Yvette P. Lopez
Helen LaVan
William M. Martin- Department of Management and Entrepreneurship, DePaul University, Chicago, IL, United States
This paper examines recent research on employment discrimination and addresses basic issues concerning who should be the focal subjects of employment discrimination research and which search terms should be examined. This article proposes that the way forward in employment discrimination research is using empirical legal scholarship and various large databases that support a more holistic approach to examining the different subjects of employment discrimination and the various search terms used to identify employment discrimination issues. This article explains how empirical legal scholarship, content analysis, and thematic analysis can be utilized to better understand employment discrimination. The paper concludes with propositions and recommendations for future research, including an intersectional focus.
Introduction
One of the key controversies in employment discrimination research is the prevalence of the problem and the difficulty of researching it. Yet, research on employment discrimination has been lacking. Several studies have expressed this concern with the current state of employment discrimination research (Hajnal and Scharle, 2023; Triana et al., 2021; Derous and Pepermans, 2019; Diaz and Bergman, 2013; Ruggs et al., 2013; Joseph and Rousis, 2013; Lindsey et al., 2013).
This research focuses specifically on the United States context, where employment discrimination law operates at two levels: federal and state. At the federal level, Title VII of the Civil Rights Act of 1964, the Americans with Disabilities Act (ADA) of 1990, the Age Discrimination in Employment Act (ADEA) of 1967, and other statutes prohibit discrimination based on protected categories such as national origin, disability, gender, and race. The Equal Employment Opportunity Commission (EEOC) serves as the primary federal enforcement agency, investigating complaints and facilitating resolutions through mediation, conciliation, or litigation. When informal resolution fails, individuals may pursue claims through federal district courts, state courts, or mandatory or voluntary arbitration systems. This multi-tiered dispute resolution framework creates rich databases of discrimination cases that researchers have largely underutilized, despite their potential to provide insights into actual experiences of employment discrimination.
One of the fundamental issues of employment discrimination research is how researchers should collect the data. That is, should the focus be on employees who have experienced employment discrimination, or should the focus be on marginalized employees? We contend that archival data consists of marginalized and non-marginalized groups pursuing their rights in a given forum. Definitions vary concerning who is marginalized in a given context/situation. Are truly marginalized individuals likely to be included in other data collection methods? Does it better contribute to our knowledge of discrimination to obtain data from individuals who are actually pursuing their rights?
Identifying individuals as subjects who have been discriminated against is difficult, regardless of the basis of typically structured research (Thompson and Morris, 2013). Only a tiny proportion of individuals in any one organization have characteristics protected under the laws, and not all are necessarily discriminated against, especially within a narrow time frame (Thompson and Morris, 2013). Furthermore, it is difficult for researchers to collect the data from discriminated individuals, given top management's concern for potential legal issues and reputation. This contributes to organizations being reluctant to grant access to researchers studying the topic of discrimination (Ruggs et al., 2013). Experiencing discrimination is decidedly underreported, which further complicates the collection of the data (Cech, 2024; Bornstein, 2021; Dahl and Knepper, 2021). A recent Pew Research Poll found that nearly 80% reported seeing discrimination against Arabs, Blacks, Hispanics, as well as Jews and Muslims (Alper et al., 2024). As it relates to employment discrimination, nine out of ten (91%) of respondents said they have experienced discrimination at work, while nearly eight out of ten (77%) have witnessed employment discrimination (Monster Poll, 2023).
Given the complexities involved with gaining access to victims of discrimination in the workplace, researchers have begun emphasizing the importance of focusing on marginalized employees as a means of better understanding employment discrimination. Marginalized employees may present an opportunity for accumulating a vast body of knowledge based on their experiences with employment discrimination (Roberts and Nkomo, 2025; Ruggs et al., 2013). “Marginalization typically involves some degree of exclusion from access to power and/or resources” (Maynard and Ferdman, 2009, p. 25). Groups likely to be marginalized consist of the working poor, immigrant workers, migrant workers, young workers, chronically unemployed individuals, and “any group that has minority or lower social status in the society, including, for example, ethnic minorities, older workers, workers with disabilities, and lesbian, gay, bisexual, and transgender/transsexual (LGBT) employees” (Maynard and Ferdman, 2009, p. 26). Marginalized employees often share struggles tied to their diversity and may face challenges related to injustice, stigma, or discrimination (Maynard and Ferdman, 2009; Nikolaou, 2022).
While research has focused mainly on discrimination of marginalized groups representing ethnic, immigrant, racial, and religious minorities (Fluit et al., 2024), researchers have missed the opportunity to examine the broader range of marginalized groups' experiences (Ruggs et al., 2013) including those with disability, LGBTQIA+ and others (Fluit et al., 2024). However, some researchers have criticized the marginalized employee approach. Such criticism has focused on several dimensions, including solely focusing on marginalized groups in descriptive and mutually exclusive ways, not recognizing the complexity of discrimination from a perspective of multiple categories, and concerns of within-category discrimination (Phillips and Ranganathan, 2025; Derous and Pepermans, 2019).
Additionally, prior research examining employment discrimination has used survey investigations, which resultantly contain participants who may or may not have experienced employment discrimination. Researchers such as (Einola and Alvesson 2021) and (Wulff and Villadsen 2020) warn against overreliance on existing survey instruments when we study complex social aspects of organizations. Yet, clearly, those who have experienced discrimination in the workplace may be the richest source of information when it comes to examining employment discrimination. Therefore, it would be valuable to access the information that those who have experienced employment discrimination can provide.
A second fundamental issue of employment discrimination research involves its limited focus. As indicated above, existing research examining employment discrimination has been predominantly concentrated. It is important to examine a broader group of marginalized individuals dealing with issues related to religion, national origin, age, disability, weight, and marital status.
Yet, researchers warn about potential negative implications when several qualifying classes are examined in employment discrimination (Joseph and Rousis, 2013; Ruggs et al., 2013; Sawyer et al., 2013). That is, “casting an overly wide net in the study of discrimination may potentially make it easier for individuals to make discrimination claims based on any number of characteristics and as a result, harm social interactions in the workplaces by creating fear among employees… taken out of proportion, the study of discrimination can become muddled” (Ruggs et al., 2013, p. 256). However, this is not a justification for limiting research on marginalized groups (Ruggs et al., 2013).
In fact, we contend that there is justification in questioning whether all employment discrimination research has been adequately identified. Recently, scholars examining discrimination have begun to follow the encouragement of earlier researchers (Sawyer et al., 2013; Styhre and Eriksson-Zetterquist, 2008) on the importance of understanding intersectionality discrimination research (Vogel et al., 2018; Köllen, 2021; Mooney, 2016; Ruiz Castro and Holvino, 2016; Bailey et al., 2019). Studying multiple intersecting identities is vital to achieving a complete view of workplace diversity (Sawyer et al., 2013).
Methodology
The way forward in employment discrimination research
Here, we propose that researchers utilize existing databases to research individuals who have been discriminated against. This is in contrast to the use of interviews (Cuadraz and Uttal, 1999), surveys (D'Ancona, 2017), correspondence tests (Zschirnt, 2019), or experimental designs (Baert, 2018). Not that these methodological approaches fail to contribute, rather, it is more a matter of concern that they do not take a sufficiently in-depth look at examining discrimination.
