime, base salary, any bonuses, or could even include some types of cash benefits such as tuition reimbursement. A W-2 is produced at the end of the calendar year, and does not coincide with the September 30 filing cycle deadline of the EEO-1 reports.  Thus a contractor’s payroll department will need to exert the same effort to generate the EEO-1 report as it does to calculate W-2 earnings for the annual income tax cycle.
  • DCI notes that for gender alone, there would be 240 cells (2 genders * 12 ranges * 10 EEO-1 categories) for an employer to complete on the report. Such a large number of cells will affect the types of analyses that can be used to analyze the data.

What is the Burden Estimate?

  • This is represented just for the companies with more than 100 employees that are required to file the additional salary information.
  • The estimate is that there will be 60,886 respondents with 401,848 reporting hours, equally a total cost of $9,736,767.
  • If we break that down by company, EEOC’s estimated burden is $159.92 per company and 6.6 hours per year.

What is Still Outstanding?

  • EEOC has not yet decided how it will collect the number of hours worked.  Take for example, the Craft Workers category (EEO Category 6) in pay band $19,240-$24,439.
    • Company 1:  10 African American craft workers, 10,000 total hours
    • Company 2:  10 African American craft workers, 20,000 total hours
  • The question becomes: How do you control for hours worked? Because the data are reported in a range and distribution format, this becomes challenging, and the user cannot simply annualize the data.
  • Public comments and a public hearing are required as a part of this process.  Written comments on this notice must be submitted on or before April 1, 2016.

What is the Purpose of the Revised Reporting Data?

  • EEOC and OFCCP will use the data to, “assess complaints of discrimination, focus investigations, and identify employers with existing pay disparities that might warrant further examination.”
  • EEOC and OFCCP plan to develop a software tool that will allow their investigators to conduct an initial analysis by looking at W-2 pay distribution within a single firm or establishment, and compare the firm’s data to the aggregate industry or metropolitan-area data.  As stated in the Federal Register, “This application would highlight statistics of interest.”
  • OFCCP will use the new EEO-1 data, rather than issue a new data collection tool.  OFCCP notes the following on the DOL blog:

This new pay data will allow the EEOC to compile and publish aggregated figures that will help employers in conducting their own analysis of their pay practices to assist in their compliance efforts. The Labor Department’s Office of Federal Compliance Contract Programs, using other data sources, will also make pay data available to the public. These data will also provide job seekers and workers with information on the aggregate pay for job groups across industries, and by gender, race and ethnicity.

Stay tuned on additional information regarding the public hearing and public comments.

By Mike Aamodt, Principal Consultant, and Joanna Colosimo, Senior Consultant, DCI Consulting Group 

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In this multi-part blog series, we will cover various topics relevant to the Federal Contractor community as it relates to statistical analyses. We begin our series with a topic that is often misunderstood by both practitioners and enforcement agencies.

This past year we heard some crazy interpretations regarding the meaning of a statistically significant standard deviation test.  Our favorite was:

“A significant standard deviation means that there is a 95% chance that the difference is due to discrimination.”

As a result of such statements, we thought this might be a good time to briefly remind everyone about the meaning of the term, “statistically significant.” The idea behind statistical significance testing is that anytime you gather a sample of data (e.g., hiring rates for a janitorial position, average salary for accountants) and compare two groups (e.g., men and women), due to chance alone, it is extremely unlikely that the difference between the two groups is going to be exactly zero. For example, for a particular hiring requisition we hired 76% of White applicants but “only” 70% of Hispanic applicants. Is this 6% difference the result of chance or something more sinister?  What would the numbers look like across ten requisitions?

When a difference between two groups is statistically significant (e.g., the difference in selection rates is greater than two standard deviations), it simply means that we don’t think the observed difference is due to chance. The greater the number of standard deviations, the less likely we are to believe the difference is due to chance. Some things to keep in mind:

  • Because a standard deviation test is greatly affected by sample size, the number of standard deviations doesn’t say anything about the size of the group difference.  For example, with 10,000 job applicants, a 1% difference in selection rates (e.g., 90% v. 89%) would exceed two standard deviations; however, a 20% difference with 40 applicants (e.g., 80% v. 60%) would not.
  • A group difference that is flagged as being statistically significant using a standard deviation test may still have occurred by chance. If we ran 100 adverse impact analyses, we would expect five to be statistically significant by chance alone!
  • A statistically significant standard deviation doesn’t imply discrimination: It simply provides some confidence that something might be going on and that we should explore the difference further.

Stay tuned for additional blogs. Also, please read our white paper to learn more about the different statistical significance tests used to analyze data for the purpose of identifying disparate impact (adverse impact) which is different from disparate treatment.

By Mike Aamodt, Principal Consultant and Yesenia Avila, Associate Consultant at DCI Consulting Group 

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11 January 2016

If the recently passed federal FY2016 budget is any indication, Congress is not pleased with the current direction of OFCCP.  The first indication of this displeasure is the actual budget appropriation for OFCCP. OFCCP asked for a $7.2 million increase, both the House ($6 million) and Senate ($10 million) initially recommended substantial cuts to OFCCP’s current budget, and the final result was a $1 million dollar cut.

