
NSF Org: |
DUE Division Of Undergraduate Education |
Recipient: |
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Initial Amendment Date: | July 9, 2019 |
Latest Amendment Date: | July 9, 2019 |
Award Number: | 1920401 |
Award Instrument: | Standard Grant |
Program Manager: |
Jennifer Lewis
jenlewis@nsf.gov (703)292-7340 DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
Start Date: | October 1, 2019 |
End Date: | September 30, 2024 (Estimated) |
Total Intended Award Amount: | $499,831.00 |
Total Awarded Amount to Date: | $499,831.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1400 CRYSTAL DR FL 10 ARLINGTON VA US 22202-3289 (202)403-5585 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1000 Thomas Jefferson Street, NW Washington DC US 20007-3835 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | ECR-EDU Core Research |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.076 |
ABSTRACT
This project will study the origins of beliefs and motivational processes that could potentially limit the full participation of girls and women in science, technology, engineering, and mathematics (STEM) fields. Specifically, this synthesis project aims to clarify mixed findings about how gender stereotypes about STEM abilities first develop and how these stereotypes relate to gender gaps in STEM. Some studies of children have found the expected stereotype of superior male ability in mathematics and science, but others have found only in-group bias or even stereotypes of female superiority. This project will synthesize more than 25 years of empirical research across the world to understand how demographic, contextual, and measurement factors explain these mixed findings. For instance, children's STEM ability stereotypes may increasingly favor boys and men as children age and learn messages in their environments. Knowledge of these developmental trajectories can help intervention researchers and practitioners target the earliest ages when children begin to believe that girls and women are inferior in STEM.
This project consists of two meta-analyses that will analyze variation in (a) mean levels of children's gender stereotypes about STEM abilities and (b) these stereotypes relation to motivational STEM outcomes such as confidence and interests. In both meta-analyses, focal moderators will include child demographics, cultural contexts, and measurement characteristics. Knowledge generated by this project will help bring clarity to the mixed developmental literature on STEM ability stereotypes. Understanding why one study finds stereotypes strongly favoring males, whereas another study finds the opposite, will be critical to foster cumulative, replicable science and build integrative theories of stereotype development. Furthermore, synthesizing how ability stereotypes relate to outcomes such as confidence and interests can build fundamental knowledge on how these beliefs might relate to gender gaps in STEM participation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project advanced scientific understanding about children’s gender stereotypes about STEM and verbal abilities using rigorous methods for research synthesis. An extensive literature search and systematic review led to compiling a dataset that represented more than 145,000 children from 33 nations across more than 40 years of research.
The results overturned some commonly accepted wisdom about these stereotypes. For instance, contrary to some prior conclusions, math stereotypes favoring male ability were minimal on average. The findings also indicated the need for future research on stereotypes that can negatively impact boys. For instance, girls were seen as far superior in verbal domains, such as reading and writing.
These decades of data also emphasized the need for continued future research to better understand and address stereotypes that might prematurely turn girls away from certain STEM fields. By age 6, children already saw boys as better than girls in computing and engineering. As girls aged, their male-STEM bias increased, which could potentially limit their future aspirations for fast-growing tech fields, such as artificial intelligence.
Children’s stereotypes widely varied by STEM field. At all ages, computing and engineering stereotypes were far more gendered than math stereotypes. This finding is especially critical because most prior research has focused on children’s ability stereotypes for math and science, not tech fields. This mismatch therefore highlights the need for targeted future research on gender stereotypes about computing and engineering fields.
In addition, our synthesis shed light on the potential mechanisms for how these stereotypes might constrain girls’ interests and confidence in these fields. Many prior studies have focused on the idea that stereotypes affect performance outcomes such as test scores through a process called stereotype threat. In contrast, our meta-analysis of correlations found little support for a connection to performance outcomes. We instead found far more robust evidence that these stereotypes correlate with gender gaps in motivational outcomes like confidence and interests.
Lastly, our project’s work led to unexpected discoveries that contributed to the advancement of research methodologies for evidence synthesis. Specifically, we developed and applied a flexible, simulation-based approach for computing the power of moderator tests in complex, modern meta-analyses. This approach can help applied meta-analysts understand which moderator tests have such little power that their results should be interpreted with special caution.
Applying rigorous methods to synthesize data from more than 145,000 children therefore brought new insights and overturned conventional wisdom. Two high-priority directions for future research include (a) focusing on computer science and engineering stereotypes and (b) understanding the mechanisms for how these stereotypes might impact girls’ interests and confidence in these fields.
Last Modified: 02/12/2025
Modified by: David Isaac Miller
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