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Samantha Holland

Samantha Holland, Ph.D.

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Samantha Holland is a Consultant at DCI Consulting Group whose work centers primarily on employee selection validation and consulting on best practices. Samantha’s areas of expertise include test development and validation, job analysis, and quantitative methods related to employee selection and retention.

Samantha received her M.A. and Ph.D. in Industrial/Organizational Psychology at George Mason University. She earned her Bachelors of Arts degrees in Mathematical Methods in the Social Sciences and Psychology from Northwestern University. During her graduate studies, Samantha led scale development efforts and presented research on best practices for moderated mediation model justification and analysis. She also gained applied experience in areas of training, competency modeling development and validation, and test development via internships at several D.C.-based consulting and non-profit organizations while earning her graduate degrees.

Samantha Holland ’s Recent Posts

On October 13, 2016, a wide variety of experts, ranging from data scientists to lawyers, testified at the EEOC to highlight the implications of big data for the American workplace. Big data, as defined by the EEOC press release, includes “the use of algorithms, ’data scraping’ of the internet, and other means of evaluating tens of thousands of pieces of information about an individual” (EEOC 10/13/16 press release) to inform employer decisions.  Topics spanned areas such as data privacy, the effectiveness of big data algorithms, and possible EEO implications. DCI’s Eric Dunleavy, Director of Personnel Selection and Litigation Support Services, testified on behalf of the Society for Human Resource Management (SHRM) to describe the legal and ethical issues associated with big data.

Dr. Dunleavy set the stage by defining big data and providing examples of how they can be used to answer key questions from employers. After presenting possible implementations of big data, he summarized how they are currently used. He then closed by noting that even though big data offer predictive potential, there are various EEO-related questions that have yet to be thoroughly considered. For example, is it possible that factors in algorithms could be proxies for protected group membership?

Here at DCI, we have recently seen an increase in the number of big data questions from clients, particularly related to employee selection and recruitment. Tricky issues we have thought hard on include:

  • How to validate or defend algorithms that change overtime
  • Whether black box algorithms can ever be defensible if it is unclear what is being measured
  • What implications big data present for the internet applicant rule

DCI is working on a big data focused session  in December to cover these issues and more, so stay tuned!

By Sam Holland, Consultant, and Cliff Haimann, Consultant, at DCI Consulting Group

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In an earlier blog  in this alert, Art Gutman summarized the April 21, 2016  Department of Labor Administrative Review Board (ARB) ruling related to a long-standing set of OFCCP allegations against Bank of America (BOA). This ruling serves as a warning to the Office of Federal Contract Compliance Programs (OFCCP) and other federal agencies to rely on more than just statistical significance when pursuing employment discrimination claims.

This ruling trumps the Administrative Law Judge (ALJ) ruling back in 2010 that supported the use of statistics demonstrating a disparity of two or more standard deviations as enough to establish a prima facie case of unlawful discrimination (see our previous blog for more details on that issue). In 2010, the ALJ found BOA in violation based on statistical evidence during the time periods of 1993 and 2002-2005. Why did they rule differently for the 2002-2005 period this time around?

Statistical versus practical significance

A key reason for the 2002-2005 period ruling of insufficient evidence is due to the extremely small shortfalls. Shortfalls give the number of expected hires for the disadvantaged group if selection rates across all groups were equal. The larger the shortfall, the more practical impact a difference in selection rates has. The ALJ noted “in 2003, 44 African Americans were offered a job instead of the expected number of 47.9; in 2005, 32 instead of 34.5. Those are small shortfalls.” In other words, even though the OFCCP found a statistically significant difference, the actual discrepancy in terms of hires was less than five people in a given year. Our takeaway from this is a precedent is set for courts to not rely on statistical significance in isolation: practical significance is another key piece of evidence.

By Yesenia Avila, Associate Consultant, and Samantha Holland, Consultant, DCI Consulting Group 
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The 31st Annual Conference for the Society of Industrial and Organizational Psychology (SIOP) was held April 14-16, 2016 in Anaheim, California. This conference brings together members of the I/O community, both practitioners and academics, to discuss areas of research and practice and share information. Many sessions cover topics of interest to the federal contractor community, including employment law, testing, diversity and inclusion, big data, and regulations for individuals with a disability. DCI Consulting Group staff members were well represented in a number of high profile SIOP presentations and also attended a variety of other sessions worth sharing. Notable session summaries and highlights can be found below.

