Transcript – Intersectionality and Diversity Training: How Social Identities Matter in Why People Participate in Diversity Training in Higher Education

Socially constructed identities feed into a core sense of self that impacts their perceptions of themselves and others, as well as how individuals are treated in organizations and in higher education. We examined how social identities are associated with why people engage in diversity training at the university. I now turn it over to Juliet and Danny to discuss the research methods and results.

So, the participants in our sample had varied social identities in terms of age, gender, ethnicity and educational level. Naturally, humans identify with multiple social groups at once, for example, Latina females. So, we modeled these social identity combinations and had a total of 263 clusters emerging from our data. We then collected data on people’s motivations for attending diversity training at the university, which helped us identify whether people attended training primarily because of their social values or because they wanted to manage others impressions of them, or if they were concerned about the organization.

Using multilevel modeling techniques, we found that differences in motivation for attending diversity training for social values, impression management and organizational cover was significantly explained by belonging to different social strata. Age, gender and ethnicity significantly predicted a different motivation towards diversity training, for instance, compared to people who are 18-24 years old. Older people tend to attend diversity training more because of poor social values and organizational culture, and less because of impression management.

In conclusion, our findings can inform the design and implementation of diversity and inclusion, training and higher education, particularly in approaches to motivate and engage our students, faculty and staff to participate. These are just some of the preliminary results from this project. And we have a lot more variables that we plan to analyze in the future from this data set. Thank you.