This does not intend to second-guess the methodologies used by other researchers in previously peer-reviewed and published articles, but rather to note that there are certain instances where researchers could have used specific databases but did not. The contributions to discrimination research that previous researchers have made could be enhanced by using available databases, approaching the data collection and analysis through the lens of empirical legal scholarship, and using state-of-the-art analytical tools. However, the previous research is limited by these factors.
Our proposed recommendation is three-pronged: (1). Identifying societal impetus that fosters the need not only for employment discrimination research, but for research on individuals who have been discriminated against. (2). Identifying illustrative publications that have used empirical legal scholarship and these databases to research employment discrimination and/or employment-related concepts, such as whistle-blowing (Lee and Fargher, 2013), bullying (Martin and LaVan, 2010; Richardson et al., 2016a,b), or justice (Best et al., 2011), and (3). Making recommendations regarding testable premises using the recommended databases.
Societal impetus that fosters the need for employment discrimination research with a victim focus
How researchers should conduct employment discrimination research is undoubtedly impacted by societal shifts, and legal rulings are sure to impact it. For example, concerning societal influences, the most monumental of these changes include the recent “Black Lives Matter” movement and the “Me Too” movement. It can be reasonably expected that both of these movements will result in discriminated individuals having more confidence in pursuing their rights. It can also be reasonably expected that more individuals who were retaliated against for pursuing their rights will pursue retaliation claims. Combined, these will probably lead to organizational issues of disparate impact and disparate treatment.
The contemporary political landscape further underscores the urgency of this research approach. The current administration has issued several Executive Orders (EOs) eliminating Diversity, Equity, and Inclusion (DEI) programs across federal agencies and encouraging similar changes in private sector organizations. There are three notable Executive Orders: EO 14173—Ending Illegal Discrimination and Restoring Merit-Based Opportunity (Executive Order 14173, 2025); EO 14151—Ending Radical and Wasteful Government DEI Programs and Preferencing (Executive Order 14151, 2025); and EO 14281—Restoring Equality and Meritocracy (Executive Order 14281, 2025). Despite these restrictions and changes, EEOC enforcement continues in other areas. For instance, Columbia University agreed to a $21 million settlement in January 2025 for alleged harassment against Jewish employees following October 7, 2023, the largest EEOC settlement in nearly 20 years. As a result, individual researchers and organizations may become reluctant to conduct and participate in survey-based research or qualitative research involving interviews or focus groups. Hence, archival legal databases and EEOC case records provide researchers access to discrimination experiences difficult to capture through conventional methods.
In terms of legal aspects, the Supreme Court recently ruled in RG and GR Harris Funeral Homes, Inc. V. Equal Employment Opportunity Commission (2019) that LGBTQ individuals are protected under Title VII of the Civil Rights Act of 1964. Additionally, the EEOC has recently started to recognize the validity of intersectional claims, meaning that individuals who have been discriminated against can file claims on multiple bases [Equal Employment Opportunity Commission (EEOC), 2005]. How researchers will conduct employment discrimination research in future studies will undoubtedly continue to change due to these societal and legal examples.
Illustrations of empirical legal scholarship
Empirical legal scholarship refers to “a specific type of empirical research: a model-based approach coupled with a quantitative method” (George, 2006, p. 141). There is a subset of empirical legal scholarship that uses statistical techniques and analyses. This involves the use of studies that “employ data (including systematically coded judicial opinions) that facilitate descriptions of or inferences to a larger sample or population as well as replication by other scholars” (Heise, 2002, p. 821). Therefore, empirical legal scholarship can range from as simple as counting and surveys to more complex empirical analysis, including significance testing, multiple regression, and logit/probit analysis (Cahoy, 2010). The goals of empirical legal research are verifiability, falsifiability, and reproducibility (Bétaille, 2025).
Some authors are starting to recognize the importance and contributions of empirical legal scholarship, albeit in legal journals, and have pointed to the value of automated content analysis to enhance empirical legal scholarship (Zeiler, 2016; Allen and Blackham, 2018; Irvine et al., 2018; Blackham, 2019; Ovádek et al., 2024). We propose that the study of employment discrimination research can be enhanced by empirical legal scholarship and content analysis methodology, cognizant of both the contributions and pitfalls (Allen and Blackham, 2018).
Contribution of content analysis methodology
Content analysis is a methodology for discerning and organizing the content of both written and verbal forms of communication. In research related to employment discrimination, researchers have explicitly referred to the content analysis of a variety of third-party resolutions of disputes in litigation and arbitration. Initially, the content analysis of data was most likely done manually by researchers and their assistants (Evans et al., 2007; Kohlbacher, 2006). This involves manually reading and coding the cases. More recently, researchers have begun to utilize software such as NVivo and Atlas.TI, R, and/or AI (Banks et al., 2018; Jackson and Bazeley, 2019; Gibbs, 2014; Kalpokaite and Radivojevic, 2019; Miner et al., 2023; Conklin and Houston, 2025) to assist with the coding process. The efficacy of other software, such as Leximancer and Crawdad, can also be used for content analysis of this type of data (Lock and Seele, 2015). Different software require different approaches to data analysis. Most software provides online tutoring for acquisition of the skills needed to do content analysis.
Some limited research as early as the 1980s used the approach we are suggesting. Specifically, they used databases to research topics related to employment discrimination. However, they have some limitations when viewed from our current perspective, such as being dated, using relatively small sample sizes, and being narrowly focused. Examples of these few exceptions include studies examining how victims of employment discrimination fared in federal discrimination litigation related to the legal venue (Clermont and Schwab, 2004), specific litigation such as comparing case outcomes under the Americans with Disabilities Act (Posthuma et al., 2016), issues relating to contexts to discern differences in the nature of employment discrimination litigation between private and public sectors (Terpstra and Honorée, 2016) and using mixed methods in analyses of sexual harassment claims (Lockwood et al., 2011). However, these streams of research do not exist in sufficient concentration to develop theory. Additionally, it is safe to say there are thousands of employment discrimination cases arbitrated, adjudicated at the EEOC, or litigated. Yet there is a dearth of scholarly literature. One recent study found that employment discrimination plaintiffs consistently achieve worse results in arbitration than in court in the U.S., with reduced win rates, lower monetary awards, and smaller percentages of their claimed damages (Gough, 2021).
Researchers have used content analysis in their research in multiple disciplines, including psychology, sociology, law, and political science (Choo and Ferree, 2010; Dhamoon, 2011; McAllister, 2019; Pedulla, 2014; Roberts et al., 2016; Showunmi et al., 2016). Content analysis has been utilized in studies examining accounting (Grant et al., 2018); analysis of mission statements (Law and Breznik, 2018; Lopez and Martin, 2018); health care (Cronin and Bolon, 2018; Odera et al., 2016; Dilmaghani, 2022); education (Ozdem, 2011; Wilkerson and Evans, 2018); intersection of workplace violence against women and discrimination (Chuemchit et al., 2024); and corporate social responsibility (CSR) (Campopiano and De Massis, 2015; Alfa et al., 2025). Therefore, there is significant potential for content analysis in research related to employment discrimination, particularly when using litigated cases of individuals who claim to have experienced employment discrimination, which is currently lacking in the literature.
