A Content Analysis of Diversity in Undergraduate I-O Psychology Textbooks

Cynthia Prehar & Patrick McGonagle, Framingham State University; & Rachael Hansen-Garshong, University of Ghana

The Society for Industrial and Organizational Psychology (SIOP) recently created the “DIP” (Diversifying I-O Psychology) Program, whose mission is to “increase diversity within the field of I-O psychology, and ultimately SIOP, by increasing the diversity of students who are applying to and accepted into funded I-O doctoral programs” (SIOP, 2022). However, amid a recent surge of anti-DEI legislation in higher education (Flannery, 2024), educators in many parts of the United States are becoming legally constrained in how explicitly inclusive they can be. This may require instructors to turn to more subtle ways of signaling to minority group members that they are appreciated, valued, and welcomed. Choosing inclusive undergraduate textbooks might be one such way to do this (see Chaney & Sanchez, 2018; Howansky et al., 2022; and Kelly & Patrice, 2019 for more on identity safety cues). 

Although multiple diversity analyses of introductory psychology textbooks have been published (e.g., Hogben & Waterman, 1997; Lonner & Murdock, 2012; Whaley et al., 2017), less is known about representation in textbooks for more specialized courses, like I-O psychology. Accordingly, the current paper includes two studies of diversity content in undergraduate industrial-organizational (I-O) psychology textbooks. The first study analyzed diversity messaging via written content. Two research questions guided our analyses in the first study:

Q1: (How) is diversity defined in undergraduate I-O psychology textbooks?

Q2: How prominent are diversity topics in undergraduate I-O psychology textbooks?

Whereas the first study analyzed text for diversity messaging, the second study focused on the textbooks’ images. In the second study, we explored the following research questions:

Q3: In undergraduate I-O textbooks’ images, what proportion of people are racially diverse, female, and depicted with visible disabilities?

Q4: How do the rates of women, people of color, and visibly disabled people in undergraduate I-O textbooks’ images compare to U.S. national averages for undergraduate college students?

Study 1 Method

Textbook Selection

To identify undergraduate I-O psychology textbooks, we consulted SIOP’s teaching resources (SIOP, 2017), college textbook publisher’s websites (e.g., McGraw Hill, Wiley), and Amazon. We also researched “older” textbooks (e.g., Schultz & Schultz, 2005) that did not appear in the first three searches.

We only included undergraduate textbooks that covered both “I” and “O” content and that were marketed toward a North American audience. This yielded eight undergraduate I-O textbooks (see Table 1).

Defining Diversity

A definition of diversity was of central importance. Because our textbooks’ definitions varied (see Table 2), we adopted one from the American Psychological Association’s (2023) Inclusive Language Guide. Text/words were considered diverse if they involved

[T]he representation or composition of various social identity groups in a work group, organization, or community. The focus is on social identities that correspond to societal differences in power and privilege, and thus to the marginalization of some groups based on specific attributes—for example, race, ethnicity, culture, gender, gender identity and expression, sexual orientation, socioeconomic status, religion, spirituality, disability, age, national origin, immigration status, and language. There is a recognition that people have multiple identities and that social identities are intersectional and have different salience and impact in different contexts. (APA, 2023, p. 3; emphasis added)

We coded content as diverse if it discussed any of the aforementioned groups. We also counted legislation and programs that address systemic discrimination, such as the Civil Rights Act and affirmative action.

Exclusion Criteria

We did not code sentences that included the word diverse, or variations thereof, if it was not clear that the authors were referring to demographic diversity. We also did not count cross-cultural content unless it highlighted underlying power differentials between groups (per the aforementioned APA definition). Last, we did not code ancillary chapter information in the first study (e.g., images, figures, tables).

