Joel Le Forestier


I research how people experience and navigate intergroup contexts and how to intervene to improve intergroup relations and minimize group-based disparities.


Members of stigmatized groups are motivated to avoid becoming the targets of stigma and prejudice. Identity concealment (i.e., preventing others from finding out that one holds a certain identity), serves as a tool for members of stigmatized groups to do so. I have found that identity concealment achieves its goal of of helping members of stigmatized groups avoid prejudice and stigmatization (Le Forestier, Page-Gould, Lai, & Chasteen, 2022) and thus facilitates their navigation of intergroup contexts (Le Forestier, Page-Gould, Lai, & Chasteen, 2020). However, I also find that concealment is judged harshly; people who conceal are perceived as immoral and unsociable (Le Forestier, Page-Gould, & Chasteen, 2022).Central to my approach to studying concealment is a novel person-by-identity approach. This approach does not assume that all identities of a certain kind are equally concealable, but rather allows individuals to have varied experiences of concealability. This approach outperforms traditional methods for studying concealment at predicting both who conceals and how successful concealment will be (Le Forestier, Page-Gould, & Chasteen, under review).In ongoing and upcoming work, I am examining another negative outcome of concealment: health. I am currently examining the mechanisms that account for why concealment undermines health, and am planning lab and field interventions to sever the link between concealment and health and reduce health disparities.

What are the best ways to reduce prejudice and improve intergroup relations?

Findings from my research highlight why prejudice reduction is so important. In a study using massive archival datasets, my collaborators and I found that regional variance in implicit racial bias predicts local disparities in police traffic stops (Ekstrom, Le Forestier, & Lai, 2022). In another, my collaborators and I documented a weight-based achievement gap in postsecondary education and found that it was at least partially attributable to identity threats higher-weight students experience on college and university campuses (Logel, Le Forestier, et al., 2021).Findings such as these bring the urgent need for scalable prejudice reduction into focus. In one ongoing project, I have developed an immersive and scalable intergroup contact intervention that takes place over social media. Preliminary results include behavioral outcomes up to a month post-intervention.In several other lines of work, I am conducting research contests to distinguish between plausible alternatives and find the best possible interventions to improve intergroup relations. This includes an investigation of the best methods to reduce ideological prejudice and improve cooperation across ideological lines and an investigation of whether the efficacy of several implicit bias interventions depends on how implicit bias is measured.

Journal Articles: Published / In press

Le Forestier, J. M., Page-Gould, E., & Chasteen, A . L. (2022). Concealment stigma: The social costs of concealing. Journal of Experimental Social Psychology, 101, 1-13. DOI: 10.1016/j.jesp.2022.104340

Ekstrom, P., Le Forestier, J. M., & Lai, C. K. (2022). Racial demographics explain the link between racial disparities in traffic stops and county-level racial attitudes. Psychological Science, 33, 497-509 DOI: 10.1177/09567976211053573

Le Forestier, J. M., Page-Gould, E., Lai, C. K., & Chasteen, A. L. (2022). Subjective identity concealability and the consequences of fearing identity-based judgment. Personality and Social Psychology Bulletin, 48, 445-462. DOI: 10.1177/01461672211010038

Logel, C., Le Forestier, J. M., Witherspoon, E. B., & Fotuhi, O. (2021). A social-belonging intervention benefits higher-weight students' weight stability and academic achievement. Social Psychological and Personality Science, 12, 1048-1057. DOI: 10.1177/1948550620959236

Le Forestier, J. M., Page-Gould, E., Lai, C. K., & Chasteen, A. L. (2020). Concealability beliefs facilitate navigating intergroup contexts. European Journal of Social Psychology, 50, 1210-1226. DOI: 10.1002/ejsp.2681

Journal Articles: In Prep / Under Review

Le Forestier, J. M., Page-Gould, E., & Chasteen, A . L. (Under review). Which identities are concealable? Individual differences in concealability.

Le Forestier, J. M., Page-Gould, E., & Chasteen, A . L. (In prep). Statistical power for a set of tests.

Le Forestier, J. M., Chan, E. W., Shephard, R., Page-Gould, E., & Chasteen, A . L. (In prep). Why does concealment undermine health and wellbeing?

Le Forestier, J. M. (In prep). Statistical power for a set of tests.

College Transition Collaborative. (In prep). Where and with whom does a brief social-belonging intervention raise college achievement? The CTC Belonging trial.

