Tricky things to describe about stat model specification

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?

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