In this week’s episode Patrick and Greg talk about outcomes that are count variables: when you need to worry about them and what you can do about them within your analytical models. Along the way they also mention: Bela Lugosi, Vlad the Impaler, Patrick the Poker, Count Chocula, Count von Count, drunken bar brawls, secret distributions, K!, bio breaks, second favorite child, Animal Farm, Cliff’s notes, A’s in band, and more equal zeros.
Related Episodes
- S5E24: Zombie Wheel of Distributions
- S2E27: (re)Connecting With Discrete Data
- S2E18: Regression: Like That Old High School Friend You’ve Outgrown
- S2E17: Embracing Your Non-Normality
Recommended Readings
Atkins, D. C., Baldwin, S. A., Zheng, C., Gallop, R. J., & Neighbors, C. (2013). A tutorial on count regression and zero-altered count models for longitudinal substance use data. Psychology of Addictive Behaviors, 27(1), 166.
Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (No. 53). Cambridge University Press.
Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of Personality Assessment, 91(2), 121-136.
McGinley, J. S., & Curran, P. J. (2014). Validity concerns with multiplying ordinal items defined by binned counts. Methodology, 10, 108-116.
McGinley, J. S., Curran, P. J., & Hedeker, D. (2015). A novel modeling framework for ordinal data defined by collapsed counts. Statistics in Medicine, 34(15), 2312-2324.
Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27, 1-25.