Coding Procedures

Using an online sentence counter, the second author counted the total number of sentences in every textbook chapter; these served as the denominator when calculating percent diversity content in a given chapter. Concurrently, the first and second authors, one Caucasian woman and one Caucasian man, independently identified diversity content in the main text of each chapter. Once we located content, we then independently coded it for number of sentences, nature of the social group(s) represented (e.g., gender, disability, race, religions, applies to multiple groups), and chapter topic (e.g., training, motivation, job analysis). All disagreements were resolved through calibration discussions.

Study 1 Results

Research Question 1: Defining Diversity

Our first research question examined how, and if, diversity was defined in undergraduate I-O textbooks. As shown in Table 2, we could find no diversity definitions in two of the eight books. Furthermore, the term was only included in one of five glossaries (three textbooks did not have glossaries).  When the term was defined in the books (in the glossary, or elsewhere in the text), a common theme was that diversity involved demographic differences between people. Some definitions/discussions broadened the concept, however, to also include nondemographic individual differences such as abilities, values, and attitudes.

Research Question 2: The Prominence of Diversity in Text

Our second research question concerned the prominence of diversity in the text of the books. We examined this in a few ways. First, we explored whether any textbooks devoted a whole chapter to diversity; none did. Conte and Landy (2019), however, devoted an entire module to the subject (in a chapter with 3 independent modules). And Howes and Muchinsky (2022) devoted several pages to discussing diversity in a new (to this edition) chapter entitled “The Context of Work.”

Second, we examined the proportion of text devoted to diversity (see Table 3). Each book had 14–15 chapters, and across all chapters, the proportion of text devoted to diversity topics ranged from 4.3%–8.4%. Chapters with the highest proportions of diversity content were typically personnel selection (13.8–42.7%), introduction to I-O psychology (8.6%–14.2%), and leadership (4.2%–19.2%). In contrast, research methods (0–0.6%), employee motivation (0–.5%), and organizational theory/development (0–.9%) had little to no diversity content. Within chapters with the most diversity content, the protected classes most typically mentioned were sex/gender (e.g., sexual harassment, women in leadership) and multiple groups (e.g., affirmative action).

Study 2: Method

Procedure

Using the same textbooks from the first study, the second and third authors independently coded the images in all textbooks. The coders were of different sexes and racial backgrounds; the second author is a Caucasian man, and the third author is an African woman. Over the course of several months, the raters met at least once a week to calibrate their codes within a given textbook and address any discrepancies. If possible, discrepancies were resolved through discussion, with most discussions resulting in consensus. However, images with unresolved discrepancies were placed in a tiebreaker section. The first author, a Caucasian woman, utilized the Study 2 codebook to provide the tiebreak codes. Across the 2878 demographic coding decisions (929 people x 3 demographic variables), there were only 17 disagreements, resulting in 99.4% agreement between the coders.

Study 2 Codebook

Textbook images were first identified by page number. Images could include people, animals, scenery, and/or objects anywhere in a chapter. The images could be photographs and/or line drawings (e.g., cartoons). If multiple images were located on one page, they were coded from the top to bottom of the page and left to right. Similarly, if multiple people were present in a single image, they were each individually coded from top to bottom and left to right (i.e., the first person coded was nearest the top of the image and to the left-most side of it). Raters also made notes to ensure they were discussing the same images and people during their calibration meetings.

Images were first coded for their print color (black/white or in color). Then, they were evaluated for the presence of people. A person was defined as any human body that had a visible face. If images had only objects and/or nonhuman animals in them, they were not further analyzed. Each person in an image was coded for multiple demographic features (described below). However, given the importance of facial features and skin color in our operational definitions (see Table 2), a person was only coded if at least 50% of their face was visible.

To address our fourth research question, we coded the race, presence of physical disabilities, and gender of each person in the images. We chose these demographics as they were the ones we felt most confident deducing from pictures. (Accurately inferring sexual orientation or presence of invisible disabilities, like asthma or schizophrenia, would be nearly impossible from a picture alone.)

Race was the first demographic variable coded. As shown in Table 4, we primarily relied on the Massey-Martin Skin Color Scale (2003) and operational definitions adapted from Reddy-Best et al. (2018) to code people as Black, Brown, White, or Asian. There were also options for “Contains another person of color” and “Cannot determine race.”

