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Dichotomization and Proportional Odds Model (Episode 10)
In this episode, we move from the logistic regression model to proportional odds model, with emphasis on
interpretation and the checking of assumptions (visually and analytically). We also speak about the
opportunities and challenges of dealing with the dichotomization of ordinal or continuous variables.
Resources:
● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.
● Agresti, Alan. Categorical data analysis. John Wiley & Sons, 2003.
● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and
nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)
View all episodes
By Alexander Schacht and Paolo EusebiIn this episode, we move from the logistic regression model to proportional odds model, with emphasis on
interpretation and the checking of assumptions (visually and analytically). We also speak about the
opportunities and challenges of dealing with the dichotomization of ordinal or continuous variables.
Resources:
● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.
● Agresti, Alan. Categorical data analysis. John Wiley & Sons, 2003.
● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and
nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)