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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

The 32nd Annual Conference for the Society of Industrial and Organizational Psychology (SIOP) was held April 26-29, 2017 in Orlando, Florida. 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; you may use the list below to navigate to a particular summary.

Analytics has a Seat at the Table: Now What?

A panel of I/O practitioners discussed considerations and challenges when building workforce analytics functions, including critical partnerships, cultural change, understanding stakeholders, and maintaining a business focus. These experts shared tips and tricks for implementing workforce analytics in their organizations, including:

  • Need an understanding of the systems, stakeholders, and IT in order to shape the input and pulls to improve data output
  • If organizations are siloed, it’s important to make connections across groups to ensure you can make the case that workforce analytics are needed
  • The role of the HR Business Partner is changing – analytics will be a part of their role if not already. Companies can provide skill building workshops to ensure knowledge and skills are appropriate. Online courses for statistics that can also be utilized.
  • If certain HR groups are opposed to utilizing analytics, advertise the benefit to HR stakeholders
    • Frontline HR: analytics can streamline work, provide better results, give better insight into workforce, candidates, etc.
    • Strategic HR: analytics can help balance a culture of constant deliverables, defining relationships by leadership and analytics, translating vision into action.
    • HR leadership: analytics can build the business case, define ROI, provide a common language
  • If rolling out a new process or program, use pilots to your advantage to make the business case, design research for short and long term results, and have two-way feedback loops with stakeholders. Leverage interviews to round out research and implementation – know your users and stay up-to-date on what’s important.

Burden of Proof: Can I-Os and Employment Counsel Successfully Collaborate?

A panel of labor attorneys and I/O psychologists, both practitioners and educators, spoke on the complexities of working on employment law issues and challenges in organizations. Listed below are several recommendations, mostly focused on considerations when developing, validating, or selecting a (off-the-shelf) test.

  • Federal agencies are focused on the search for less adverse alternatives for tests. I/O panelists shared that this has been a challenge in getting this search addressed fully in the technical report; this is especially a challenge with vendors who only have one or few tests and they don’t have alternates available.
  • Panelists cautioned about using automated resume screens and machine learning. Without perfect correlations there’s a high likelihood that at least one resume will make it through that is not good – this can limit defensibility of process. Or the tools could be weighing for words like church, Africa, etc. which would create risk. Also, how do you validate a test that is constantly changing? A panelist from PepsiCo noted that they opted to not do this – they don’t want to be the first in the courtroom.
  • Panelists also cautioned about working with vendors who specialize in gaming assessments – these vendors often don’t have I/O or validation knowledge and skill set.
  • Panelists recommended that organizations get legal involved early when selecting a vendor and/or new test – involve them in the RFP process.
  • It was also noted not to forget about international considerations, especially data privacy. It’s recommend that the organization involve counsel from each country.

Making Better Business Decisions? Risks and Rewards in Big Data      

This session was moderated by DCI’s Dr. Emilee Tison and highlighted both the risks and rewards in using big data techniques to inform employment decisions. As data analytic techniques continue to evolve and incorporate increasingly sophisticated methodologies, employers are cautioned in using such techniques with little to no transparency on the how or why results are being calculated. Although big data approaches in employment decision making can offer great benefits in terms of overall cost, time constraints, more positive candidate experiences, and better statistics, it is imperative for employers to also weigh these benefits against any legal and practical considerations. Such considerations may include privacy/confidentiality concerns, and also the increased potential for such techniques to lead to adverse impact against protected groups if variables are not fully validated or researched at the onset.

Big data techniques are only increasing in popularity and will only continue to evolve at a rapid pace moving forward. Although these techniques can seem very appealing to an employer in informing decisions on the front-end, they can prove very difficult to defend in court at a later date. For this reason, companies are advised to be cautious in implementing any new and improved techniques, as they will be asked to explain how the information being used is job-related. The major takeaway here is for employers to ensure they have the right justification for what they are doing before they incorporate any big data approach into business decisions.

Solving the Law Enforcement Staffing Crisis

DCI’s Dr. Michael Aamodt, together with representatives from both the U.S. Secret Service and U.S. Customs and Border Protection, lead an open discussion on the challenges that many law enforcement agencies are experiencing present day in both attracting qualified candidates to their organizations and meeting demanding staffing needs. Often times, demand is high, but agencies struggle to fill positions as a result of small applicant pools and applicants who do not pass the background check stage in the process.

