I sometimes speak to researchers studying the relationship between variables in clinical psychology samples. A typical example is as follows:
- 100 patients with diagnosed depression
- many clinically relevant variables are measured such as demographics, anxiety, intelligence, drug and alcohol use, and so on.
Researchers often want to then develop models of what predicts depression.
However, the problem is that the sample was selected because they already have been deemed to possess a certain threshold level of depression. Substantial variation still remains in depression with some participants being more severe than others.
Thus, there are problems in trying to generalise observed relationships to describe what predicts depression, because it is not a random sample of the population.
I use depression as a specific case, but the problem applies to many studies of clinical populations (e.g., kids with behavioural problems, kids with intellectual disabilities, OCD, etc.).
- What advice would you give to such researchers about how to analyse and generalise from such data?
- Are there any references that provide an example of best practice of how to analyse and generalise from such data?