Data Literacy Training Summer 2020
Principal Investigator(s): View help for Principal Investigator(s) Janice Dias, Grassroot Community Foundation ; Melody Goodman , New York University; Jinal Shah, New York University
Version: View help for Version V2
| Name | File Type | Size | Last Modified |
|---|---|---|---|
| Code_R-scripts- | 01/04/2024 05:29:PM | ||
| Data-files | 01/04/2024 05:27:PM |
Project Citation:
Dias, Janice, Goodman , Melody, and Shah, Jinal. Data Literacy Training Summer 2020 . Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-01-11. https://doi.org/10.3886/E191001V2
Project Description
Summary:
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Because of the COVID-19 pandemic, presentation of public health data to the public has increased without much of the public having the knowledge to understand what these statistics mean or why some populations are at higher risk of adverse outcomes. Recognizing that those most impacted by COVID-19 are from vulnerable populations, we developed a training program called "The quantitative public health data literacy training program", aimed at increasing the data literacy of towards high school and college students from such vulnerable groups that introduces the basics of public health, data literacy, statistical software, descriptive statistics, and data ethics.
The instructors taught eight synchronous sessions (five were also offered asynchronously), consisting of lectures and experiential group exercises. The program recruited, engaged, and retained a large cohort (n > 100) of underrepresented students in biostatistics and data science for a virtual data literacy training. The course provides a framework for developing and implementing similar public health training programs designed to increase diversity in the field.
This project provides de-identified data for program's baseline/final assessment , program feedback as well as grades for certain portion of the program. The "Data-files" folder contains all the data collected during program. Along with the deidentified data, code is also provided (in R language) to analyze the data as presented in tables in potential publications.
The instructors taught eight synchronous sessions (five were also offered asynchronously), consisting of lectures and experiential group exercises. The program recruited, engaged, and retained a large cohort (n > 100) of underrepresented students in biostatistics and data science for a virtual data literacy training. The course provides a framework for developing and implementing similar public health training programs designed to increase diversity in the field.
This project provides de-identified data for program's baseline/final assessment , program feedback as well as grades for certain portion of the program. The "Data-files" folder contains all the data collected during program. Along with the deidentified data, code is also provided (in R language) to analyze the data as presented in tables in potential publications.
Scope of Project
Subject Terms:
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Data science eduction;
Web training;
Data visualization;
Data Ethics;
Diversity;
Equity;
Inclusion;
statistical software
Time Period(s):
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6/1/2020 – 8/31/2020
Data Type(s):
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administrative records data;
survey data
Methodology
Response Rate:
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We received 695 applications in the 12-day period the application was open. After reviewing applications, the program’s staff assigned the candidates to one of three tracks: students (Track 1), non–public health professionals (Track 2), or public health professionals (Track 3). Since our program was initially designed for students, we selected only applicants from Track 1 for this pilot study, and we present only the results for Track 1..Even within the student population (Track 1, n = 511), we received more applications than anticipated. Since we had limited resources and one instructor, we selected our target population for participation in the live session, which was Track 1A: students who are less likely to have access or opportunity to participate in similar quantitative programs, including underrepresented racial/ethnic minorities in data science or biostatistics (n = 240). These students were then sent a one-question survey, informing them about class date/time and confirming their participation in the program. Most (69%; n = 166) students completed the survey with 164 of them agreeing to participate in the program. These 164 students were sent the baseline assessment; 154 of them completed the assessment, and 118 of them attended the first session. Those who completed the baseline assessment but did not attend the first session were contacted to confirm their participation in the program. Two of the students contacted us and stated that they could not attend the first session but that they wanted to continue with the program; therefore, they were enrolled. The final class size for Track 1A was 120 students, for whom the results are presented.
Sampling:
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None
Collection Mode(s):
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other;
web-based survey
Unit(s) of Observation:
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Individuals
Related Publications
Published Versions
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