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Testing the Efficacy of Educational Interventions on Matched Student Samples: A Primer for Propensity Score Matching in R

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Title: Testing the Efficacy of Educational Interventions on Matched Student Samples: A Primer for Propensity Score Matching in R
Language: English
Authors: Nicholas D. Evans, Perla C. Perez, Osvaldo F. Morera
Source: Journal of STEM Outreach. 2025 8(1).
Availability: Journal of STEM Outreach. PMB 0367, 230 Appleton Place, Nashville, TN 37203. e-mail: jstemoutreach@vanderbilt.edu; Web site: https://www.jstemoutreach.org/
Peer Reviewed: Y
Page Count: 9
Publication Date: 2025
Sponsoring Agency: National Institutes of Health (NIH) (DHHS)
Contract Number: 1R25GM13295904
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Statistical Analysis, Research Methodology, Science Education, Educational Research, Science Programs, Equal Education, Grade Point Average, Biomedicine, Disadvantaged Schools, Disproportionate Representation, Intervention, High School Students, Matched Groups, Programming Languages
Geographic Terms: Texas, New Mexico
ISSN: 2576-6767
Abstract: In many educational intervention programs, it is not possible to randomly assign students to an experimental and control condition. For example, in our research we wanted to compare students who were enrolled in a biomedical pathway program to students who were not in such a program. However, students select their academic pathway program and a randomized controlled trial cannot be conducted. Propensity score matching (PSM) is a valuable statistical technique in areas of research when randomized control trials are not always possible. It can be widely used to mimic the process of randomization by creating comparable groups based on key covariates while increasing causal inference and reducing bias. The aim of this article is to provide guidance for science education researchers to make informed decisions about the selection of matching methods and implementation of PSM using the MatchIt package (Ho et al., 2011) in R. In this article, we 1) discuss the utility of using PSM for research involving educational interventions, 2) provide a comprehensive guide for conducting PSM with educational data and provide a detailed step-by-step guide on conducting PSM for nearest neighbor matching using R, and 3) apply it to a National Institutes of Health (NIH)-funded high school education program.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1489363
Database: ERIC
FullText Text:
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  Data: In many educational intervention programs, it is not possible to randomly assign students to an experimental and control condition. For example, in our research we wanted to compare students who were enrolled in a biomedical pathway program to students who were not in such a program. However, students select their academic pathway program and a randomized controlled trial cannot be conducted. Propensity score matching (PSM) is a valuable statistical technique in areas of research when randomized control trials are not always possible. It can be widely used to mimic the process of randomization by creating comparable groups based on key covariates while increasing causal inference and reducing bias. The aim of this article is to provide guidance for science education researchers to make informed decisions about the selection of matching methods and implementation of PSM using the MatchIt package (Ho et al., 2011) in R. In this article, we 1) discuss the utility of using PSM for research involving educational interventions, 2) provide a comprehensive guide for conducting PSM with educational data and provide a detailed step-by-step guide on conducting PSM for nearest neighbor matching using R, and 3) apply it to a National Institutes of Health (NIH)-funded high school education program.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
    Subjects:
      – SubjectFull: Statistical Analysis
        Type: general
      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Science Education
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      – SubjectFull: Educational Research
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      – SubjectFull: Science Programs
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      – SubjectFull: Equal Education
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      – SubjectFull: Grade Point Average
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      – SubjectFull: Biomedicine
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      – SubjectFull: Disadvantaged Schools
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      – SubjectFull: Disproportionate Representation
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      – SubjectFull: Intervention
        Type: general
      – SubjectFull: High School Students
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      – SubjectFull: Matched Groups
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      – SubjectFull: Programming Languages
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      – SubjectFull: Texas
        Type: general
      – SubjectFull: New Mexico
        Type: general
    Titles:
      – TitleFull: Testing the Efficacy of Educational Interventions on Matched Student Samples: A Primer for Propensity Score Matching in R
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