Discussions focused primarily on causes of the applicant shortage (i.e., working conditions, job location, and strict policies/requirements), strategies for assessment and recruitment, and changes that may be warranted with regard to the background check process. It was also suggested that agencies review their current policies and procedures, and update where possible any strict policies that appear to exclude otherwise qualified applicants (i.e., work to reduce a strict tattoo or piercing policy).

I/O’s Role in Advancing HR in the Big Data Charge

Panelists in this session included DCI’s Dr. Eric Dunleavy and others from diverse backgrounds in both applied research and analytics departments who discussed recommendations with regard to how the I/O community can advance the current state of human resources management.

As big data continues to become increasingly prevalent in the world of HR, I/O psychologists find themselves in a position to lead the big data charge and contribute their knowledge and expertise in this realm. Companies are tasked with balancing a great deal of risk associated with the use of big data techniques in the employment decision making process, and establishing meaningful and legally defensible models requires a lot of human touch and input. New tools may make compiling and analyzing big data simple, but the tools themselves don’t tell us why something occurred and what we should do based on those results. This fact represents an opportunity for I/O psychologists to assist HR professionals in turning results into actionable information.

Optimizing Validity/Diversity Tradeoffs in Employee Selection

The session entitled “Optimizing Validity/Diversity Tradeoffs in Employee Selection” included three presentations that discussed alternative ways to handle the tradeoff between selection procedure validity and adverse impact. Typically, high validity is associated with high adverse impact, and this session focused on ways to maximize validity while keeping adverse impact at a tolerable level. Methods included algorithms to identify biodata scoring systems that balance this tradeoff, pareto optimal methods for weighting the selection components in a composite, and methods to estimate the extent to which pareto-optimal weights established in one sample generalize to other samples.

This session highlighted the challenges inherent in developing a selection system that is both highly predictive and free of subgroup differences. Art Gutman, the discussant for this session and a frequent contributor to DCI’s blog, commented on the approaches presented within the context of legal precedent. He noted that while these approaches may seem reasonable from an academic/research perspective, there may be sizeable hurdles to overcome in the courtroom. DCI will be on the lookout for the utilization of these approaches.

O*NET Based Research: Leading Edge or Wasted Opportunity

This symposium entitled “O*NET Based Research: Leading Edge or Wasted Opportunity?” showcased novel uses of O*Net data. A presentation by DCI’s Dr. Kayo Sady examined the importance of various personality characteristics in predicting salary across different industries. For example, he found that extraversion is highly valued in the tech industry. Dr. Sam Holland presented a tool that explored O*Net data from a network perspective to help job seekers find jobs that offer many potential career options. Using this tool, one can identify both the most advantageously situated jobs and the patterns of characteristics associated with those jobs.

Leading the Charge: IGNITING Veteran–Workforce Integration Solutions

A diverse panel consisting of representatives from academia, The SHRM Foundation, the military, consulting, and employer perspectives led a discussion touching on a specific timepiece in a veteran’s transition into the civilian work life. Challenges facing veteran transitions are broad in nature, and the lack of available data to assist with researching veteran outcomes as they transitioned to civilian life was discussed. Resources to help recruit and retain veterans have been published by the SHRM Foundation.  Future research is underway to help understand retention challenges for veterans in organizations.

Annual EEOC/OFCCP Practitioner Update

DCI’s Mike Aamodt and Joanna Colosimo were joined by colleagues from Fortney Scott, LLC and Capital One to update the SIOP community on current EEOC and OFCCP enforcement trends and implications from the presidential election. The panel focused on current pay equity enforcement trends, strategic outreach and recruitment for protected groups, and selection issues from an EEO perspective.  Best practice takeaways from the session highlighted the importance of collaborating with legal counsel, conducting proactive pay equity studies, and proactively monitoring the effectiveness of selection, recruitment and outreach programs on protected groups.

Mentoring for Women in I/O: Career Changes, Interruptions, and Transitions

In a moderated panel session, the presenters discussed issues for women in I/O that arise due to non-linear career trajectories. For example, job changes often are seen as resulting from indecision rather than strategy. Panelists and moderators were:

  • Silvia Bonaccio – University of Ottawa
  • Irini Kokkinou – SCAD
  • Kea Kerich – Marriott International
  • Alison L O’Malley – Deere & Company World Headquarters
  • Tatana M. Olson – United States Navy
  • Kristen M. Shockley – University of Georgia
  • Jane Wu – IBM
  • Lynda Zugec – The Workforce Consultants

The primary focus of the session was on anecdotes of the panelists’ own career trajectories. Additionally, the panelists were asked to respond to the following questions:

  1. What factors led you to a non-linear career path?
  2. What challenges did you face in pursuing a non-linear career path? How did you handle these challenges?
  3. What opportunities resulted from your non-linear career path?
  4. What skills did you develop from implementing your career change(s), interruption(s), or transition(s)?
  5. What advice do you have for graduate students going on the job market or for more experienced professionals considering interrupting/changing careers?