 

Beyond Frequentist Paradigms in Legal Scenarios: Consideration of Bayesian Approaches

High-stakes employment scenarios with legal ramifications historically rely on a frequentist statistical approach that assesses the likelihood of the data assuming a certain state of affairs in the population. This, however, is not the same as the question that is usually of interest, which is to assess the likelihood of a certain state of affairs in the population given the data. This session explored the use of a Bayesian statistical approach, which answers the latter question, across different high-stakes employment scenarios. In each of the presented studies, data were simulated and analyzed, and results between the Bayesian and frequentist approaches compared:

  • David F. Dubin, Ph.D., and Anthony S. Boyce, Ph.D., illustrated the application of Bayesian statistics for identifying selection test cheaters and fakers.
  • Chester Hanvey, Ph.D., applied a Bayesian approach for establishing whether jobs are correctly classified as exempt in wage and hour questions.
  • Kayo Sady, Ph.D., and Samantha Holland, Ph.D., demonstrated the advantages of a Bayesian analysis in compensation scenarios with difficult-to-detect subgroup differences.

In each of the studies, the results suggested the utility of a Bayesian analysis in some specific circumstances. Overall, the presenters agreed that the Bayesian analysis should supplement more traditional frequentist analyses and noted specific issues to consider when designing these analyses. Given the lack of legal precedent and difficulties introducing a new set of statistical interpretations into the courtroom, the takeaway was that the best current value-add for Bayesian approaches is in proactive, non-litigation applications.

 

Contemporary Issues in Occupational Credentialing

The opportunity for credentialing or micro-credentialing is ever increasing, with credentials popping up in many professional fields that previously had none. What it takes to develop and maintain these credentialing exams, however, is something that many people know little about. In this session led by Samantha Holland (DCI), panelists from both private and public sector credentialing programs shared their experiences with issues such as maintaining test security, developing test content, and establishing validation evidence for their exams. Some highlights are noted below:

  • John Weiner, from PSI, noted the many security aspects to consider when administering exams online, a situation that requires additional measures beyond those described by other panelists.
  • Rebecca Fraser, from the Office of Personnel Management, shared her experience using methods beyond practice analysis to establish the content domain for specialized, low sample size domains.
  • Lorin Mueller, from the Federation of State Board of Physical Therapists (FSBPT), discussed the need for clearer boundaries when it comes to regulation of certification boards: the line between what is good for a profession, and what is good for business, can sometimes become blurred.
  • Alex Alonso, from the Society of Human Resource Management (SHRM), provided his experiences of building a certification program from the ground up for his organization’s newly minted HR certification program.

 

A View from the Trenches: EEOC/OFCCP Practitioner Update

DCI’s Joanna Colosimo moderated this panel, featuring DCI’s Mike Aamodt, Michelle Duncan of Jackson Lewis, Eyal Grauer of Starbucks, and David Schmidt of DDI, providing an update on recent regulatory changes, enforcement trends, and other topics related to compliance.

In fiscal year 2015, the OFCCP completed fewer compliance evaluations, but the duration of audits has increased as a result of the revised scheduling letter and more in-depth follow-up requests, particularly related to compensation. The panel also discussed the increase in steering allegations and settlements where whites and/or males were the alleged victims of systemic hiring discrimination.

Dr. Aamodt spoke about two hot topics: the EEOC’s proposed pay data collection tool and the use of criminal background checks for employment decisions. With regard to the EEO-1 pay data collection tool, he highlighted the burden of reporting pay data for 10 EEO-1 categories, 12 pay bands, 7 race/ethnicity categories, and 2 sex categories, as well as some of the limitations of using W-2 data. Additionally, he discussed how difficult it would be for the EEOC to use the resulting data to identify pay issues. For employers using criminal background checks, Dr. Aamodt recommended that contractors adopt narrowly-tailored policies that consider the nature of the offense, the duration of time since the offense, and the nature of the job being sought.

 

Strategically Evaluating Outreach for Individuals with Disabilities and Veterans

This session presented research conducted by DCI’s Kristen Pryor, Rachel Gabbard, and Joanna Colosimo to investigate best practices amongst federal contractors in complying with the 503-VEVRAA formal evaluation of outreach and recruitment obligations. Representatives from 77 federal contractor organizations provided survey feedback on current methods and prospective strategies for evaluation. Results identified strategies such as tracking resource specific metrics on qualified referrals and hires as well as ROI analysis for evaluating the success of outreach efforts. Results also suggest general frustration among federal contractors due to insufficient and ambiguous regulatory guidance on this requirement. The full white paper is available here. In addition, DCI will be conducting follow-up research in the near future to determine if further progress has been made in this area, now that the regulations have been in effect for over two years.

