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Week 1: (May 31st 2022-June 3rd 2022)

  • Attended REU introduction meeting
  • Attended REU meeting on how to conduct research.
  • Attended meeting with mentor discussing initial steps
  • Reviewed annotated bibliography
  • Completed RCR training with CITI modules

Week 2: (June 6th 2022 - June 10th 2022)

  • Attended training on proper research methods
  • Located relevant work in the field
  • Attended meeting with mentor
  • Begin annotated bibliography
  • Generate Potential Research Questions

Week 3: (June 16th 2022 - June 17th 2022)

  • Continue annotated bibliography
  • Refine Research Questions
  • Attend meeting to introduce myself to the project team
  • Attend training about LaTex

Week 4: (June 20th 2022 - June 24th 2022)

  • Continue annotated bibliography
  • Begin cleaning data
  • Learn about R
  • Investigate methods of analysis for the data
  • Attend research talk
  • Attend meeting with mentor and the quantitative team

Week 5: (June 27th 2022 - July 1st 2022)

  • Attend presentation about how to do presentation
  • Continue cleaning and preparing data
  • Continue annotated bibliography
  • Present current progress of project to the group
  • Continue exploring R
  • Learn how to use pivot tables
  • Attend check-in with mentor and quantitative team
  • Attend meeting with full research team
  • Continue literature review

Week 6: (July 4th 2022 - July 8th 2022)

  • Attend Check-in
  • Meet with research team
  • Continue preparing data
  • Continue annotated bibliography
  • Continue exploring R
  • Begin writing final paper

Week 7: (July 11th 2022 - July 15th 2022)

  • Attend presentation on data ethics
  • Meet with research team
  • Begin preliminary analysis
  • Finish preparing data
  • Continue annotated bibliography
  • Continue writing the final paper
  • Begin creating visuals

Week 8: (July 18th 2022 - July 22nd 2022)

  • Attend presentation on good posters
  • Begin to create final poster and presentation
  • Continue writing final paper
  • Finish preliminary analysis
  • Begin finding predictive validity and bias