In the final portion of this session, panelists met with groups of audience participants to discuss in more detail their experiences and advice.

Innovative Adverse Impact Analysis

This expert panel covered a variety of topics related to complex adverse impact analyses. The panelists shared innovative approaches to constructing analytics when responding to intricate personnel decision-making situations. Moderators of the panel were Scott B. Morris (Illinois Institute of Technology) and Eric M. Dunleavy (DCI Consulting Group). Panelist topics included the following:

  • Donald R. Deere (Welch Consulting) discussed options for accounting for non-neutral analysis of cases in which multiple applicant records exist for a candidate.
  • Daniel C. Kuang (Biddle Consulting Group) discussed use of a composition analysis through binomial statistics to measure the difference of % selected versus % expected.
  • Fred Oswald (Rice University) discussed alternative measures to using impact ratio. Such measures included: odds ratio, Phi, absolute selection rate difference, Cohen’s h, and shortfall.
  • Richard F. Tonowski (U.S. Equal Employment Opportunity Commission) discussed the utility of measuring practical significance, sharing examples of when the addition of practical significance is critical to cases seen by the EEOC.

Alternative Session:  New Directions:  Enhancing Diversity and Inclusion Research and Practice

In a thought-provoking, alternative session entitled, “New Directions:  Enhancing Diversity and Inclusion Research and Practice,” five scholars and practitioners took stage to discuss the current state of diversity and inclusion research and how to better align research with practice. The following quotation, which was cited twice during the session, really resonated:  “Despite a few new bells and whistles, courtesy of big data, companies are basically doubling down on the same diversity and inclusion approaches they’ve used since the 1960s.” (Frank Dobbin, Harvard University; Alexandra Kalev, Tel Aviv University). Dr. Alice Eagly, Northwestern University, argued that right now there is a large gap between what research shows (academia) and what generalizations policy makers and practitioners are using. She challenged researchers and practitioners to be better, more honest stewards of diversity and inclusion knowledge so that “fake news”-esque generalizations propagated by advocacy groups, in particular, do not hinder forward progress in this field.

Up next was Julie Nugent, Vice President of Research at Catalyst, who led a discussion on what inclusion and exclusion feels like for employees in organizations. The Catalyst study found that employees feel included when they are valued for their specific contributions (uniqueness) and are welcomed among their peers (belongingness). In contrast, feelings of exclusion in employees arise if they are devalued/dismissed for their unique characteristics, especially their gender, race/ethnicity, nationality, age, religion, and sexual orientation.

Last, Dr. Gabriela Burlacu, SAP SuccessFactors, wrapped up the session by explaining how each step of the employee life cycle and the personnel decisions made therein, from applying to being hired, paid, trained, promoted, and terminated, can be better tracked and managed by technology – technology that when used appropriately can mitigate the threat of unconscious bias.  She spoke of “technology nudges,” such as defaulting bonuses and pay increases to absolute values rather than percentage increases based on base salary.

At DCI, mitigating unconscious bias through the creation and administration of structured personnel systems is something we assist our clients with every day, especially as it relates to EEO risk. We will continue to follow developments related to this type of technology, as well as other forms of “big data,” used to make personnel decisions and keep you posted with our recommendations.

Symposium/Forum:  Novel Workplace Diversity Interventions:  Field Experiments with Promising Results

In this well-attended session entitled “Novel Workplace Diversity Interventions:  Field Experiments with Promising Results,” five researchers and practitioners presented on the effectiveness of four field experiments in promoting positive diversity-related outcomes and improving diversity management in organizations. Dr. Alex Lindsey’s research focused on diversity interventions such as perspective taking (to produce empathy), goal setting (to increase internal motivation) and reflection (to produce guilt) and their effects on pro-diversity attitudes and behaviors. He found that the reflection intervention was most effective in increasing internal motivation and pro-diversity behaviors, but it also promoted anger and frustration a week later. Dr. Lindsey admitted future research (perhaps into more hybrid reflection and goal-setting activities) might be necessary to reach resistant diversity trainees in organizations.