 

No Longer an Afterthought? Reasonable Alternatives and Title VII Litigation

DCI’s Emilee Tison moderated this session where panelists discussed their perspectives and experiences related to identifying and evaluating reasonable alternatives. Panelists included Winfred Arthur, Jr (Texas A&M Univ.), Theodore Hayes (FBI), James Kuthy (Biddle Consulting Group, Inc.), and Ryan O’Leary (PDRI, a CEB Company).

Discussion topics included:

  • The Uniform Guidelines text related to the “reasonable effort” in identifying alternatives with “equal” validity and “lesser” adverse impact
  • Strategies for identifying and considering alternatives, including the impact this will have on two selection goals: validity and diversity
  • The potential impact of recent case law on discussions of reasonable alternatives
    • Lopez v. City of Lawrence, 2014
    • Johnson v. City of Memphis, 2014
    • Documenting a consideration of alternative selection procedures

Panelists ended the session with a few parting words, including:

  • Clearly identify what you are considering an alternative
    • Note that not all alternatives are created equally
    • Put in the effort to identify and document your search for alternatives
    • When documenting alternatives, steer clear of ‘stock language’ by providing justification for your choice(s)

 

Competencies and Content Expertise for I/O Psychology Expert Witnesses

In light of recent developments in case law and updated regulatory guidance, panelists provided competencies and strategies for expert witness testimony, focusing on three main topics: social framework analysis (SFA), new measures for test validation, and wage and hour concerns related to revised FLSA regulations on exempt status employees. Panelists included DCI’s Eric Dunleavy and Arthur Gutman, in addition to Margaret Stockdale of IUPUI, Cristina Banks of Lamorinda Consulting, Caren Goldberg of Bowie State University, and David Ross of Seyfath Shaw.

The goal of SFA as it relates to expert witnesses is to educate the court and jury on the processes underlying cognitive bias and other socially constructed concepts like gender inequality. Panelists cited the 2011 Supreme Court case of Walmart v. Dukes as a prime example of applying SFA methodology to diagnose discrimination in personnel practices. Although SFA has been met with some criticism, it can be said that there is a certain degree of subjectivity in many employment processes that have the potential to lead to discrimination. For this reason, experts are encouraged to look at seemingly neutral factors that may have a disproportionate impact on members of a protected group.

Shifting focus to standards regarding test validation, panelists commented on the outdated nature of the Uniform Guidelines on Employee Selection Procedures (UGESP), which have not been updated in nearly 40 years.  Although the panel was not aware of any initiatives to update the guidelines, it was noted that several SIOP representatives have met with the Equal Employment Opportunity Commission (EEOC) regarding the guidelines and other topics of mutual interest. Panelists also advised the audience to rely on both the SIOP Principles and APA Standards as supplemental, more contemporary resources regarding test validation standards. Additionally, SIOP will be publishing a white paper on minimum qualifications and adverse impact analyses that addresses data aggregation concerns and other testing considerations.

The final topic discussed focused on wage and hour issues concerning the revised FLSA regulations. The panel discussed the difficulties that many employers face in accurately classifying jobs as exempt or non-exempt, and also when determining whether independent contractors should be considered employees. It was recommended that job analyses be done for individual positions, rather than general ones, to help determine exempt status and how much time is spent doing each type of work. Employers should also be aware of any differences regarding state law.

 

Opening the “Black Box”: Legal Implications of Big Data Algorithms

The subject of “big data” has become a hot topic as access to increasingly large amounts of data provides employers with new opportunities to make informed decisions related to recruitment, selection, retention, and other personnel decisions. However, “data scientists” often overlook the legal implications of using big data algorithms within an employment context, especially when it comes to employee selection. Panelists discussed several issues emerging from the use of big data algorithms, including the potential for discrimination, Title VII consequences, and strategies for mitigating risk.

As suggested by DCI’s Eric Dunleavy, many of the “big data” models really do not differ from empirically keyed biodata, which is not a new concept. What is new are methods of collecting larger amounts of data from new sources. Like empirically keyed biodata, big data can be very effective in predicting work-related outcomes. However, if the employer cannot explain how the algorithm works or illustrate that it is job-related, it may be difficult to justify use of the algorithm if facing a legal challenge.