Types of data available within the proposed databases
Researchers can use numerous existing databases to research discrimination against individuals who have reported workplace discrimination. As previously indicated, those who take action and file formal complaints of discrimination are rare (Leonard, 1984), and rulings in litigated cases can confirm whether an individual has experienced discrimination beyond claiming it. Researchers with varying levels of data analytics skills can use the databases proposed here, enabling them to research employment discrimination incidents more appropriately. Many of these data sources are readily available for little or no cost. While we describe these databases in more detail below, Table 1 provides some preliminary information about these potential data sources for employment discrimination research. Table 1 provides information describing the data sources, a description of the data contained within the data source, whether the data source is freely accessible, and whether the data source requires a subscription or other related costs. Table 1 is illustrative.
Table 1. Sources of data for employment discrimination research.
Proposed premises for future research using the recommended databases
The databases that we propose utilizing include Federally Litigated Cases, Arbitration Cases, Collective Bargaining Agreements, and Employer Codes of Conduct.
Below, we describe examples of research involving employment discrimination that can be examined utilizing the proposed data sources. Table 2 briefly lists these data sources, associated societal impetuses, potential employment discrimination topics/concepts/terms to explore, suggested testable premises, and illustrative examples of content that may be researched from the databases. These suggestions are meant to be examples and are not intended to be comprehensive.
Table 2. Examples of testable premises using suggested databases.
Examples of research using federally litigated cases
Federal cases are particularly useful to test a variety of hypotheses related to employment discrimination and retaliation. A few studies in the extant literature support this approach. For example, (Best et al. 2011) empirically tested litigated employment discrimination cases using a sampling frame of over 50,000 cases from 1965 to 1999, ultimately selecting a 2% random sample from both District Courts and Appellate courts. They concluded that individuals who file claims based on multiple bases do not fare as well as those filing claims based on a single protected demographic. Future research may examine how this result is likely to change given the increased societal impetus associated with intersectionality.
(Richardson et al. 2016b) researched cases in the federal court system to identify litigation relating to bullying. They identified ninety-three cases from nine U.S. Courts of Appeal and eighty-four U.S. District Courts. Even though there is no federal law to help prevent it, protection can be provided in the court system, if individuals can prove claims such as discrimination, hostile work environment, retaliation, harassment, violations of the Americans with Disabilities Act, infliction of emotional distress, U.S. Constitution claims, disparate treatment, impact and/or discipline, wrongful discharge or termination, or violations under the Family Medical Leave Act.
Examples of research using EEOC cases
Researchers can use EEOC cases to test hypotheses related to various types of employment discrimination. Bases for discrimination may include age, sex, race, color, religion, national origin, and disability. Recent studies that have utilized EEOC cases include (Baumle et al., 2020), who conducted a study examining 9,121 sexual orientation or gender identity discrimination charges filed with the EEOC or state or local fair employment practices agencies. They discerned trends in charge filings over time and industry patterns, issues raised, and the charges' outcomes. They specifically analyzed the differences in claim outcomes when retaliation is an issue.
(McMahon et al. 2017) conducted a study utilizing EEOC cases that looked at individuals with learning disabilities (N = 9,480) when compared to other general disabilities, such as physical, behavioral, or sensory disabilities (N = 313,480). They concluded that discrimination issues tend to involve current employees and involve harassment, intimidation, constructive discharge, and discipline. On the positive side, failure to provide reasonable accommodation and unlawful discharge are less commonplace for employees with learning disabilities.
Examples of research using arbitration cases
Researchers can use arbitration cases to examine hypotheses related to employment discrimination and procedural justice, as well as accommodations for illnesses and/or disabilities. Past studies that have utilized arbitration cases include (Colvin and Gough, 2015), who conducted a study using the American Arbitration Association (AAA) database to discern (N = 10,335) case characteristics that would lead to a settlement before the arbitration hearing. In a recent study, (Gough 2021) compared employment discrimination outcomes between litigation and arbitration cases.
Examples of research using collective bargaining agreements
Researchers can use collective bargaining agreements to examine hypotheses related to employment discrimination and issues related to transfer decisions, promotions, and constructive discharge. For example, (Riccucci and Saldivar 2014) used Westlaw to search for employment discrimination suits filed against police and fire departments due to the failure to promote between 2000 and 2011. While the authors expected to find that women and people of color are filing lawsuits against police and fire departments, they found just the opposite—most of the lawsuits filed against police and fire departments are “reverse discrimination” lawsuits filed by White men.
Examples of research using codes of conduct
To examine hypotheses related to employment discrimination and issues related to fair treatment, researchers can use employer codes of conduct. For example, (Mazza and Furlotti 2019) analyzed the codes of ethics of companies listed on the Italian stock exchange to discern content relating to employees as stakeholders. They noted that Equal Employment Opportunity statements were more likely to be included in codes of conduct in companies with low financial distress.
As demonstrated above, there have been some studies using the proposed methodology involving various data sources, which supports how the databases listed would provide experiences of discrimination. However, that an extensive search only identified a paucity of articles that have utilized this approach in over a decade warrants mentioning. This would support our contention that these databases, yet relevant, are being underutilized. This can be potentially advantageous to increasing our understanding of employment discrimination given the increasing amount of discrimination in the workplace related to the different bases of discrimination, harassment, retaliation, mutability/immutability, communications, discipline, and intersectionality.
Illustration of the process of content analysis
Utilizing the proposed databases above is particularly attractive given content analysis and the era of AI, big data analytics, and NLP (Waltermann and Leeuw, 2025; Brewster et al., 2014; Tene and Polonetsky, 2013). To illustrate how researchers can implement the analysis using the suggested databases, consider examining disparate impact in the workplace. Disparate impact may exist when a seemingly neutral policy or practice disproportionately negatively impacts protected individuals. This raises the question of whether there are protections for the bases under the law.
To discern how disparate impact has been conceptualized and researched in the literature, researchers should consult prior research on disparate impact. It is not likely that prior research will have used the recommended databases and empirical legal scholarship. This underutilization of existing databases is one of the main points of this manuscript. Researchers could test this type of hypothesis using federally litigated or arbitrated cases. The researchers should be looking for terms that will be in the case content. At this point, researchers should confer with subject matter experts to help formulate the concepts. In addition, some content analysis software enables the use of trend analysis to discern trends in the data that might not be obvious to human experts. Content analysis software provides output in the form of frequencies of terms.
Researchers can use content analysis software to query the cases for the relevant terms. In the content analysis process, it is possible to identify synonyms for the relevant terms that are coded. It is also possible to eliminate terms that do not have their intended meaning in the given context, such as the term “Black” which could be the name of an individual or a company. Then the researcher can identify the variation in the cases to test the premise. It is possible to use a graphic portrayal of the data and multivariate nonparametric statistics to analyze the data. The results of this research could be structured in such a way as to provide changes in human resource management and organizational change processes.
Discussion
Most previous publications have not used individuals who have been discriminated against as the unit of analysis, regardless of the strategy used. This is even though there are multiple sources of readily available data. There have been significant advances in methodologies, including software, that facilitates handling big data sets with more sophisticated statistical methodologies, including principal component analysis and cluster analysis. Our discipline should be more accepting of the viability and contributions of empirical legal scholarship, and the use of big data and related analytics.
In the U.S. context, given the political and legal headwinds regarding DEI and employment discrimination at both federal and organizational levels, our proposed approach offers researchers a pathway to continue advancing our understanding of employment discrimination. These databases capture the experiences of individuals and collective aggregate populations who have navigated the formal legal system, providing insights that remain accessible despite these contextual changes.