In one more example, Jose David and Carolyn Fotouhi from Merck presented on their company-wide women’s sponsorship program. Jose explained that Merck had recently come out with key healthcare products in oncology, HPV, and insomnia, but their sales were lagging, so they decided to revamp their operating model. After some internal research, Merck found that females comprised less than 1/3 of incumbents in critical roles and slightly over 1/3 female incumbents in director-level roles, yet females make 90% of healthcare expenditure decisions and make up more than 50% of the patient base. Thus, Merck developed an advancement-focused women’s sponsorship program that allowed women protégés of all ranks to interface with women in leadership positions through one-on-one virtual sessions and networking circles.  They found that women protégés experienced a higher rate of internal movements (9.5% more females in critical roles and 3.5% more females in Director-level roles) and greater representation of females in succession planning slates. It will be exciting to note if increases in female representation in critical/director-level roles translates into increased key product sales. Perhaps only time will tell.

Caught on Video: Best Practices in One-Way Interviewing

The “Caught on Video: Beat Practices in One-Way Interviewing” session kicked off with a definition of one-way interviewing and how it differs from traditional two-way interviewing.  In a nutshell, one-way interviewing is the practice of utilizing video recording to capture applicant’s responses to interview questions that can then be scored at a later time.

One-way interviewing is still relatively new and not widespread in practice.  Therefore, the panelists recommended some best practices based on their experiences including:

  • Filming actual recruiters (as opposed to actors playing recruiters) asking the interview questions
  • Creating a behavioral indicators checklist for recruiters to quantitatively and systematically rate applicants
  • Developing questions through a rigorous process which may include a job analysis
  • Continually rotating questions to prevent question-sharing among applicants

According to the panelists, initial reactions from applicants have been positive.  For example, they like the flexibility of one-way interviewing.  Recruiters also enjoy the method for its flexibility (some recruiters watch the videos while on the treadmill!) and like that the interviews have a clear scoring rubric.

What is Machine Learning? Foundations and Introduction to Useful Methods

The session entitled “What is Machine Learning? Foundations and Introduction to Useful Methods” was targeted at individuals with basic to intermediate understanding of machine learning.  Supervised vs. unsupervised models of machine learning were discussed.  As a best practice, the panelists recommended cross-validation to estimate the R2 shrinkage.  Optimally, the data would be broken into a training, validation, and test set so that the researcher may develop, train, and then test the final model.

There are several important concerns that may impact machine learning models.  For example, overfitting occurs when the model predicts data too well for the sample and does not generalize.  Another concern with machine learning is the bias/variance tradeoff.  This was likened to reliability/validity in that high reliability is akin to low variance and high validity is akin to low bias.  Finally, the curse of dimensionality refers to the fact that more predictors require a bigger sample.  As tempting as it may be to add many predictors, it’s prudent to keep in mind what your sample size is when building a machine learning model.

Applicant Reactions During Selection: Overview and Prelude to a Review

With the rise in technology-based selection tools, the panelists in the “Applicant Reactions During Selection: Overview and Prelude to a Review” session claim that research on applicant reactions (ARs) has not kept pace with the advances in the types of selection tools used in practice.  Research results suggest that applicants favor technology-based testing over traditional media (e.g., Potosky & Bobko, 2004).  However, when it comes to face-to-face interactions (e.g., interviews), individuals still prefer they be in-person without a technology interface (Straus, Miles, & Levesque, 2001).

Another topic centered on why employers should be concerned with ARs.  The argument is that because ARs are not directly tied to measurable performance on the job for hired employees, it may not matter to employers how applicants perceive the selection process.  However, the panelists argue that ARs do matter.  Further, AR research has been limited in that there are several moderating variables, including selection context variables (e.g., hiring expectations and selection ratio, job desirability), organizational context variables (e.g., organizational size), and individual-level variables (e.g., personality) that have not been examined but that may significantly impact ARs in differing ways.

The Pre-conference Master’s Consortium: Advantages & Strategies for Building Your Business Acumen

A pre-conference session on “Advantages & Strategies for Building Your Business Acumen” was presented by Keli Wilson with DCI Consulting Group. The basis for this talk was to help I-O psychologists entering the workforce to understand the following three concepts: (1) the bottom-line; (2) business development; and (3) the request for proposal process.