In addition to traditional adverse impact concerns related to women and minorities, some big data techniques may have the potential to discriminate against other protected groups. For example, one panelist mentioned a computer program that can automatically score an applicant’s body movements and analyze vocal attributes from a video recording of an interview. Several other panelists noted that certain body movements or vocal attributes may be related to protected class status, in particular individuals with disabilities. The main takeaway here is that if an employer is using data algorithms, it is imperative that they not only validate the model, but also understand how it is making decisions.

 

Big Data Analytics and Employment Decisions: Opportunities and Challenges

In this session, speakers highlighted the increasing popularity of the use of big data techniques (e.g., machine learning) within organizations to predict work outcomes , pointing out both benefits and challenges inherent to these approaches.

As one example of a big data “win”, Facebook’s David Morgan described how data collected on the current workforce can be used to identify employees at risk of turnover. More caution is required, however, when using big data to inform selection decisions. Many big data algorithms are essentially “black boxes”: data goes in and results come out with little transparency of the how or the why. Not being able to explain the “why” makes these approaches very difficult to defend in court. Rich Tonowski, representing the EEOC, advised that companies be knowledgeable and comfortable with the process being used as the agency will obtain access to the algorithm. Similarly, companies should be able to explain how the information being used is job-related, especially when data have been mined from social media or other Internet sources.

A final caveat was that machine learning tools may use data that is correlated with protected-class status in some way.  Dave Schmitt of DDI suggested one way to test for this is to determine if the model can predict the race or sex of applicants. If so, then it may be subterfuge for discrimination. This may be especially compounded by the “digital divide,” where minorities may be less likely to have regular access to the Internet due to lower socio-economic status.

 

Applied Criterion-Related Validation Challenges: What We Weren’t Taught in Textbooks

This panel, which included DCI’s Art Gutman, discussed a variety of challenges faced when working to conduct criterion-related validation studies for client organizations. Challenges included study design issues, data collection problems, determinations regarding appropriate analysis, and meeting reporting requirements. Specifically, presenters discussed the criteria problem (obtaining appropriate and accurate measures of job performance), problems with predicting low base rate events, issues of range restriction and the appropriateness of applying corrections, among others. The panelists hypothesized that upcoming issues in criterion validation will include dealing with big data (“messy predictors”), processes for validating non-psychometric assessments, addressing validity equivalence (or lack thereof) in multi-platform or mobile assessments, and the eventuality of court cases evaluating validity generalization.

 

Implications of Revisions to FLSA Exemptions for Organizations and Employees

In this session, a panel of experts provided insights on the proposed changes to the FLSA exemption criteria.  The panel discussed the salary test for exemption, which would increase from $455 a week to the 40th percentile of weekly earnings for full-time salaried workers (estimated at $970 for 2016) and the implied potential changes to the job duties test. Regarding the salary test, panelists agreed that a change is overdue. However, they argued that a phased approach would be more appropriate and that the regulation should not be set at a dollar value, but instead aligned to a value that will allow it to stay in line with inflation. The NPRM’s discussion of the job duties test did not propose a change, but asked for feedback on whether a quantitative threshold, like the 50% “primarily engaged” test in California, should be implemented. The DOL estimated that approximately 20% of exempt employees would be impacted by the salary changes alone. Implications for employers are staggering, especially in light of the potential for a 60 day implementation window. First, employers must assess the extent to which they are comfortable with their exempt/nonexempt classifications and reasoning and plan to re-evaluate where needed. Second, budgeting and cost scenarios for moving exempt positions to non-exempt, realigning duties, or increasing pay should be evaluated. Finally, internal messaging and communication plans should be in place to outline the changes, reasoning, and any new procedures.

 

Novel Approaches for Enhancing Diversity Training Effectiveness in the Workplace

In this session, four different presenters provided insights on diversity training. Three presented information from academic research, and one presenter provided information from an organization context. A full 67% of organizations provide some form of diversity training, though research into the impact of that training on the job is varied. One series of studies found that individuals who are high in social dominance orientation (e.g., high preference for hierarchy in a social system and dominance over lower-status groups) tend to be more resistant to diversity training, but that this resistance can be mitigated when the training is endorsed by an executive leader. Another series of studies found that men are more likely to place importance on gender issues addressed when those issues are put forth by other men, and that this holds in both written context and in-person contexts. A Google employee presented on the training Google has implemented as part of new hire on-boarding on implicit or unconscious biases. The training focuses first on increasing awareness and understanding of the topic, to provide a common language, and initial suggestions for mitigation. Follow-up training has focused more on role playing type scenarios to cement the behavior change and mitigation aspect, increasing employee comfort level with calling out biases when and where they are observed.

