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Teaching Missing Data Methodology to Undergraduates Using a Group-Based Project Within a Six-Week Summer Program.

Megan M Marron1, Abdus S Wahed2

  • 1Center for Aging and Population Health, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 North Bellefield Avenue, Pittsburgh, PA 15213, USA.

Journal of Statistics Education : an International Journal on the Teaching and Learning of Statistics
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

Introducing missing data concepts early in biostatistics training is crucial for biomedical research. Project-based learning effectively enhances statistical literacy and reasoning in trainees with limited prior preparation.

Keywords:
Method of handling missing dataMissing data mechanismSimulationSummer institute for training in biostatistics

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Area of Science:

  • Biostatistics
  • Biomedical Research Education

Background:

  • Handling missing data is fundamental in biomedical research but often delayed until graduate studies.
  • Early introduction of missing data concepts can improve statistical literacy and reasoning.

Purpose of the Study:

  • To demonstrate the feasibility of teaching missing data concepts to trainees with limited statistical background.
  • To enhance statistical literacy and reasoning through practical, project-based learning.

Main Methods:

  • Utilizing a project-based setting at the Summer Institute for Training in Biostatistics (SIBS) Pittsburgh.
  • Incorporating simulation and data analysis of a Hepatitis C treatment study.
  • Applying learned missing data mechanisms and statistical techniques to real-world data.

Main Results:

  • Successful introduction of advanced missing data concepts to trainees with limited statistical preparation.
  • Project-based training effectively enhances trainees' statistical literacy and reasoning.
  • Demonstrated feasibility of teaching complex statistical topics in an accessible format.

Conclusions:

  • Project-based training, including simulation and data analysis, is effective for teaching advanced statistical topics like missing data.
  • Early exposure to missing data handling improves foundational skills for biomedical research.
  • The SIBS program successfully motivates trainees towards graduate studies and research in biostatistics.