The proposed research approach of using empirical legal scholarship and content analysis to enhance employment discrimination research not only contributes to practice, but also to theory. Specifically, researchers can apply a grounded theory methodology given the proposed qualitative research methods approach (Morse, 2009). This enables the discovery of inductive theory (Wiesche et al., 2017). Grounded theory “allows the researcher to develop a theoretical account of the general features of a topic while simultaneously grounding the account in empirical observations or data” (Martin and Turner, 1986, p. 141).
A major emphasis of this article is that employment discrimination research should be conducted on employees who have experienced discrimination. Researchers might expect some pushback due to inconsistencies in reporting. However, no method is without some limitations. This is not significantly different from data gathering issues such as interviewer bias, non-respondent bias, and coding errors. Moreover, experimental designs may contain contaminating variables that are not controlled. Scholars recognize that using litigants in the sample represents the extreme end, when other internal dispute resolution mechanisms have failed. However, given the large sample sizes that are possible with these databases, knowledgeable court staff can review case write-ups to obtain accurate data. Also, the auto-coding and the analysis of trends in NVivo (for example). Auto-coding and trend analysis in NVivo (for example) increase insight into the data and reduce human coding errors.
Data analytics is offering researchers of employment discrimination the opportunity to research the complexity of discrimination in a systematic, scientific, and comprehensive manner. Based on an examination of the literature, experimental design and surveys are two methodologies that have been utilized extensively by management researchers.
Multiple case studies, analyzing one or a few cases at a time, have been used significantly by legal researchers. Yet, big data analytics allows for both managerial and legal issues to be examined more comprehensively based on real instances of discrimination.
Limitations
The proposed methodology faces challenges, including the fact that some cases remain unpublished, making it unclear how well the published articles are representative. It has been contended that there are other methodological issues, such as the fact that the write-up of a case is post hoc, there are changes in the law over time, and researchers use inappropriate statistics and data aggregation methods. Additionally, safeguards are needed to protect subjects, especially when databases are shared.
Research context
The U.S.-specific nature of this research presents both strengths and limitations. While the federal legal framework and multi-tiered dispute resolution system create comprehensive databases, the findings may have limited generalizability to other national contexts, cultural norms, or discrimination complaint processes. Additionally, the current political climate's impact on DEI programs and discrimination reporting may influence the types and frequencies of cases appearing in these databases, potentially creating temporal bias that researchers must consider when interpreting trends and outcomes.
Published vs. unpublished cases
Published vs. unpublished cases as a concern is not dissimilar from non-respondents in a survey. In fact, this is not a new issue (Siegelman and Donohue, 1990; Swenson, 2004). This concern is more or less accepted as a limitation. Some publications have offered insight into unpublished cases (McAllister, 2019) and some researchers have been able to combine both published and unpublished cases in their analysis (Cooper and Barrett, 1984). There is even a contention that there is discrimination in how decisions are made regarding which cases get published and which cases do not get published (Tillman and Hinkle, 2018).
Issues with policy capturing
Ultimately, the methodology involved in this proposal is a form of policy capturing, which has been linked with contributions and limitations regardless of the subject matter. Early research has cautioned using cases for policy capturing (Roehling, 1993). In some regards, this caution can be briefly summarized as follows: The write-ups of the cases are post hoc, the evolving nature of the laws present sampling problems, the statistics involved may be too simple at times and too complex at other times, there can be systematic sampling biases, and there may be incorrect aggregation of the data.
Yet, we believe that these cautions are controllable in the data collection and analysis process. First and foremost, one has to take the position that while not all cases are published, the ones that are published have a greater impact on future litigation and policy-making. Additionally, the cases are frequently reviewed by more than one individual before publication. They frequently have expert witnesses, friends of the courts, and other documents to supplement the judges' rulings. One way of overcoming changes in the law over time is to have a shorter sampling time frame, and ideally, a more recent time frame. Additionally, the more widespread use of nonparametric statistics can overcome some of the statistical issues. Moreover, since (Roehling 1993), authors have contended (and reviewers and editors have agreed) that these are not insurmountable challenges (Clermont and Schwab, 2009; Goldhaber et al., 2016; Johnson et al., 2008; Knapp and Heshizer, 2001; McMullen, 2016; Zschirnt, 2019; Hajnal and Scharle, 2023).
Methodological progress
Methodologists have been working to develop techniques to improve research using policy capturing (Aiman-Smith et al., 2002; Karren and Barringer, 2002) and large databases. As with other methodologies, methodological issues are consistently being addressed by methodologists, such as: the handling of missing data (Bonaccio and Dalal, 2010); the issue of socially desirable responses in self-reporting (Tomassetti et al., 2016), the issue of multi-collinearity (Paetzold, 1992), and the ethics in the use of correspondence studies (Zschirnt, 2019). Methodologists have also worked on issues relating to large databases (Ross et al., 2018). These authors have pointed out the challenge of sharing of data sets and still maintaining the privacy of the research participants (Ross et al., 2018).
It should be noted that some of these concerns are not applicable to the methodological approaches being discussed in this manuscript. There are absolutely no concerns regarding social desirability, the ethics of correspondence studies (Zschirnt, 2019) and the protection of subjects. Moreover, the data in these databases are considered archival, and an internal review by an Internal Review Board (IRB) is typically not required. In some of the databases, however, some identifying data is redacted.
Future recommendations
First and foremost, our recommendation would be to use the large databases that are readily available, some of which are free, to research employment discrimination. We have identified at least 25 sources of this data type, but surely there are more. Additionally, we have suggested approaches to developing testable hypotheses utilizing the recommended data sources.
Future research should examine how current political and organizational headwinds on DEI programs and labor immigration policies influence discrimination patterns, complaint-filing behaviors, and case outcomes with these databases. Additionally, comparative studies examining discrimination patterns before, during, and after major events could provide valuable insights into how policy environments shape employment discrimination experiences.
Researchers in our discipline should learn from researchers' experiences in other disciplines, especially psychology, sociology, political science, gender studies, and law. It should be noted that some of these publications are in the top journals of their respective disciplines or subdisciplines.
Conclusion
The problem of improving research in employment discrimination has been addressed for at least the past 15 years. It is a multinational issue. Although the focus of this research has primarily been on data collection in databases in the U.S., (Foster and Williams 2011) noted the existence of intersectionality and its legal aspects in employment discrimination research in Great Britain. Moreover, the suggestions, perhaps even the admonishments, have considered broadening the categories on which the discrimination could be based, i.e., intersectional bases, and including contextual factors.
Cortina and Kirkland's (2018) perspective is that intersectionality and what they entitle double jeopardy (meaning employment discrimination disputes filed on more than one basis), persist in complicating discrimination research. However, this double jeopardy reflects reality (Sicenica, 2023). They suggest new approaches to conducting the research. These include newer, interdisciplinary fields that offer boundary-spanning vantage points, promising to move discrimination research in new directions. These include such fields as…sociolegal studies…and disability studies.
(Outtz 2018) considered whether scholarly work in discrimination can make a practical difference. He noted that much existing research on discrimination does not give sufficient consideration to the factors that underlie discrimination or defines the target group too narrowly.
(Triana et al. 2021) recommended expanding the focus of discrimination research to other target categories (i.e., outside of sex and race) that have received comparably less attention. Additionally, they suggest broadening the scope of discrimination research beyond commonly studied contexts (e.g., recruiting, selection, and pay) to examine where, when, and how discrimination occurs more covertly.
