|Title:||"Boys Have It; Girls Have to Work for It": The Development and Consequences of Gender Stereotypes About Natural Talent vs. Effort in Mathematics|
|Principal Investigator:||Cimpian, Andrei||Awardee:||New York University|
|Program:||Cognition and Student Learning [Program Details]|
|Award Period:||4 years (07/01/2020 - 06/30/2024)||Award Amount:||$1,399,994|
Co-Principal Investigators: Cimpian, Joseph; Lubienski, Sarah; Cheryan, Sapna
Purpose: The central aims of this research are to investigate the (1) development and (2) consequences of gender stereotypes that portray girls' mathematics achievements as resulting from effort and boys' mathematics achievements as resulting from natural talent (effort-vs.-talent stereotypes) and (3) to identify potential means of reducing the negative effects of these stereotypes on children's mathematics outcomes. From a theoretical standpoint, this work will be the first to characterize the development of gender stereotypes about the relative roles of effort and natural talent in mathematics success and to identify the consequences of these stereotypes for affective and cognitive processes involved in mathematics learning. Practically, this work will raise awareness of these important gender stereotypes, as well as inform efforts to mitigate their effects.
Project Activities: Researchers will conduct three studies to investigate the development and consequences of gender stereotypes about the roles of effort and talent in mathematics success. They will complete a descriptive cross-sectional study that will inform the developmental trajectory of gender stereotypes, and two experimental studies to investigate possible causal connections between these gender stereotypes and mathematics achievement.
Products: Researchers will develop a theoretically plausible and practically significant framework from which to design educational tools that enhance math learning and engagement. They will share the findings from this research via conference presentations and peer-reviewed publications.
Setting: This study will take place in New York City public schools.
Sample: Researchers will recruit 1,296 schoolchildren in grades 1 to 4. The sample will be collected from schools with racially, ethnically, and socioeconomically diverse student populations.
Factors: The current research will explore whether effort and talent stereotypes about mathematics learning and achievement are malleable factors.
Research Design and Methods: In Study1, researcherswill use a cross-sectional sample of children in grades 1 to 4 to examine the developmental trajectory of the stereotypic belief that girls' math achievement is due to effort and boys' due to natural talent. This study will also characterize the relations between these stereotypes and key math outcomes (motivation, attitudes, and achievement). In Study 2, researcherswill use an experimental design to investigate whether exposure to effort vs. talent stereotypes causally affectschildren's motivation and attitudes. Finally, the goal of Study 3, which also uses an experimental design, is to identify means of mitigating the negative effects of effort-vs.-talent stereotypes on children's math motivation and attitudes by normalizing effort as necessary for success in mathematics.
Control Condition: Due to the nature of the research design in Study 1, there is no control condition. In Study 2, there is no control condition. Instead, students will be randomly assigned to receive exposure to either effort or talent stereotypes about a novel learning task they are about to complete. In Study 3, participants in the control condition will not receive any messages about the role of effort or talent in success.
Key Measures: In Study 1, researchers will administer a novel measure designed to assess children's effort vs. talent stereotypes. They will then examine the relations between this measure and outcomes relevant to math learning: math self-efficacy, interest, anxiety, problem-solving preferences, and achievement (measured via a standardized test). For Studies 2 and 3, researchers will adapt these outcome measures to apply to novel learning scenarios and will administer them to students following the experimental manipulation.
Data Analytic Strategy: Researchers will analyze these data with multilevel mixed-effects linear regressions that take into account the nested structure of the data (e.g., children nested in classrooms).