Greg Depow
@gregdepow.bsky.social
Postdoc at UCSD/Harvard, PhD from University of Toronto. Studying #empathy #effort #wellbeing #socialmedia and #prosocial behaviour. #rstats #PhDad #openscience website: gregdepow.com
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October 23, 2025 at 9:45 PM
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It depends: Logistic is for binary variables typically coded as 0 or 1, e.g., disease or not. Poisson is for count variables, e.g. number of drinks per week. For poisson, people use Incidence Rate Ratios instead of Odds Ratios, but both can be converted to statements about Quantities of Interest.
October 2, 2025 at 5:01 PM
It depends: Logistic is for binary variables typically coded as 0 or 1, e.g., disease or not. Poisson is for count variables, e.g. number of drinks per week. For poisson, people use Incidence Rate Ratios instead of Odds Ratios, but both can be converted to statements about Quantities of Interest.
Reposted by Greg Depow
Halvorson et al. (2021) should probably be required reading for folks working with binary and count data (@kevinmking.bsky.social )
pmc.ncbi.nlm.nih.gov/articles/PMC...
pmc.ncbi.nlm.nih.gov/articles/PMC...
Making sense of some odd ratios: A tutorial and improvements to present practices in reporting and visualizing quantities of interest for binary and count outcome models
Generalized linear models (GLMs) such as logistic and Poisson regression are among the most common statistical methods for modeling binary and count outcomes. Though single-coefficient tests (odds rat...
pmc.ncbi.nlm.nih.gov
September 25, 2025 at 2:20 AM
Halvorson et al. (2021) should probably be required reading for folks working with binary and count data (@kevinmking.bsky.social )
pmc.ncbi.nlm.nih.gov/articles/PMC...
pmc.ncbi.nlm.nih.gov/articles/PMC...
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September 6, 2025 at 5:51 AM
Get a session manager extension for your browser. You can categorize and save your tabs and open them on your new laptop!