(Stainback 2018) sums it up perfectly: “Our ability to move beyond a description of what happens to a focus on identifying the factors that might be most effective in reducing status-linked inequalities would renew the relevance of academic scholarship for real-world workplaces” (p. 53).
In conclusion, we believe that research on employment discrimination can be enhanced by utilizing empirical legal scholarship and content analysis software to analyze large, publicly available databases. Recent enhanced societal awareness of the subjects of discrimination and harassment, as evidenced by reported incidents and multimillion- dollar lawsuits, would allow the proposed methodologies to make significant contributions to the understanding, theory, practice, and potential avoidance of employment discrimination.
Author contributions
YL: Formal analysis, Writing – original draft, Data curation, Writing – review & editing, Conceptualization. HL: Formal analysis, Data curation, Writing – review & editing, Conceptualization, Writing – original draft. WM: Writing – review & editing, Writing – original draft, Conceptualization, Formal analysis.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation of this manuscript.
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References
Aiman-Smith, L., Scullen, S. E., and Barr, S. H. (2002). Conducting studies of decision making in organizational contexts: a tutorial for policy-capturing and other regression-based techniques. Organ. Res. Methods 5, 388–414. doi: 10.1177/109442802237117
Alfa, A. A., Aryee, R., Manu, F., Hinson, R., and Cherian, J. (2025). Scientometric and content analysis-based review of employees in micro-level corporate social responsibility research. Corp. Soc. Responsib. Environ. Manag. 32, 1945–1969. doi: 10.1002/csr.2997
Allen, D., and Blackham, A. (2018). Using empirical research to advance workplace equality law scholarship: benefits, pitfalls and challenges. Griffith Law Rev. 27, 337–365. doi: 10.1080/10383441.2018.1525512
Alper, B. A., Silver, L., and Mohamed, B. (2024). Rising Numbers of Americans Say Jews and Muslims Face a Lot of Discrimination. Pew Research Center Report. Available online at: https://www.pewresearch.org/2024/04/02/rising-numbers-of-americans-say-jews-and-muslims-face-a-lot-of-discrimination/ (Accessed July 28, 2025).
Baert, S. (2018). “Hiring discrimination: an overview of (almost) all correspondence experiments since 2005,” in Audit Studies: Behind the Scenes with Theory, Method, and Nuance, Ed. S. M. Gaddis (Cham: Springer) 63–77.
Bailey, J., Steeves, V., Burkell, J., and Regan, P. (2019). Getting at equality: research methods informed by the lessons of intersectionality. Int. J. Qual. Methods 18, 1–13. doi: 10.1177/1609406919846753
Banks, G. C., Woznyj, H. M., Wesslen, R. S., and Ross, R. L. (2018). A review of best practice recommendations for text analysis in R (and a user-friendly app). J. Bus. Psychol. 33, 445–459. doi: 10.1007/s10869-017-9528-3
Baumle, A. K., Badgett, M. L., and Boutcher, S. (2020). New research on sexual orientation and gender identity discrimination: effect of state policy on charges filed at the EEOC. J. Homosex. 67, 1135–1144. doi: 10.1080/00918369.2019.1603494
Best, R. K., Edelman, L. B., Krieger, L. H., and Eliason, S. R. (2011). Multiple disadvantages: an empirical test of intersectionality theory in EEO litigation. Law Soc. Rev. 45, 991–1025. doi: 10.1111/j.1540-5893.2011.00463.x
Bétaille, J. (2025). From doctrine to data: towards an empirical turn in European legal scholarship. Eur. J. Empir. Leg. Stud. 2, 1–13. doi: 10.62355/ejels.25120
Blackham, A. (2019). Empirical research and workplace discrimination law. Brill Res. Perspect. Comp. Discrimin. Law 3, 1–60. doi: 10.1163/24522031-12340006
Bonaccio, S., and Dalal, R. S. (2010). Evaluating advisors: a policy-capturing study under conditions of complete and missing information. J. Behav. Decis. Mak. 23, 227–249. doi: 10.1002/bdm.649
Bornstein, S. (2021). Disclosing discrimination. Boston Univ. Law Rev. 101, 287–358. Available online at: https://scholarship.law.ufl.edu/facultypub/1047/ (Accessed April 2, 2025).
Brewster, M. E., Velez, B. L., Mennicke, A., and Tebbe, E. (2014). Voices from beyond: a thematic content analysis of transgender employees' workplace experiences. Psychol. Sex. Orientat. Gend. Divers. 1, 159–169. doi: 10.1037/sgd0000030
Cahoy, D. R. (2010). Considerations in the rise of empirical legal scholarship. Am. Bus. Law J. 47, v–ix. doi: 10.1111/j.1744-1714.2010.01098.x
Campopiano, G., and De Massis, A. (2015). Corporate social responsibility reporting: a content analysis in family and non-family firms. J. Bus. Ethics 129, 511–534. doi: 10.1007/s10551-014-2174-z
Cech, E. A. (2024). Lowering their meritocratic blinders: White men's harassment experiences and their recognition and reporting of workplace race and gender bias. Am. J. Sociol. 129, 1033–1083. doi: 10.1086/728738
Chan, P. E., Hakala, A., Katsiyannis, A., Counts, J., and Carlson, A. (2022). Litigation on accommodating teachers with disabilities. J. Disabil. Policy Stud. 33, 112–121. doi: 10.1177/10442073211036899
Choo, H. Y., and Ferree, M. M. (2010). Practicing intersectionality in sociological research: a critical analysis of inclusions, interactions, and institutions in the study of inequalities. Sociol. Theory 28, 129–149. doi: 10.1111/j.1467-9558.2010.01370.x
Chuemchit, M., Linn, N., Han, C. P. P., Tun, K. M., Mon, M. M., Nway, N. C., et al. (2024). Discrimination and violence against women migrant workers in Thailand during the COVID-19 pandemic: a mixed-methods study. PLoS ONE 19:e0300388. doi: 10.1371/journal.pone.0300388
Clermont, K. M., and Schwab, S. J. (2004). How employment discrimination plaintiffs fare in federal court. J. Empir. Leg. Stud. 1, 429–458. doi: 10.1111/j.1740-1461.2004.00013.x
Clermont, K. M., and Schwab, S. J. (2009). Employment discrimination plaintiffs in federal court: from bad to worse? Harv. Law Policy Rev. 3, 103–132. Available online at: https://heinonline.org/HOL/P?h=hein.journals/harlpolrv3&i=105 (Accessed April 2, 2025).
Colvin, A. J. S., and Gough, M. (2015). Individual employment rights arbitration in the United States: actors and outcomes. ILR Rev. 68, 1019–1042. doi: 10.1177/0019793915591984
Conklin, M., and Houston, C. (2025). Measuring the rapidly increasing use of artificial intelligence in legal scholarship. St. Mary's J. Leg. Malpract. Ethics 15, 309–320. Available online at: https://commons.stmarytx.edu/lmej/vol15/iss2/4 (Accessed April 2, 2025).
Cooper, E. A., and Barrett, G. V. (1984). Equal pay and gender: implications of court cases for personnel practices. Acad. Manag. Rev. 9, 84–94. doi: 10.2307/258235
Cortina, L. M., and Kirkland, A. (2018). “Looking forward: what lies ahead in employment discrimination research?,” in The Oxford Handbook of Workplace Discrimination, Eds. A. J. Colella and E. B. King (Oxford: Oxford University Press) 435–442.
