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Vertical transfer from community college to a university offers a promising, although unrealized, pathway to diversify STEM disciplines. Studying how successful transfer-receiving universities support STEM transfer students can offer insights into the institutional practices that promote transfer student retention and success. Using institutional data is crucial to identify vulnerable populations within the STEM transfer population and to design necessary changes in practice or policy, especially at the department level. Providing discipline-specific multidimensional support throughout STEM transfer students’ undergraduate careers can improve transfer rates and retention and ease students’ transition to the university. Although universities have developed promising practices and programs, support for STEM transfer students is not systematically available and should be more targeted, intentional, and comprehensive throughout the transfer and adjustment process.
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Taking the Next Step in Exploring the Literary Digest 1936 Poll
While many instructors are aware of the Literary Digest 1936 poll as an example of biased sampling methods, this article details potential further explorations for the Digest’s 1924-1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to basic data literacy and visualization skills, to exploring one or more methods of adjustment to account for bias based on information collected at that time. Students can also compare how those methods would have performed. One option could be to give introductory students a first look at the idea of “sampling adjustment” and how this principle can be used to account for difficulties in modern polling, but the context is rich in other opportunities that can be discussed at various times in the course or in more advanced sampling courses.
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- Award ID(s):
- 2235355
- PAR ID:
- 10545495
- Publisher / Repository:
- Taylor and Fransis; Journal of Data Science and Statistics Education
- Date Published:
- Journal Name:
- Journal of Statistics and Data Science Education
- ISSN:
- 2693-9169
- Page Range / eLocation ID:
- 1 to 14
- Subject(s) / Keyword(s):
- Data cleaning Data validation Bias Post-stratification
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/26939169.2024.2395505
