PREJUDICE AWARENESS AND REDUCTION (PAR) LAB
Welcome!
At the Prejudice Awareness and Reduction (PAR) Lab, we are dedicated to exploring the complexities of human biases, both subtle and overt. Our research delves into the underlying mechanisms that drive prejudice and discrimination, striving to understand when and why these biases emerge in various social contexts.
Our primary mission is to uncover the factors that fuel bias and devise strategies for cultivating more inclusive and equitable environments. To achieve this, we are actively testing various interventions designed to diminish bias and foster understanding and collaboration among diverse groups. Our work extends to various settings, including educational institutions and workplaces, where we strive to empower individuals to create enduring positive change in their lives and communities.
The PAR Lab is always eager to welcome new members. If you're interested in contributing to research that tackles real-world issues of prejudice and social justice, we would be happy to hear from you! Email us at parlab@stfx.ca
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Broadly, student researchers will work on studies investigating predictors (e.g., media consumption, ideologies) and consequences (e.g., internalized harm among targets) of prejudice and discrimination, primarily (but not exclusively) related to systems of sexism, racism, and heterosexism. Students can feel free to propose projects related to prejudice and discrimination or work on existing projects including those concerning prejudice-reduction interventions (e.g., perspective-getting narratives, mindfulness interventions). A diversity framework for unpacking prejudice and discrimination is encouraged, including considering the roles of people's identities, demographic characteristics, social statuses, situational contexts, geographical regions, and other individual differences.
As a PAR lab member, you will gain skills across the research cycle, from study conceptualization, research design, study construction, data collection (online or in-person), data cleaning, data analysis and interpretation, and reporting and visualizing results.