Black Girls' Mathematics and Science Identities Using Large-Scale Data: A QuantCrit Framework

• In Event: Diversity, Equity, and Social Justice in Educational Measurement

Abstract

Early exposure to topics of mathematics and science is vital for recruiting and retaining youth in Science, Technology, Engineering, and Mathematics (STEM) fields. Gender and racial inequity are embedded barriers within education systems, specifically for young Black girls. Psychological factors related to Black girl STEM trajectories must be understood. This study draws from a QuantCrit framework by combining advanced statistical methods and Critical Race Theory (CRT) to explore Black girl’s mathematics and science dispositions. Latent Profile Analysis is used to explore unique, characterized groups of Black girls based on their confidence, interest, value, and motivation towards STEM topics, with links to opportunity and outcome factors to support Black girls’ STEM trajectories. Implications of this race-focused work are discussed.

Authors

• Thao Thu Vo, Washington State University - Pullman

• Brian F. French, Washington State University

• Shenghai Dai, Washington State University - Pullman