Writing

Here are some links to recent writing I’ve done, both in peer-reviewed publications and on blogs.

Peer-reviewed publications

• McNamara, A. (2024). Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes. Journal of Statistics and Data Science Education 32(4), 374–394. DOI:10.1080/08993408.2024.2403971. Additional materials: pre-print, GitHub repo, Curricular materials.
  
• McNamara, A. (2023). The impact (or lack thereof) of analysis choice on conclusions with Likert data from the Many Analysts Religion Project. Religion, Brain & Behavior, 13(3), 324-326. DOI:10.1080/2153599X.2022.2070256
• Hoogeveen, S., Sarafoglou, A., van Elk, M., Wagenmakers, E-J., [249 additional coauthors, including McNamara, A.]. (2023). A Many-Analysts Approach to the Relation Between Religiosity and Well-being. Religion, Brain & Behavior, 13(3), 237-283. DOI:10.1080/2153599X.2022.2070255. Additional materials: my analysis.
• Abdel-Ghani, A. 1, Bodwin, K., McNamara, A., Theobold, A., and Flores Siaca, I. “Looks okay to me”: A study of best practice in data analysis code review. International Conference on Teaching Statistics (ICOTS-11) Proceedings DOI:10.52041/iase.icots11.T8I1
• Cetinkaya-Rundel, M., Hardin, J., Baumer, B., McNamara, A., Horton, N.J., Rundel, C. An educator’s perspective of the tidyverse Technology Innovations in Statistics Education, Vol. 14, Issue 1. DOI:10.1080/00031305.2018.1482784. Additional materials: pre-print, GitHub repo.
• Schweinberg, M., Feldman, M., Staub, N., [175 additional coauthors, including McNamara, A.], Uhlman, E. L. (2021). Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis. Organizational Behavior and Human Decision Processes. 165, 228-249. DOI:10.1007/S00180-019-00879-X
• McNamara, A. Key attributes of a modern statistical computing tool, https://www.tandfonline.com/doi/full/10.1080/00031305.2018.1482784”>The American Statistician, Vol. 73, Issue 4. DOI:10.1080/00031305.2018.1482784 Additional materials: pre-print, blog post.
• McNamara, A. Community engagement and subgroup meta-knowledge: Some factors in the soul of a community. Computational Statistics, Vol. 34, Issue 4. DOI:10.1007/S00180-019-00879-X Additional materials: pre-print, GitHub repo)
• Rafalski, T., Uesback, P. M., Panks-Meloney, C., Daleiden, P., Allee, W., McNamara, A. and Stefik, A. (2019). A Randomized Controlled Trial on the Wild Wild West of Scientific Computing with Student Learners. In Proceedings of the 2019 ACM Conference on International Computing Education Research (pp. 239-247). DOI:10.1145/3291279.3339421
• McNamara, A., and Horton, N. J. Wrangling categorical data in R, The American Statistician, Vol. 72, Issue 1 (pre-print in PeerJ Collection) DOI:10.1080/00031305.2017.1356375
• McNamara, A., Horton, N.J., Baumer, B. Greater data science at baccaleureate institutions, Journal of Computational and Graphical Statistics, Vol. 26, No 4.

Blog posts

• McNamara, A. Data visualisation by hand: drawing data for your next story. DataJournalism.com, March 24 2021.
• McNamara, A. Counting commits and peer code review. Teach Data Science, July 28, 2019.