Cronin, C. E., and Bolon, D. S. (2018). Comparing hospital mission statement content in a changing healthcare field. Hosp. Top. 96, 28–34. doi: 10.1080/00185868.2017.1366188
Cuadraz, G. H., and Uttal, L. (1999). Intersectionality and in-depth interviews: methodological strategies for analyzing race, class, and gender. Race Gend. Class 6, 156–186.
Dahl, G. B., and Knepper, M. M. (2021). Why is Workplace Sexual Harassment Underreported? The Value of Outside Options amid the Threat of Retaliation. NBER Working Paper No. w29248. National Bureau of Economic Research. Available online at: https://doi.org/10.3386/w29248 (accessed July 25, 2025).
D'Ancona, M. Á. C. (2017). Measuring multiple discrimination through a survey-based methodology. Soc. Sci. Res. 67, 239–251. doi: 10.1016/j.ssresearch.2017.04.006
Derous, E., and Pepermans, R. (2019). Gender discrimination in hiring: intersectional effects with ethnicity and cognitive job demands. Arch. Sci. Psychol. 7, 40–51. doi: 10.1037/arc0000061
Dhamoon, R. K. (2011). Considerations on mainstreaming intersectionality. Polit. Res. Q. 64, 230–243. doi: 10.1177/1065912910379227
Diaz, I., and Bergman, M. E. (2013). It's not us, it's you: why isn't research on minority workers appearing in our “top-tier” journals? Ind. Organ. Psychol. 6, 70–75. doi: 10.1111/iops.12010
Dilmaghani, M. (2022). The link between smoking, drinking and wages: health, workplace social capital or discrimination? Ind. Relat. J. 53, 160–183. doi: 10.1111/irj.12361
Einola, K., and Alvesson, M. (2021). Behind the numbers: questioning questionnaires. J. Manag. Inq. 30, 102–114. doi: 10.1177/1056492620938139
Equal Employment Opportunity Commission (EEOC) (2025). Available online at: https://www.eeoc.gov/ (Retrieved April 2, 2025).
Evans, M., McIntosh, W., Lin, J., and Cates, C. (2007). Recounting the courts? Applying automated content analysis to enhance empirical legal research. J. Empir. Leg. Stud. 4, 1007–1039. doi: 10.1111/j.1740-1461.2007.00113.x
Executive Order 14173 (2025). Ending Illegal Discrimination and Restoring Merit-Based Opportunity. January 21.
Fluit, S., Cortés-García, L., and von Soest, T. (2024). Social marginalization: a scoping review of 50 years of research. Humanit. Soc. Sci. Commun. 11:794. doi: 10.1057/s41599-024-04210-y
Foster, D., and Williams, L. (2011). “The past, present and future of workplace equality agendas: problems of intersectionality in theory and practice,” in Reassessing the Employment Relationship, (London: Palgrave Macmillan) 318–341.
George, T. E. (2006). An empirical study of empirical legal scholarship: the top law schools. Indiana Law J. 81, 141. Available online at: https://heinonline.org/HOL/P?h=hein.journals/indana81&i=151 (Accessed April 2, 2025).
Gibbs, G. R. (2014). Computer Assisted Qualitative Data Analysis: NVivo, MaxQDA, AtlasTI, Qdaminer, HyperResearch. Presentation at the Institute for Manufacturing's 21st Annual Research Methodology Workshop, University of Cambridge.
Goldhaber, D., Lavery, L., and Theobald, R. (2016). Inconvenient truth? Do collective bargaining agreements help explain the mobility of teachers within school districts? J. Policy Anal. Manag. 35, 848–880. doi: 10.1002/pam.21914
Gough, M. (2021). A tale of two forums: employment discrimination outcomes in arbitration and litigation. ILR Rev. 74, 875–897. doi: 10.1177/0019793920915876
Grant, S. M., Hodge, F. D., and Sinha, R. K. (2018). How disclosure medium affects investor reactions to CEO bragging, modesty, and humblebragging. Account. Organ. Soc. 68–69, 118–134. doi: 10.1016/j.aos.2018.03.006
Hajnal, Á., and Scharle, Á. (2023). What works for whom? What theories may predict about the effectiveness of measures against employment discrimination. Int. J. Sociol. Soc. Policy 43, 1162–1179. doi: 10.1108/IJSSP-12-2022-0318
Heise, M. (2002). The Past, Present, and Future of Empirical Legal Scholarship: Judicial Decision Making and the New Empiricism. University of Illinois Law Review. 819–850. Available online at: https://heinonline.org/HOL/P?h=hein.journals/unilllr2002&i=833 (Accessed April 2, 2025).
Irvine, K., Hoffman, D. A., and Wilkinson-Ryan, T. (2018). Law and psychology grows up, goes online, and replicates. J. Empir. Leg. Stud. 15, 320–355. doi: 10.1111/jels.12180
Jackson, K., and Bazeley, P. (2019). Qualitative Data Analysis with NVivo. London: Sage Publications Limited.
Johnson, S. W., Stidham, R., Carp, R. A., and Manning, K. L. (2008). The gender influence on U.S. district court decisions: updating the traditional judge attribute model. J. Women Polit. Policy 29, 497–526. doi: 10.1080/15544770802092675
Joseph, D. L., and Rousis, G. J. (2013). Casting a wider net: recommendations for the study of broad discrimination experiences. Ind. Organ. Psychol. 6, 88–92. doi: 10.1111/iops.12014
Kalpokaite, N., and Radivojevic, I. (2019). “Researching and engaging with the computer-assisted qualitative data analysis software Atlas.TI,” in Proceedings of the 3rd World Conference on Qualitative Research, (Cham: Springer) 120–121.
