When describing statistical models and results in writing, the following are tricky issues and require decisions and standardized way of description (and they must be brief, intuitive, full of meaning):
- How do we choose omitted category/reference group?
- Why is there no level-1 error term in logistic regression?
- Why use HLM?
- Why use logistic regression model?
- Meaning of odds ratio
- Effect size interpretation (Why 2.0 is often used)
- Why use certain covariates
- How do we talk about predictors, covariates, and the treatment indicator (1 if treatment subject; else 0). There seems a difference between predictors and covariates.
- How to discuss variance change (R2, etc.)
- Negative level-2 variance in case of HLM
- What do we do when between-group variance is small (the model may not converge)
- What to do when the model does not converge?
- How to deal with model names such as HLM, HGLM, etc.
- When converting a scale or ordinal variable into a binary variable as an outcome of logistic regression, there are many possible cutpoints to define 0 vs. 1 (low vs. high). How do we justify?