Within the context of a research proposal in the social sciences, I was asked the following question:
I have always gone by 100 + m (where m is the number of predictors) when determining minimum sample size for multiple regression. Is this appropriate?
I get similar questions a lot, often with different rules of thumb. I've also read such rules of thumb quite a lot in various textbooks. I sometimes wonder whether popularity of a rule in terms of citations is based on how low the standard is set. However, I'm also aware of the value of good heuristics in simplifying decision making.
- What is the utility of simple rules of thumb for minimum sample sizes within the context of applied researchers designing research studies?
- Would you suggest an alternative rule of thumb for minimum sample size for multiple regression?
- Alternatively, what alternative strategies would you suggest for determining minimum sample size for multiple regression? In particular, it would be good if value is assigned to the degree to which any strategy can readily be applied by a non-statistician.