After race, we next coded the people in the images for presence of a visible physical disability (yes or no). Physical disabilities were coded as present when observable assistive aids, such as wheelchairs, braces, canes, and/or hearing aids, existed in an image.

Our last demographic variable, gender, was coded based on operational definitions adapted from Reddy-Best et al. (2018; see Table 4). Women were defined by visible breasts, curved hips, and soft facial features, while men were defined by the presence of facial hair (e.g., a thick beard and/or mustache), angular jawline, and broad shoulders. There was also an option for “Could not determine gender.”

Study 2: Results

Research Question 3: Demographic Representation in Images

To describe representation in the textbooks’ imagery, we calculated the percentage of people depicted in each race, disability, and gender category. Across all eight textbooks, 929 people were clearly displayed in images. Of these 929 people, 626 were White (67.4%), 127 were Black (13.7%), 83 were Brown (8.9%), 88 were Asian (9.5%), 1 was another person of color (0.1%), and 4 people’s race was unable to be determined (0.4%). Table 5 shows the racial distribution by textbook. In all books, White people were the dominant racial group depicted (ranging from 51.4% to 77.4% of the people depicted in the images). In six textbooks, the second most frequently represented racial group was Black (11.3–22.9% of depictions). The least represented racial groups were Brown (3.8–9.1%; 5 books) and Asian (5.7%–11.5%; 3 books).

In terms of gender, 422 people appeared to be female (45.4%), 506 appeared to be male (54.47%), and 1 person’s gender could not be determined (0.1%). In three of the eight textbooks, however, women outnumbered men (see Table 5).

Last, only 4 (0.3%) of the 929 people shown in the textbooks’ images had a visible physical disability. As shown in Table 5, these four images were dispersed across four different textbooks.

Research Question 4: Representation Relative to Undergraduate Demographic Composition

Our final research question asked whether the rates of representation in the textbooks’ images reflected the demographic composition of the United States’ undergraduate students, on average. To explore this question, we performed chi-square goodness-of-fit analyses, and we relied on numbers from the National Center for Education Statistics (NCES) for our expected values. We chose the NCES because it is “the primary statistical agency of the U.S. Department of Education” (National Center for Education Statistics, n.d., About NCES, para 1).

Race

With respect to race, the NCES reports college enrollment rates for White, Black, Hispanic, and Asian students. We did not have a coding system for Hispanic individuals, however, and we were not confident that our codes of “Brown” skin tone exclusively reflected people of Hispanic heritage. Thus, we analyzed only the data for people coded in our study as White, Black, and Asian (n = 841). For our expected values, we utilized the undergraduate enrollment rates of White, Black, and Asian students in 2018 (National Center for Education Statistics, 2019a). The chi-square goodness-of-fit analysis for race was not significant χ² (2, n = 841) = 4.82, p = .09.  In other words, racial groups were represented in the images at proportions relatively similar to the 2018 enrollment rates in the United States’ undergraduate population (e.g., 614 White people expected in the images, 626 observed; 149 Black people expected, 127 observed).

Gender

The NCES also reports the gender breakdown of young adults enrolled in U.S. college and universities. Since 1979, women have outnumbered men in undergraduate college enrollment, and in 2018, women comprised 57% of the 18–24-year-olds enrolled in college (National Center for Education Statistics, 2019b). With a significant chi-square result, however, this trend was not reflected in our sample of textbooks published between 2018 and 2021, χ² (1, n = 928) = 50.30, p = .001. Women were depicted at lower rates than expected (422 vs. 529, respectively), and men were depicted at higher rates than expected (506 vs. 399, respectively).

Disability

Last, we had planned to compare the observed rates of people with visible physical disabilities with the expected rates of physically disabled people enrolled in college. However, because we observed fewer than five images of physically disabled people across all of our textbooks, we did not satisfy the necessary assumptions for a chi-square goodness-of-fit test (Pallant, 2013).