Karren, R. J., and Barringer, M. W. (2002). A review and analysis of the policy-capturing methodology in organizational research: guidelines for research and practice. Organ. Res. Methods 5, 337–361. doi: 10.1177/109442802237115
Katz, M., and LaVan, H. (2023). Unions' role in intersectional age discrimination litigation cases. Empl. Relat. 45, 328–344. doi: 10.1108/ER-09-2021-0394
Kazyak, E., Burke, K., Oliver, M., and Behrendt, M. (2023). Making the case: examining outcomes of religious-based claims in federal litigation involving LGBT rights. Sex. Res. Soc. Policy 20, 1393–1406. doi: 10.1007/s13178-023-00812-4
Kleps, C. (2022). Race, gender, and place: how judicial identity and local context shape anti-discrimination decisions. Law Soc. Rev. 56, 188–212. doi: 10.1111/lasr.12606
Knapp, D. E., and Heshizer, B. P. (2001). Outcomes of requests for summary judgments in federal sexual harassment cases: policy capturing revisited. Sex Roles 44, 109–128. doi: 10.1023/A:1010972718542
Kohlbacher, F. (2006). The use of qualitative content analysis in case study research. Use Qual. Content Anal. Case Study Res. 7, 1–30. doi: 10.17169/fqs-7.1.75
Köllen, T. (2021). Diversity management: a critical review and agenda for the future. J. Manag. Inq. 30, 259–272. doi: 10.1177/1056492619868025
LaVan, H., and Martin, W. M. (2024). Inequality, mutability and intersectionality differences in discrimination in the U.S. federal public employment sector. Empl. Responsib. Rights J. 36, 101–127. doi: 10.1007/s10672-023-09437-6
Law, K. M. Y., and Breznik, K. (2018). What do airline mission statements reveal about value and strategy? J. Air Transp. Manag. 70, 36–44. doi: 10.1016/j.jairtraman.2018.04.015
Lee Badgett, M. V., Baumle, A., and Boutcher, S. (2018). Evidence from the frontlines on sexual orientation and gender identity discrimination. doi: 10.2139/ssrn.3407967
Lee, G., and Fargher, N. (2013). Companies' use of whistle-blowing to detect fraud: an examination of corporate whistle-blowing policies. J. Bus. Ethics 114, 283–295. doi: 10.1007/s10551-012-1348-9
Leonard, J. S. (1984). The impact of affirmative action on employment. J. Labor Econ. 2, 439–463. doi: 10.1086/298041
Leslie, M., Strauser, D. R., McMahon, B. T., Greco, C., and Rumrill, P. D. (2020). The workplace discrimination experiences of individuals with cancer in the Americans with Disabilities Act Amendments Act era. J. Occup. Rehabil. 30, 115–124. doi: 10.1007/s10926-019-09851-4
Lindsey, A., King, E., McCausland, T., Jones, K., and Dunleavy, E. (2013). What we know and don't: eradicating employment discrimination 50 years after the Civil Rights Act. Ind. Organ. Psychol. 6, 391–413. doi: 10.1111/iops.12075
Lock, I., and Seele, P. (2015). Quantitative content analysis as a method for business ethics research. Bus. Ethics Eur. Rev. 24, S24–S40. doi: 10.1111/beer.12095
Lockwood, G., Rosenthal, P., and Budjanovcanin, A. (2011). A quantitative and qualitative analysis of sexual harassment claims 1995–2005. Ind. Relat. J. 42, 86–103. doi: 10.1111/j.1468-2338.2010.00599.x
Lopez, Y. P., and Martin, W. F. (2018). University mission statements and sustainability performance. Bus. Soc. Rev. 123, 341–368. doi: 10.1111/basr.12144
Martin, P. Y., and Turner, B. A. (1986). Grounded theory and organizational research. J. Appl. Behav. Sci. 22, 141–157. doi: 10.1177/002188638602200207
Martin, W. M., and LaVan, H. (2010). Workplace bullying: a review of litigated cases. Empl. Responsib. Rights J. 22, 175–194. doi: 10.1007/s10672-009-9140-4
Maynard, D. C., and Ferdman, B. M. (2009). The marginalized workforce: how I-O psychology can make a difference. Ind. Organ. Psychol. 46, 25–29. doi: 10.1037/e578882011-004
Mazza, T., and Furlotti, K. (2019). Quality of code of ethics: an empirical analysis on the stakeholder employee. Soc. Responsib. J. 16, 1109–1123. doi: 10.1108/SRJ-03-2019-0113
McAllister, M. C. (2019). Extending the sex-plus discrimination doctrine to age discrimination claims involving multiple discriminatory motives. Boston Coll. Law Rev. 60, 469–508. Available online at: https://heinonline.org/HOL/P?h=hein.journals/bclr60&i=481 (Accessed April 2, 2025).
McMahon, M., McMahon, B. T., West, S. L., Armstrong, A. J., and Belongia, L. (2017). Actual vs. perceived workplace discrimination involving charging parties with learning disabilities: the National EEOC ADA Research Project. J. Vocat. Rehabil. 46, 203–208. doi: 10.3233/JVR-160856
McMullen, A. L. (2016). The impact of recent employment-discrimination decisions. J. Entrep. Public Policy 5, 113–120. doi: 10.1108/JEPP-03-2014-0016
Miner, A. S., Stewart, S. A., Halley, M. C., Wallace, E., Frioux, S., Bernier, A., et al. (2023). Formally comparing topic models and human-generated qualitative coding of physician mothers' experiences of workplace discrimination. Big Data Soc. 10:20539517221149106. doi: 10.1177/20539517221149106
Monster Poll (2023). Workplace Discrimination. Available online at: https://hiring.monster.com/resources/blog/monster-poll-workplace-discrimination/ (Accessed July 28, 2025).
Mooney, S. (2016). ‘Nimble' intersectionality in employment research: a way to resolve methodological dilemmas. Work Employ. Soc. 30, 708–718. doi: 10.1177/0950017015620768
Morse, J. M. (2009). “Tussles, tensions, and resolutions,” in Developing Grounded Theory: The Second Generation, Eds. J. M. Morse, P. N. Stern, J. Corbin, B. Bowers, K. Charmaz, and A. E. Clarke (New York, NY: Routledge) 13–22.
Movahed, M., and Hirsh, E. (2023). Mobilizing equal employment rights: the social and political determinants of discrimination complaints (2009–2018). Sociol. Q. 64, 296–319. doi: 10.1080/00380253.2022.2099998
Nikolaou, D. (2022). Same-sex marriage laws, LGBT hate crimes, and employment discrimination charges. South. Econ. J. 88, 869–905. doi: 10.1002/soej.12548
Odera, O., Scott, A. H. S., and Gow, J. (2016). Factors influencing corporate social and environmental disclosures: a systematic review. Int. J. Bus. Gov. Ethics 11, 116–134. doi: 10.1504/IJBGE.2016.078199
Outtz, J. L. (2018). “Can scholarly works on discrimination make a practical difference?,” in The Oxford Handbook of Workplace Discrimination, Eds. A. J. Colella and E. B. King (Oxford: Oxford University Press) 419–422.
Ovádek, M., Schroeder, P., and Zglinski, J. (2024). Where law meets data: a practical guide to expert coding in legal research. Eur. Law Open 3, 820–848. doi: 10.1017/elo.2024.23
Ozdem, G. (2011). An analysis of the mission and vision statements on the strategic plans of higher education institutions. Educ. Sci. Theory Pract. 11, 1887–1894. Available online at: https://files.eric.ed.gov/fulltext/EJ962679.pdf (Accessed April 2, 2025).
Paetzold, R. L. (1992). Multicollinearity and the use of regression analyses in discrimination litigation. Behav. Sci. Law 10, 207–228. doi: 10.1002/bsl.2370100206
Pedulla, D. S. (2014). The positive consequences of negative stereotypes: race, sexual orientation, and the job application process. Soc. Psychol. Q. 77, 75–94. doi: 10.1177/0190272513506229
Phillips, D. J., and Ranganathan, A. (2025). Addressing marginalized populations in management research. Adm. Sci. Q. 00018392251347282. doi: 10.1177/00018392251347282
Posthuma, R. A., Flores, G. L., Dworkin, J. B., and Useem, M. (2016). Social context and employment lawsuit dispute resolution. Int. J. Confl. Manag. 27, 547–569. doi: 10.1108/IJCMA-10-2015-0072
RG and GR Harris Funeral Homes Inc. V. Equal Employment Opportunity Commission. (2019). Supreme Court 1599.
Riccucci, N. M., and Saldivar, K. (2014). The status of employment discrimination suits in police and fire departments across the United States. Rev. Public Pers. Adm. 34, 263–288. doi: 10.1177/0734371X12449839
Richardson, R. E., Hall, R., and Joiner, S. (2016a). Workplace bullying in the United States: an analysis of state court cases. Cogent Bus. Manag. 3:1256594. doi: 10.1080/23311975.2016.1256594
Richardson, R. E., Joiner, S., and Hall, R. (2016b). The status of workplace bullying in federal court cases. Acad. Bus. Res. J. 1, 117–131.
