Joel Le Forestier
SSHRC DOCTORAL FELLOW IN
SOCIAL AND QUANTITATIVE PSYCHOLOGY
AT THE UNIVERSITY OF TORONTO
I research how people experience and navigate intergroup contexts, how to leverage insights from intergroup research to solve real-world problems, and the quantitative methods we use along the way.
HOW DO PEOPLE EXPERIENCE AND NAVIGATE INTERGROUP CONTEXTS?
The primary goal of my research is to understand the factors that shape people’s experiences in intergroup contexts and diverse societies. Much of my research examines how members of stigmatized groups respond to the experience of stigmatization through concealment. This has included the impacts of subjective identity concealability on people’s feelings and interactions (Le Forestier, Page-Gould, Lai, & Chasteen, in press; Le Forestier, Page-Gould, Lai, & Chasteen, 2020) and perceivers’ appraisals of those who conceal (Le Forestier, Page-Gould, & Chasteen, under review). In ongoing research, I am examining predictors of concealment behavior and the contributions of distinct concealment-related processes to the consequences of concealment.
Additional ongoing research examines stigmatized and non-stigmatized people’s responses to instances of intergroup conflict and recent research examined the relationship between regional-level racial bias and racial disparities in police behavior (Ekstrom, Le Forestier, & Lai, in press).
HOW CAN WE LEVERAGE INSIGHTS FROM SOCIAL-PSYCHOLOGICAL RESEARCH TO SOLVE REAL-WORLD PROBLEMS?
One line of research involves developing and testing theory-based interventions for prejudice reduction and attenuating the costs of stigma. Ongoing prejudice-reduction work includes a comparative investigation of multiple interventions to reduce implicit bias and a field intervention testing the effects of intergroup contact through social media. A recent intervention administered at scale and in the field reduced the weight-based academic achievement gap through social-belonging (Logel, Le Forestier, Witherspoon, & Fotuhi, 2020).
HOW CAN RESEARCHERS IMPROVE THE QUALITY OF THEIR STATISTICAL INFERENCES?
I study the conditions under which we can draw valid conclusions from our data and develop tools to help researchers conduct rigorous research. One current project involves the development of methods and tools for power analysis for sets of statistical tests (Le Forestier, Page-Gould, & Chasteen, under review), including the development of an R package, SimulPower (Le Forestier, 2020).
Le Forestier, J. M., Page-Gould, E., & Chasteen, A . L. (Under review). Concealment stigma: The social costs of concealing.
Le Forestier, J. M., Page-Gould, E., & Chasteen, A . L. (Under review). Statistical power for a set of tests.
Kawakami, K., Williams, A., Pek, J., Page-Gould, E., & Le Forestier, J. (Under review). Analyzing relative attention to the eyes of Black and White faces.
Published / In press
Ekstrom, P., Le Forestier, J. M., & Lai, C. K. (In press). Racial demographics explain the link between racial disparities in traffic stops and county-level racial attitudes. Psychological Science.
Le Forestier, J. M., Page-Gould, E., Lai, C. K., & Chasteen, A. L. (In press). Subjective identity concealability and the consequences of fearing identity-based judgment. Personality and Social Psychology Bulletin. 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
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: https://doi.org/10.1007/978-3-319-69892-2_584-1
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.
A three-item measure originally used in Le Forestier, Page-Gould, Lai, & Chasteen (Under review) 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 (Under review) to assess individual differences in participants' beliefs in the concealability of their own identities.
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.
When you use this function (and we hope you do!), please cite the package:
Le Forestier, J. M. (2020). SimulPower: Simultaneous power analysis for a set of statistical tests. https://doi.org/10.31219/osf.io/w96uk
and/or cite the accompanying paper:
Le Forestier, J. M., Page-Gould, E., & Chasteen, A. L. (Under review). Statistical power for a set of tests.
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
Install the devtools package, which allows you to install packages from GitHub, if you don't have it installed already.
Using Pride Palettes
Load the PridePalettes package.
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:
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:
For additional guidance and a full list of available palettes, refer to each function's help page:
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