General Discussion

Recent SIOP attempts to make I-O psychology more inclusive inspired this work (e.g., Shyamsunder et al., 2020; Sim & Hewitt, 2023; SIOP, 2022). The American Psychological Association (APA), SIOP’s parent organization, has established similar goals (American Psychological Association, 2019, 2021; Pappas, 2022).  The study primarily concentrated on undergraduate education due to its optimal potential for attracting students from historically underrepresented groups to I-O graduate programs.  Furthermore, preliminary studies suggest that inclusive classrooms have positive effects on student achievement and attitudes. For instance, in their quasi-experiment, Kelly and Patrice (2019) manipulated diverse imagery in PowerPoint slides across four introductory psychology classes.  Black students who were exposed to Black imagery in the slides had higher final course grades than Black students in the White imagery condition. Howansky et al., (2022) also found positive effects when they manipulated various identity safety cues (ISC) in the classroom. When students perceived that their professor tried to create an inclusive environment, they reported a higher sense of belonging and fewer absences compared to the control classroom. Although neither of these studies measured the impact of an inclusive textbook, they suggest that small changes in pedagogy may have a meaningful impact on student behavior.

Overall, we found that the undergraduate I-O psychology textbooks in our sample lacked a unified definition of diversity, and none focused an entire chapter on it (cf. Conte & Landy, 2019). In addition, the chapters that covered research methods, motivation, and organizational development frequently lacked sufficient diversity material. We encourage textbook authors to adopt standardized definitions of diversity and related terms (e.g., equity, inclusion, intersectionality). Furthermore, we also recommend that authors devote an entire chapter to discussing diversity and/or that they conduct a “diversity audit” to ensure they intentionally include diversity content in all chapters.¹ For example, research studies that describe reactions to coworkers with non-normative gender identities (e.g., Waite, 2021) would provide more inclusive content in a research methods chapter.

In contrast to low levels of diversity messaging observed in the text of some books, we found adequate racial representation in the textbooks’ images (relative to the racial composition of the likely textbook consumers, i.e., undergraduate students). However, more men than women were depicted in the images, despite the fact that there are more female college students than male. Furthermore, people with visible physical disabilities were almost nonexistent in the textbooks. To the extent that textbook authors have control of the images in their textbooks, we encourage them to be mindful of selecting images with more women and more diverse body abilities.

Multiple limitations were present in this study. First, defining diversity more broadly than the APA (2019) definition would lead to more “hits.” We opted to embrace the more APA definition, however, because it centered on historically marginalized groups, the focal groups for SIOP efforts like the DIP (SIOP, 2022). Second, we only coded the main text in Study 1, excluding tables and other pedagogical aids. This choice likely led us to undercount diversity content in that study. We recommend that future researchers explore the amount of diversity content found in pedagogical aids; this research could help inform instructors how often diversity topics are centered in a given book (vs. treated as peripheral reading).

In the context of rising anti-DEI legislation, inclusive textbooks could be a subtle, yet powerful, way to attract minoritized undergraduates to the I-O profession. Of course, textbook selection is based on many factors, and we encourage instructors to consider diversity content as they contemplate their next I-O psychology textbook selection. We hope that these findings help instructors in that endeavor as well as provide textbook authors with insights on how to make their next editions more inclusive.

Note

^[1] For more guidance on diversity audits, we recommend the Racial Equity Course Review from the University of Pennsylvania’s Center for Excellence in Teaching, Learning and Innovation (n.d.) website and the preface of Riggio and Johnson (2022).

References

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Table 1

 Textbooks Examined in Studies 1 & 2 (in alphabetical order by first author)

Table 2

 Textbooks’ Diversity Definitions

Table 4

Table 5

Demographic Representation of People in the Textbooks’ Images

 Note. The first number in each cell represents the # of people who were coded with that demographic. Then we divided that number by the total # of people observed in the book to calculate the ensuing percentages.