Roberts, L. M., and Nkomo, S. M. (2025). Navigating marginalized identities in diverse organizations. Curr. Opin. Psychol. 62:101983. doi: 10.1016/j.copsyc.2024.101983
Roberts, T.-A., Curtin, N., Duncan, L. E., Cortina, L. M., Lardon, C., Babcock, L., et al. (2016). Feminist Perspectives on Building a Better Psychological Science of Gender. Cham: Springer.
Roehling, M. V. (1993). “Extracting” policy from judicial opinions: the dangers of policy capturing in a field setting. Pers. Psychol. 46, 477–502. doi: 10.1111/j.1744-6570.1993.tb00881.x
Ross, M. W., Iguchi, M. Y., and Panicker, S. (2018). Ethical aspects of data sharing and research participant protections. Am. Psychol. 73, 138–145. doi: 10.1037/amp0000240
Ruggs, E. N., Hebl, M. R., Law, C., and Cox, C. B. (2013). Gone fishing: I–O psychologists‘ missed opportunities to understand marginalized employees' experiences with discrimination. Ind. Organ. Psychol. 6, 39–60. doi: 10.1111/iops.12007
Ruiz Castro, M., and Holvino, E. (2016). Applying intersectionality in organizations: inequality markers, cultural scripts and advancement practices in a professional service firm. Gend. Work Organ. 23, 328–347. doi: 10.1111/gwao.12129
Rumrill, P. D., Zhang, H., Li, J., Leslie, M., McMahon, B. T., Bishop, M., et al. (2022). Workplace discrimination allegations and outcomes involving Caucasian Americans, African Americans, and Hispanic/Latinx Americans with multiple sclerosis: a causal comparative analysis. J. Vocat. Rehabil. 56, 93–106. doi: 10.3233/JVR-211175
Sawyer, K., Salter, N. P., and Thoroughgood, C. N. (2013). Studying individual identities is good, but examining intersectionality is better. Ind. Organ. Psychol. 6, 80–84. doi: 10.1111/iops.12012
Showunmi, V., Atewologun, D., and Bebbington, D. (2016). Ethnic, gender and class intersections in British women's leadership experiences. Educ. Manag. Adm. Leadersh. 44, 917–935. doi: 10.1177/1741143215587308
Sicenica, A. M. (2023). Increasing representation: expanding intersectional claims in employment discrimination. Duquesne Law Rev. 61, 341–364. Available online at: https://heinonline.org/HOL/P?h=hein.journals/duqu61&i=353 (Accessed April 2, 2025).
Siegelman, P., and Donohue, J. J. I. I. I. (1990). Studying the iceberg from its tip: a comparison of published and unpublished employment discrimination cases. Law Soc. Rev. 24, 1133–1170. doi: 10.2307/3053664
Stainback, K. (2018). “Organizations, employment discrimination, and inequality,” in The Oxford Handbook of Workplace Discrimination, Eds. A. J. Colella and E. B. King (Oxford: Oxford University Press) 41–58.
Styhre, A., and Eriksson-Zetterquist, U. (2008). Thinking the multiple in gender and diversity studies: examining the concept of intersectionality. Gend. Manag. 23, 567–582. doi: 10.1108/17542410810912690
Swenson, K. D. (2004). Federal district court judges and the decision to publish. Justice Syst. J. 25, 121–142. doi: 10.1080/0098261X.2004.10767714
Tene, O., and Polonetsky, J. (2013). A theory of creepy: technology, privacy and shifting social norms. Yale J. Law Technol. 16, 59–102. Available online at: https://heinonline.org/HOL/P?h=hein.journals/yjolt16&i=59 (Accessed April 2, 2025).
Terpstra, D. E., and Honorée, A. L. (2016). Differences in the nature of employment discrimination litigation between private sector organizations and public sector organizations. Int. J. Discrimin. Law 16, 200–213. doi: 10.1177/1358229116645677
Thompson, J. L., and Morris, S. B. (2013). What factors influence judges' rulings about the legality of affirmative action plans? J. Bus. Psychol. 28, 411–424. doi: 10.1007/s10869-013-9292-y
Tillman, E. A., and Hinkle, R. K. (2018). Of whites and men: how gender and race impact authorship of published and unpublished opinions in the U.S. courts of appeals. Res. Polit. 5:2053168018762869. doi: 10.1177/2053168018762869
Tomassetti, A. J., Dalal, R. S., and Kaplan, S. A. (2016). Is policy capturing really more resistant than traditional self-report techniques to socially desirable responding? Organ. Res. Methods 19, 255–285. doi: 10.1177/1094428115627497
Triana, M. D. C., Gu, P., Chapa, O., Richard, O. C., and Colella, A. (2021). Sixty years of discrimination and diversity research in human resource management: a review with suggestions for future research directions. Hum. Resour. Manag. 60, 145–204. doi: 10.1002/hrm.22052
Vogel, F., Hamann, H., and Gauer, I. (2018). Computer-assisted legal linguistics: corpus analysis as a new tool for legal studies. Law Soc. Inq. 43, 1340–1363. doi: 10.1111/lsi.12305
Waltermann, A., and Leeuw, F. L. (2025). “Empirical legal research in a digital society: questions, approaches, examples, and a case study,” in Artificial Intelligence and Big Data, (Cheltenham: Edward Elgar Publishing) 106–125.
Wiesche, M., Jurisch, M. C., Yetton, P. W., and Krcmar, H. (2017). Grounded theory methodology in information systems research. MIS Q. 41, 685–701. doi: 10.25300/MISQ/2017/41.3.02
Wilkerson, E. A., and Evans, C. A. (2018). A content analysis of mission statements in nonprofit education. J. Nonprofit Educ. Leadersh. 8, 3–17. doi: 10.18666/JNEL-2018-V8-I1-7321
Wulff, J. N., and Villadsen, A. R. (2020). Are survey experiments as valid as field experiments in management research? An empirical comparison using the case of ethnic employment discrimination. Eur. Manag. Rev. 17, 347–356. doi: 10.1111/emre.12342
Zeiler, K. (2016). The future of empirical legal scholarship: where might we go from here? J. Leg. Educ. 66, 78–122. Available online at: https://heinonline.org/HOL/P?h=hein.journals/jled66&i=78 (Accessed April 2, 2025).
Zilic, I., and LaVan, H. (2020). Arbitration of accommodation in U.S. workplaces: employee, stakeholder and human resources characteristics. Ind. Relat. J. 51, 454–473. doi: 10.1111/irj.12308
Keywords: big data analytics, content analysis, empirical legal scholarship, employment discrimination research, mixed methods research, intersectionality, NVivo, qualitative research
Citation: Lopez YP, LaVan H and Martin WM (2025) How to advance employment discrimination research in an era of big data and analytics. Front. Sociol. 10:1605748. doi: 10.3389/fsoc.2025.1605748
Received: 29 April 2025; Accepted: 20 October 2025;
Published: 10 November 2025.
Edited by:
Cinzia Calluso, Guido Carli Free International University for Social Studies, ItalyReviewed by:
Mina Kuzminac, University of Belgrade, SerbiaAyten Ordu, Near East University, Cyprus
Ronaldo Gomes-Souza, Universidade Federal do Amazonas, Brazil
Copyright © 2025 Lopez, LaVan and Martin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Helen LaVan, aGxhdmFuQGRlcGF1bC5lZHU=