Specifically for the bottom-line discussion, information was shared regarding how to communicate and tie organizational outcomes to savings (e.g., minimizing turnover, mitigating risks such as discrimination claims by understanding the mission of EEOC and OFCCP, and leveraging press releases of other companies to demonstrate purpose and savings for embracing I-O practices).

As I-O psychologists advance in their careers and understand the organization they work for or consult with, there may be opportunities to identify potential gaps and communicate opportunities for efficiencies and business growth. A high-level overview of what this process entails was covered during the session (e.g., conduct market research, recognize stakeholders, have a strategic vision, prepare and implement a business plan, market and create buy-in). The guidance provided was to hone presentation skills and learn how to pitch and sell ideas.

Finally, given I-O psychologists commonly go into consulting careers, there is a need to understand the request for proposal process in order to win and partner with clients on projects. An overview of the typical stages of a proposal process were shared with the graduating students (e.g., sales call, proposal, interview, question/answer, sales demo, negotiation of pricing, and signed contract). It was discussed that the scope of the project within the proposal should clearly state the identified problem and provide a proposed solution. Additionally, a tutorial on proposal pricing was provided in this session (e.g., pros/cons of hourly, daily, or project based pricing).

In summary, students graduating with an I-O Master’s degree were provided with the opportunity to be exposed to the business aspects that may not be covered in I-O graduate programs.

Industry Differences in Talent Acquisition

This SIOP conference session was led by a panel of speakers from various organizations: Jenna C. Cox, IBM; Amanda Klabzub, IMB; Mary Amundson, Land O Lakes; Jennifer M. Dembowski, The Home Depot; Nicole Ennen, Google; Hailey A. Herleman, IBM; and Lisa Malley, DDI. The focus of the discussion was on the similarities and differences of talent acquisition across industries. The similarities across industries is in the area of attracting and selecting the talent that supports business initiatives, but differences were noted in the scarcity of talent and need to grow specific talent (e.g., through educational programs), as well as the geography in which the company operates in (e.g., the talent mix in different markets). Also, unemployment rates may be great for job candidates, but not for retailers because of the reduction of qualified pools. It was shared that manufacturing jobs that pay very well can be difficult to fill due to schedules that are unappealing, unpredictable work, and often dirty working conditions. Furthermore, it was communicated that agriculture is very hard to staff, particularly middle management. As for the tech industry, it was stated that you may need to source candidates more than other industries because some of the most qualified and best people for the job already have jobs.

The panelists mentioned that a key differentiator in attracting talent is organizational culture and branding. In addition, the panelists touched on the candidate experience and how they strive to bring down the median days to selection. Some examples of how this was achieved were through cutting out layers of approval, using selection tools, and trimming down the number of interviews as long as a fewer number of interviews was just as predictive. In regards to the candidate experience, the company desire to want people to come back to them, as well as to understand what went well and what can be improved, which is gathered through a candidate experience survey.

Finally, a way to make your company more appealing to candidates seeking employment is to focus on the career site (e.g., social life at company and organizational culture). The goal would be to make it easy to use the career site and to allow candidates to find the jobs that they want to apply for. When it comes to the selection process, it was shared that having a hiring committee can help mitigate individual unconscious bias in the hiring process (e.g., come in and review all the materials of the entire process and feedback/scores from structured interviews).

“That Company is Great!” Best Practices for Improving Candidate Experience

This SIOP conference session was panel style with the following guest speakers: Brittany J. Marcus-Blank, University of Minnesota; Sarah A. Brock, Johnson & Johnson; Pamela Congemi, Medtronic; Jim Matchen, Target Corporation; Marina Pearce, Ford Motor Company; and Amy Powell Yost, Capital One. The topic of discussion was on how to create a positive candidate experience.

Taking action to improve candidate experiences not only helps to secure top talent, but it also benefits brand loyalty. The following are some of the practices shared amongst the panelists to increase candidate experiences:

  • setting candidate expectations (i.e., transparency of the selection process and communication of their status at each step);
  • calling each declined candidate (i.e., receive a personal call from talent acquisition);
  • being aware of the physical space in which the interviewees will spend time (e.g., have an identified space that candidates will not be distracted and at the same time impressed by what the candidate can see of the company);
  • picking the candidate up from the airport;
  • planning a welcome session and/or tour of facility;
  • assigning an onsite coordinator to welcome the candidate being interviewed;
  • providing the candidate with information about the interviewer(s) beforehand;
  • empowering recruiters to use discretion in sending gifts on behalf of the company (e.g., send a small gift to someone who just graduated with an MA degree or who just dropped out of the selection process due to a devastating life event);
  • implementing a candidate reaction survey;
  • monitoring Glassdoor and similar websites to discover feedback; and
  • training employees on good behavior (e.g., actions that will be appreciated by the candidates, such as a recruiter remembering their name).