Although a budget cut of any size sends a message to a federal agency, it is the words used by the appropriations committee members that sent an even stronger message. Although acknowledging the importance of OFCCP’s work, the House appropriations committee stated:

The Committee is concerned that OFCCP has lost its focus on identifying and addressing real discrimination in employment and has become hyper-focused on fulfilling quotas instead of equal opportunity by relying on statistics alone in evaluating contractors. The Committee believes OFCCP should take steps to use common sense in the use of government resources to focus on finding actual discriminatory treatment instead of presumed discrimination based solely on what OFCCP assumes through statistics.

Further, the Committee believes that OFCCP should end its reliance on threatening sanctions, including debarment and the costs associated with an extremely drawn-out administrative litigation process, to induce contractors to waive their legal rights and to enter into conciliation agreements that are not justified by the evidence.

The Senate added:

The Committee is concerned that OFCCP has lost its focus on identifying and addressing real employment discrimination and is imposing excessive compliance burdens on contractors.

The Committee is also concerned about reports that OFCCP is increasingly subjecting contractors to overly broad and unnecessary document and data requests as well as unreasonably numerous and lengthy compliance reviews.

The OFCCP is directed to cease utilization of this de facto quota system for evaluating hiring practices and to report within 120 days of enactment to the Committees on Appropriations of the House of Representatives and Senate on steps it is taking to enforce non-discrimination standards on a more fair, case-by-case basis focused on evidence of actual discrimination rather than on statistical generalizations and quota benchmarks.

It will be interesting to see if OFCCP is responsive to the concerns of Congress.

By Mike Aamodt, Principal Consultant at DCI Consulting Group

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With OFCCP regularly requesting multiple forms of compensation during compliance audits, we revisit the difference between EEO compliance evaluations of base pay versus other compensation metrics. An individual’s base pay is a function of many different compensation decisions spanning multiple years, decision-makers, market characteristics, life circumstances, and career decisions. Effectively modeling such complex phenomena to appropriately evaluate whether EEO issues exist, requires a fairly sophisticated understanding of historical data and regression analyses. Further, the data needed to fully account for legitimate differences in pay are often not available (or, at least, not easily obtained in electronic format). In comparison, such forms of compensation as year-end bonus, spot bonus, and profit sharing are discrete amounts awarded in response to recent individual or company performance levels. Evaluating the EEO compliance of such compensation forms is relatively straightforward, as data availability is not usually a problem and the factors that influence distribution of awards are typically limited. Given the very different analytic scenarios presented by analysis of base pay versus other forms of compensation, we continue to urge our clients to separate compensation types in audit submissions and to communicate clearly the different reasons for awards. Given that “total pay” combines components that are the result of many decisions (i.e., base pay) and components that are the result of a single decision (e.g., annual bonus, spot bonus), caution must be taken when analyzing and interpreting a total pay composite.

By Kayo Sady, Senior Consultant and Mike Aamodt, Principal Consultant at DCI Consulting Group

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Prior to the recently enacted California Fair Pay Act (CFPA), federal and state laws and regulations allowed employers to justify sex differences in salaries by using statistical methodologies to control for legitimate pay factors such as education, experience, and performance. Although the CFPA provides defenses for compensation differences based on seniority systems, merit systems, earnings systems based on quantity/quality of production, and bona fide occupational factors (other than sex), the law directs employers to explain the entire pay differential between men and women. This unprecedented requirement has already resulted in confusion within the employer community.

One of the most common questions we are already receiving from clients is, does the word “entire” actually mean a $0 average difference in salary between men and women?

The standard used by the courts (in cases tried under Title VII of the Civil Rights Act of 1964) has always been that the observed difference in salary must be statistically significant.  That is, the observed difference from $0 is not likely to be due to chance. For example, a contractor finds that there is a $1,500 average difference in salary between male and female administrative assistants and that difference is statistically significant (i.e., t ≥ 2.00).  After running a regression analysis that controls for years of experience and education, the difference drops to $400, which is not statistically significant (i.e., t < 2.00).  Years of case law are clear that the statistically non-significant difference would not be viewed as pay discrimination under Title VII guidelines for pay enforcement.  Would this statistically non-significant difference, however, be viewed by the CFPA as being illegal?  DCI has never seen a situation in which a regression analysis resulted in an average difference of exactly $0.

What about a job title in which there are too few employees to run a regression analysis?  Take, for example, a situation in which a male employee was paid $55,000 and a female employee $53,000. Both have the same amount of experience but the male employee has a master’s degree whereas the female employee only has a bachelor’s degree.  Does the master’s degree explain the “entire” $2,000 difference?  What if the master’s degree was actually valued at $5,000 per year? Does the male employee now have a case for pay discrimination?

We can’t wait for the first test of this concept to go to court – with a non-DCI client, of course.

By Mike Aamodt, Principal Consultant, and Jana Garman, Consultant at DCI Consulting Group 

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Michael Aamodt, Principal Consultant at DCI Consulting Group, wrote an article featured in SIOP’s TIP publication, January 2017.

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EQUAL PAY DAY: BE AWARE AND PROACTIVE!

Today, April 4, 2017, people across the United States will observe a national day to bring awareness around the gender pay gap. This date symbolizes how far into 2017 women must work to earn what men made in 2016, based on national pay averages.

On Equal Pay Day in 2014, President Obama signed an executive order to strengthen pay transparency for federal contractors.

In observance of Equal Pay Day, it is important to be mindful by evaluating compensation systems in organizations.  Conducting a proactive pay equity study to ensure disparities by both sex and race are due to legitimate factors is imperative for organizations.  Also, exploring proactive analytics such as a Shareholder Wage

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