Kawakami, K., Williams, A., Pek, J., Page-Gould, E., & Le Forestier, J. (In prep). Analyzing relative attention to the eyes of Black and White faces.

Lai, C. K. & Le Forestier, J. (In prep). A comparative investigation of interventions to reduce weight bias on five implicit measures.

Book Chapters

Chasteen, A. L., Schiralli, J. E., Le Forestier, J. M., & Erentzen, C. (In press). Age stereotypes and ageism as facets of subjective aging. In Y. Palgi, A. Shrira, & M, Diehl (Eds.), Subjective views of aging: Theory, research, and practice. New York, NY: Springer.

Chasteen, A. L., Bergstrom, V. N. Z., Schiralli, J. E., & Le Forestier, J. M. (2019). Age stereotypes. In D. Gu & M. E. Dupre (Eds.), Encyclopedia of gerontology and population aging. New York, NY: Springer. DOI:

Initiation of Intergroup Contact

A seven-item measure originally used in Le Forestier, Page-Gould, Lai, & Chasteen (2020) to assess participants' proclivity to initiate contact with outgroup members.

Situational Avoidance

A three-item measure originally used in Le Forestier, Page-Gould, Lai, & Chasteen (2022) to assess participants' proclivity to avoid otherwise-desirable activities on account holding of a specific identity.

Subjective Identity Concealability

An eight-item measure developped and validated in Le Forestier, Page-Gould, Lai, & Chasteen (2022) to assess individual differences in participants' beliefs in the concealability of their own identities.


Calculate statistical power to simultaneously detect a set of effects.

Pride Palettes

Color palettes based on Pride flags.

Toronto Covid-19 Neighbourhood Data Visualizer

Explore factors that predict the prevalence of Covid-19 in Toronto's neighbourhoods.

Note: Covid-19 data are up-to-date as of August 2021.


SimulPower is an R package for simulating simultaneous power for a set of statistical tests.SimulPower is a work-in-progress. The current version is Version 0.8.0, updated in July 2021. While you may feel free to use it, please also check back for updates in the future. If you have feedback, I'd love to hear it via email!

Installing and using SimulPower

Information for installing and using SimulPower can be found here.

Citing SimulPower

Le Forestier, J. M. (2020). SimulPower: Simultaneous power analysis for a set of statistical tests.

Pride Palettes

PridePalettes is an R package that provides you with Pride flag color schemes to use in your R plots. It also comes with pre-made Pride flags using ggplot2.

Installing Pride Palettes

Step 1:
Install the devtools package, which allows you to install packages from GitHub, if you don't have it installed already.
install.packages("devtools")Step 2:
Install PridePalettes.

Using Pride Palettes

Step 1:
Load the PridePalettes package.
library(PridePalettes)Step 2:
Make your graph!
The pride_palette function returns character vectors of HEX codes representing colors on the Pride flag of your choice, in the order they appear on the flag. So, supplying it to whatever arguments in your graph require a list of colors will color your graph like the flag. For example, the following code creates a bar chart using the colors from the Philadelphia People Of Color Pride Flag:library(ggplot2)means <- c(1, 2, 3, 4, 5, 6, 7, 8)
groups <- c("1", "2", "3", "4", "5", "6", "7", "8")
data <- data.frame(means, groups)
ggplot(data = data, mapping = aes(x = groups, y = means)) +
geom_col(aes(fill = groups)) +
scale_fill_manual(values = pride_palette("philly_poc_pride"))
PridePalettes also includes the flag function, which generates pre-made Pride flags using ggplot2. For example, the following code generates the Trans Pride Flag:flag("trans_pride")For additional guidance and a full list of available palettes, refer to each function's help page:?pride_palette

Color Blind-Friendly Pride Palettes

While PridePalettes is primarily intended for use as a novelty, anyone who uses it for data visualization they intend to share with others should be mindful that not all Pride flags translate into in color blind-friendly palettes. However, some do! Those using Pride Palettes for data visualization are encouraged to choose from the following list of flags that are color blind-friendly for three of the most common forms of color blindness (i.e., protanopia, deuteranopia, and tritanopia).

  • Agender Pride Flag

  • Aromantic Pride Flag

  • Asexual Pride Flag

  • Genderqueer Pride Flag

  • Nonbinary Pride Flag

  • Pansexual Pride Flag

  • Trans Pride Flag