Physical Abilities Testing: Lessons Learned in Test Development and Validation

This panel, which included DCI’s Emilee Tison, discussed unique challenges associated with physical abilities testing. Panelists identified challenges encountered in the field and shared lessons learned in this area of work. Specifically, the presenters addressed the following topics:

  • Test Development – how developing a physical abilities test is different from other types of selection tests
  • Adverse Impact – typical assumptions of existing sub-group differences and methodologies to reduce adverse impact
  • Validation – strategies typically used for physical abilities tests and how this differs from other types of selection tests
  • Criteria for Validation – typical criteria used for criterion-related validation evidence and challenges faced during these analyses

Panelists cautioned organizations to ensure a full understanding of the physical requirements of the job as well as the types of physical abilities tests available for implementation. Physical abilities testing is not a ‘one-size-fits-all’ process; considering it as such increases risk of a mismatch between the physical requirements of the job and the test being implemented, and increases legal risks.

Master Tutorial: R Shiny Apps in I/O

In this session spearheaded by DCI’s Sam Holland, the use of R’s Shiny package was showcased to demonstrate its usefulness in sharing and visualizing analytic results. It allows R users with no programming background the ability to deploy web-ready applications to showcase results. After walking participants through the basic concepts and principles needed to leverage the package, presenters demonstrated how quickly basic R scripts can be transformed into interactive dashboards.

Everything UGESP Forgot to Tell You About Content Validity

This panel, moderated by Emilee Tison, Ph.D. (DCI Consulting Group), discussed the importance, usefulness, and practicality of content-oriented validation methodologies, which is the extent to which the content of the selection procedure reflects important performance domains of the job. This methodology, however, is often criticized for having limited application and questioned as to whether it increases the likelihood of actual prediction of job performance.

Each panel member spoke about a different topic and the role played by content-oriented validation methodologies:

  • James Sharf, Ph.D. (Sharf and Associates) provided a history of the development of EEOC’s Uniform Guidelines on Employment Selection Procedures (UGESP). Of particular interest was the use of validity generalization and whether or not it was appropriate to use.
  • Mike Aamodt, Ph.D. (DCI Consulting Group) discussed background checks and the various aspects to address when linking risk areas to specific tasks performed.
  • Damian Stelly, Ph.D. (Flowserve Corporation) addressed using content validation as a potential alternative to criterion validation for personality assessments. The applicability will often depend on the specific context of the situation.
  • Deborah Gebhardt, Ph.D. (HumRRO) discussed a number of important considerations when using content validation for physical ability tests. Some of these include: a reflection of essential job tasks and work behaviors, feasibility of the simulation, using only basic skills and not those learned on the job or in training, safety, ability to standardize test components, using a meaningful scoring metric, and the reliability of test components.

By Amanda Shapiro, Senior Consultant; Brittany Dian, Associate Consultant; Samantha Holland, Consultant; Joanna Colosimo, Director of EEO Compliance; Jana Garman, Senior Consultant; Jeff Henderson, Associate Consultant; Julia Walsh, Consultant, Keli Wilson, Senior Manager of EEO Compliance, D&I; Cliff Haimann, Consultant; Bryce Hansell, Associate Consultant; and Emilee Tison, Associate Principal Consultant, at DCI Consulting Group

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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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Really, I Come Here for the Food: Sex as a BFOQ for Restaurant Servers

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

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Fiscal Year 2018 Budget Proposes Merger of OFCCP and EEOC

The Department of Labor’s Fiscal Year 2018 (FY2018) budget proposal was released today, May 23, 2017.  The budget outlines the initiatives and priorities of the new administration, and as predicted by DCI, recommends merging the Office of Federal Contract Compliance Programs (OFCCP) and Equal Employment Opportunity Commission (EEOC) by the end of FY2018.

The proposed budget indicates that the consolidation will provide efficiencies and oversight.  Additionally, the proposed budget allots $88 million for OFCCP, a decrease of $17.3 million from Fiscal Year 2017.  The main cut to the budget appears to be headcount, with a proposed 440 full-time equivalent (FTE) headcount, a reduction from 571 FTEs.  Some other interesting items that have

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