I have a population of ~500 000 individuals. Each of them either own or don't own a bike, and only 500 own one. I need to divide my population based on several criteria (age, home-town, own a car, etc...), but I have many criteria and I could end up with groups of only one individual.
So I have two questions:
How do I choose the order of criteria for the splitting : e.g., should I split first by "age" and then by "home-town" or the opposite? What tool can I use to find that?
When do I stop splitting, e.g. what is the minimal representative population for a group ?
I found a very large number of ways to determine a sample population, and I am a bit lost about how to choose the best one for my case. My issue is that I have only 0.1% of the population who owns a bike.
As for the splitting method, I though about ordering my criteria in ascending number of possible outcomes : home-town may have 1000 different outcomes, but "own a car" only have 2 : True or False. So I was thinking about splitting by "own a car" first, then by "age" and then by "home-town". That being said, I think there is a better (and proper) way to handle this.
- Why do want to split your data?
I want to see if some groups (defined by a set of characteristics) are more susceptible to buy bikes than other groups.
- In how many sets do you want to split the data?
As much as possible, given there is enough individual to remain representative
- Do these datasets need to be of (roughly) equal size?