I am a biologist with an interest in experiment design. I recently refreshed my memory about what kinds of response variables that are possible, and made this chart (see jpeg). I would like feedback whether it is correct or not. I think it would be helpful to have a chart like this to show to my fellow biologists, who don't get much formal training in DOE/stats. Thanks! Chris
=== Responses to Bjorn's Comments ===
Hi Bjorn, Thanks for your comments, let me respond to your points one-by-one:
:: You miss some big categories: [0, Inf) and/or (0, Inf), time-to-event with censoring, multivariate outcomes (discrete or continuous) etc.
- Is there already a list of all response variable categories that includes the types I missed? I do not know time-to-event or multivariate responses (these are likely more advanced than what i see in my field of cell biology)
- Are [0, Inf) and (0,Inf) on the real number line, or are these integer responses? Also, do these distributions tend to appear in certain fields over others?
:: Where I start to have a real headache is the "convert data to"
- In the chart, "convert data" refers to the internal "conversion" of count data, from a contingency table for example, into proportion or log-odds data by the model fitting algorithm. I do not know how the math works, but from the outside it appears to me that a kind of "conversion" has occurred after fitting these models. Alternatively, the phrase "convert data" could be switched to "count data treated as…", or something like that.
:: Response distribution (omitting lots and lots of obvious and useful distributions)
- From my experience i only know about the following major response distributions that can be used for modeling: gaussian, poisson, multinomial, and binomial
- Are there other distributions commonly used, and are they associated with certain fields over others?
:: Model (any "modern" models from the 1960s onwards like logistic regression?
- I believe the logit model is equivalent to logistic regression, but please correct me if that's wrong
:: Hierachical models?
- What are hierarchical models and what are they used for?
:: Covariate adjustment?
- What is covariate adjustment? If covariate adjustment is the inclusion of a continuous predictor variable with a categorical predictor, then maybe I can treat it as a separate issue from response variables, as it deals exclusively with the predictors, but again please correct me if that is not correct
Thanks again for your comments, Cheers, Chris