Joel Le Forestier


SSHRC DOCTORAL FELLOW IN SOCIAL PSYCHOLOGY
AT THE UNIVERSITY OF TORONTO

I research how members of stigmatized groups experience and navigate intergroup contexts, how to leverage insights from this and other social-psychological research to solve real-world problems, and the quantitative methods we use along the way.

How do members of stigmatized groups experience and navigate intergroup contexts?

Members of stigmatized groups are often the targets of prejudice and discrimination. I research how members of stigmatized groups experience and manage this reality, as well as the positive and negative consequences of their management strategies. Current research topics include the impact of people's lay beliefs about the concealability of their own identities on their intergroup experiences, the impact of people’s concealment behaviors on others’ perceptions of them, an the impact of intergroup conflict on future intergroup interactions.

How can we leverage insights from social-psychological research to solve real-world problems?

Stigma is associated with worse outcomes in education, the workplace, health, and more. I study how we can use theoretically-informed interventions to address these problems. Current projects include assessing the effects of a social-belonging intervention on academic achievement and physical health, a comparative investigation of multiple interventions to reduce implicit bias against higher-weight people, and intergroup contact field interventions in online settings.

How can researchers improve the quality of their statistical inferences?

As scientists, our findings are only as valid as our methods. I study the conditions under which we can draw valid conclusions from our data. Current projects include estimating the sample sizes at which coefficient estimates in interaction models stabilize and the development of methods and tools for power analysis for sets of statistical tests, including the development of an R package, SimulPower.

Under Review

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

Ekstrom, P., Le Forestier, J. M., & Lai, C. K. (Revise and resubmit). Racial disparities in police traffic stops are associated with county-level racial attitudes.

Published / In press

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.

Logel, C., Le Forestier, J. M., Witherspoon, E. B., & Fotuhi, O. (2020). A social-belonging intervention benefits higher-weight students' weight stability and academic achievement. Social Psychological and Personality Science. 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

Scales

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 (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.

R Packages

SimulPower

Calculate statistical power to simultaneously detect a set of effects.

Pride Palettes

Color palettes based on Pride flags.

Shiny Apps

Toronto Covid-19 Neighbourhood Data Visualizer

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

Infographics

Simulpower

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 0.7.0, updated in November 2020. 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

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.

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.

devtools::install_github("joelleforestier/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
?flag

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