Discussion Overview
The discussion revolves around potential issues related to the separation of values in ordinal logistic regression, particularly in the context of 3-valued or higher-valued logistic regression. Participants explore the implications of separation, its causes, and possible solutions, focusing on both theoretical and practical aspects of the modeling process.
Discussion Character
- Debate/contested
- Technical explanation
- Exploratory
Main Points Raised
- One participant questions whether issues of separation in binary logistic regression also apply to 3-valued logistic regression, suggesting a connection to the behavior of the S-curve.
- Another participant clarifies that "Separation of Points" typically refers to complete or quasi separation, which can occur in small or miscoded datasets, and notes that this issue does not disappear in more general cases.
- A participant explains that separation occurs when there is a threshold value of the independent variable beyond which all outcomes are either successes or failures, providing an example related to smoking and cancer.
- One participant agrees that separation is indeed a problem for higher orders and mentions that statistical packages often alert users to this issue, suggesting penalizing maximum estimators or hidden logistic models as potential solutions.
- A follow-up question is raised about the appropriateness of slightly altering data values to avoid separation, with the participant expressing concern about preserving the intrinsic properties of the dataset while modeling probabilities.
Areas of Agreement / Disagreement
Participants generally agree that separation is a relevant issue in ordinal logistic regression, including higher orders. However, there is no consensus on the best approaches to address this problem, and differing opinions on the implications of altering data values are present.
Contextual Notes
Participants express uncertainty regarding the definitions and implications of separation, and there are unresolved questions about the effects of modifying data values to mitigate separation issues.