 

Why Survey Data Fail – and What to Do About it

Panelists discussed their experiences conducting surveys, times when things went wrong, and recommendations for a successful survey. Anyone can use and develop a survey, but issues can arise when multiple stakeholders are involved, each with a different opinion. For this reason, it is important to communicate the purpose of the survey and how the results will be used. Branding can be beneficial to help develop awareness, generate interest, and increase participation. Positive changes implemented based on survey results can also lead to increased participation the following year. Additionally, it is important to research any null or opposite findings between survey iterations to give you a better understanding of any issues that may be present within your organization.

Panelists also addressed problems they have encountered when implementing results, including trying to do too much with the findings, or slicing the data so many ways that your results become less reliable. It was also emphasized that results should be presented in a way that leaves little room for subjective interpretation to avoid making conclusions that are not supported by the data.

Finally, the panel provided a few recommendations for a successful survey:

  • Make responding easy
  • Get people excited about data by telling a good story
  • Provide insights and summaries when reporting results
  • Make an effort to understand your audience in order to keep participants engaged year after year

 

Can Technology Like Deep Learning Eliminate Adverse Impact Forever?

This debate-style session posed the question of whether or not big data techniques (specifically deep learning or machine learning) could/should be used to eliminate adverse impact during selection. The panel included data scientists and I/O psychologists to present their perspectives. The I/O psychologists opposing this technique – including DCI’s Emilee Tison – presented the following high-level points:

  • The identification of adverse impact alone is not synonymous with illegal discrimination
    • The blind elimination of it may eliminate meaningful differences that exist due to legitimate job-related factors – impacting the validity of the selection procedure
    • Adverse impact is the prima facie standard for a disparate impact case; however, procedures that produce adverse impact have two additional considerations:
      • The job relatedness or business necessity of the procedure
      • The consideration of reasonable alternatives
  • Making selection decisions based on protected class status is illegal according to the CRA 91 and, as supported in recent case law, selection decisions should not be based on adverse impact alone (Ricci v. DeStefano, 2009)
  • Data scraping techniques – that learn and pull in factors to use in predicting important outcomes (such as information from Facebook) – call into question the job-relatedness of the selection procedure

In summary, the panelists came from very different perspectives and foundational knowledge bases; however, it was the start of what hopefully becomes meaningful cross-discipline dialogue.

 

 

By: Kayo Sady, Senior Consultant; Samantha Holland, Consultant; Brittany Dian, Associate Consultant; Dave Sharrer, Consultant; Kristen Pryor, Consultant; Rachel Gabbard, Associate Consultant; Joanna Colosimo, Senior Consultant; Emilee Tison, Senior Consultant; and Bryce Hansell, Associate Consultant at DCI Consulting Group 

 

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Most scientific studies include people talking about “statistically significant” results that support whatever effect they are examining, typically mentioning p-values less than .05 as sufficient evidence. This is true in adverse impact analyses as well. A previous blog in the statistical significance series outlines what the term means, and mentions an issue that we will focus on in this part of the series: identifying when the principles of chance may be at play.

In the most commonly used statistical models, p-values indicate the likelihood that a set of observed data is compatible with the absence of whatever effect is specified in a given hypothesis (for example, that mean differences exist between protected classes ).  This is called null hypothesis testing. So, a p-value of .05 indicates a 5% chance that the data is compatible with the null hypothesis (i.e., no mean differences between protected classes), and a 95% chance that the data is incompatible with the null hypothesis (i.e., there are  mean differences between protected classes).

Those are pretty good odds, and we typically take them: when p<.05, we conclude the observed pattern of data is “significant” evidence against the null hypothesis, and, therefore, infer support for the hypothesized effect.  In other words, this “significant” evidence does not tell us why  there are differences, only that we are fairly confident that differences exist based on the data.

Things get trickier when running multiple statistical tests on your data. The 5% margin of error on each test (i.e., using a p-value of .05) means that if you were to run 100 analyses, you’d expect to incorrectly conclude, based on the data, that the hypothesized effect exists about five times. In other words, some results may be false alarms (i.e., the pattern in the data may seem to be related to the hypothesized effect but is in fact due to random variation).  If you’re only running a few adverse impact analyses, this possibility is generally ignored. It’s a different story, however, when running proactive AAP analyses, where the number of individual analyses can easily run into the thousands!

