ABSTRACT
As the United States grows more racially diverse, it is imperative to understand whether being in a racially diverse environment impacts conversations about race. This study examines whether exposure to, and interactions with racially diverse others relate to whether people talk about race, the frequency with which people talk about race, and their comfort with doing so within the racially diverse context of Hawaii. We employed experience sampling to measure whether people had conversations about race, how frequently conversations about race occurred and their comfort in those conversations, and whether their exposure to and interactions with racially diverse others predicted these behaviors. Exposure to and interactions with racially diverse others were not significant predictors of race-related conversations (and their comfort with said conversations). However, interactions with racially diverse friends was related to greater likelihood of discussing race, more frequent discussions of race, and more comfort with race-related conversations. These findings illustrate the importance that interactions with cross-race friends have for improving intergroup relations.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data described in this article are openly available in the Open Science Framework at https://doi.org/10.17605/OSF.IO/S248G.
Open scholarship
This article has earned the Center for Open Science badges for Open Data and Open Materials through Open Practices Disclosure. The data and materials are openly accessible at https://doi.org/10.17605/OSF.IO/S248G.
Notes
1. We specifically chose to ask participants about racially/ethnically diverse others due to the importance of ethnic diversity in Hawaii (Bocher & Ohsako, Citation1977; Newton et al., Citation1988; Okamura, Citation1994)
2. Although surveys could also be nested within day, we found that the three-level nested structure resulted in a singular model, indicating that this three-level data structure was too complex to be supported by the data. In addition, a cross-classified model indicated day (0.02% of variance) and beep (i.e., morning or evening survey; 2.18% of variance) accounted for a negligible amount of variance and were thus omitted from the model.
3. Results were identical when running models with an autoregressive covariance structure, Likelihood Ratio Test = 2.40, p= .121.
4. We conducted initial analyses for Level 1 predictors across participants who completed the final backend survey vs. not and found there were no significant differences among our main variables of interest (mentioning race, frequency of race, and comfort with race), and thus we only report results for participants who completed the final survey.
5. There was no significant difference in the frequency of mentioning of race, and participants’ comfort with race, across the type of race-related conversation, and therefore we do not examine type of race-related conversation further in the paper.
6. We calculated the mean using a random-intercept multilevel model with no predictors to account for the nested nature of the data. The mean is the intercept.
7. Two participants had missing data and were thus excluded from analyses.
8. Twelve participants did not report having a race-related conversation throughout the week, therefore analyses are conducted on n = 43.
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Notes on contributors
Chanel Meyers
Chanel Meyers is an assistant professor of psychology at the University of Oregon. She received her PhD in psychology at the University of Hawaiʻi at Manoa and then resumed as a postdoctoral researcher at York University. Her program of research is focused on how racial diversity shapes social cognitive processes, with an emphasis on centering underrepresented groups and contexts within psychological research.
Sabrina Thai
Sabrina Thai is currently Assistant Professor of Psychology at Brock University. She completed her PhD in social psychology at the University of Toronto and her SSHRC-funded postdoctoral fellowship at McGill University. Her research interests include social comparisons, close relationships, and the self. She is the lead developer of ExperienceSampler, an open-source scaffold for developing smartphone apps for experience sampling.
Kristin Pauker
Kristin Pauker is full professor in psychology at University of Hawai‘i at Manoa. She got her PhD at Tufts University and completed a postdoc at Stanford University funded by an NIH K99 award. She studies how culturally-shaped theories about race impact basic social perception, social interactions, and stereotyping in childhood and throughout development.