For example, assume a company has 100 AAPs and 30 job groups. Running male/female adverse impact analyses on applicants could be up to 3,000 analyses. By the time analyses are also run on promotions and terminations, we are up to 15,000 analyses. Statistically speaking, we’d expect to see around 150 applicant analyses (3,000 x 5%) and 750 overall analyses (15,000 x 5%) with potentially false positive results at a p-value of .05. A recent statement from the American Statistical Association (ASA) cautions that when multiple analyses are run, only reporting p-values that surpass a significance threshold, without reporting the number, types, and hypotheses behind all statistical analyses run, makes those “reported p-values essentially uninterpretable.” So, how do we know if similar results are real disparities, or just due to luck of the draw?

One option, and the way OFCCP previously recommended in a 1979 Technical Advisory Committee (TAC) manual, is by applying a statistical correction that accounts for the Type I error rate (i.e., false alarms) when running repeated analyses. The Bonferroni correction is one of the most widely used, though there are others as well. These essentially work by adjusting the required significance level based on the total number of analyses run. For example, if 20 tests are being run with a p-value of 0.05, applying the Bonferroni correction means that significance would only be asserted when the p-value is less than or equal to 0.0025.

The field has yet to converge on clear guidance as to which correction is best or when they must be used. However, knowing what “statistically significant” actually means coupled with the practical realities of running hundreds or thousands of tests will put you in a good position to ask the right questions when working through proactive AAPs.

By Kristen Pryor, Consultant, and Sam Holland, Consultant at DCI Consulting Group 

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OFCCP has released a new voluntary poster: Opening Doors of Opportunity for ALL Workers. The poster emphasizes the agency’s goals of diversity and equal opportunity, as well as expectations that federal contractors “must treat workers fairly and without discrimination” and “pay all workers fairly.”

Although the intention of this poster may be good and bring awareness to OFCCP’s mission, the use of fairness language may be misleading in comparison to actual regulatory requirements. For us here at DCI and other experts in selection and compensation practices, fairness could mean a wide variety of things depending on context. For example, fairness may speak to people’s perceptions of decisions, or processes used to arrive at decisions, in terms of how just they seem. This may be independent of the actual objective equal treatment of different individuals. Importantly, OFCCP’s regulations only require the latter, not the former.

Though this may seem like a matter of semantics, the distinction between fairness and non-discrimination is important to keep in mind. While what constitutes discrimination is very well-defined within the body of OFCCP regulations, finding a way to ensure the perception of fairness would be very difficult indeed.

By Samantha Holland, Consultant and Kristen Pryor, Consultant at DCI Consulting Group

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The new California Fair Pay Act (CFPA) is summarized in depth in a previous blog in this series. The CFPA includes strict stipulations for employing “[a] bona fide factor other than sex, such as education, training, or experience” to explain wage differentials. If we look at the key components of the requirements, excerpted below, we see the CFPA is really just calling for a validation process for compensation factors.

“This factor shall apply only if the employer demonstrates that the factor is not based on or derived from a sex-based differential in compensation, is job related with respect to the position in question, and is consistent with a business necessity… This defense shall not apply if the employee demonstrates that an alternative business practice exists that would serve the same business purpose without producing the wage differential.”

Demonstrating validity for a selection procedure is a core tenet of defensible employer selection decisions (see here and here for more on that). But how does the concept of selection procedure validation translate to the compensation arena, in light of the CFPA?

Let’s consider educational background, a commonly used pay factor. Education may be a clear “bona fide factor” for jobs where a degree is required to practice (e.g., MD for doctors), but what about for other jobs for which degrees are simply considered beneficial (e.g., MBAs for managers)? For the latter, the CFPA may require a rigorous analysis linking education to core job duties or outcomes to justify its use as a valid pay factor. Furthermore, it is unclear if establishing job-relatedness is enough to also satisfy the business necessity component, or if an employee could put forth a reasonable alternatives argument that certain experience or certifications would be comparable to the educational background, meeting the same business purpose.

Demonstrating the validity of pay factors has come up before in the EEO context. As an example, see Jock et. al. v Sterling Jewelers (Part 1 and Part 2), where  an arbitrator recently certified a class for a disparate impact claim alleging that job experience factors used to set starting salary were not job-related. The merits of the claim have yet to be decided. It appears that the CFPA will increase the challenges for California employers. We are all still working to understand how exactly the CFPA will be interpreted by the courts, so stay tuned.

By Samantha Holland, Consultant and Kristen Pryor, Consultant at DCI Consulting Group

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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.

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