Sample designing

[meh-wzdech]

Hi! I came around your package and I'm not fully sure if it does what I'm looking for. If no - you can treat my post as a slight suggestion. So, I'm looking for a library to design a sample for a survey which is going to be conducted (hence no existing data yet). I have desired proportions of key variables and would love to get the full sample design - i.e. all variables combined with proportions and counts. Let's say my goal is to get n = 2000 with the stratification of 3 key variables (gender, age, education) below:

expected_coverage: dict = {
    "woman": 0.64,
    "man": 0.36,
    "18-30": 0.20,
    "31-50": 0.50,
    "50+": 0.30,
    "lower": 0.30,
    "mid": 0.50,
    "higher": 0.20
}

As you can see I treat those variables separately. For example, I need to check what would be the proportion and counts of men, aged 18-30 with higher education (obviously each variable values should not be combined with themselves, within a group). Is there any way to get a sample design with samplics? You can also check my Stack Overflow post to read about the whole issue: SO post.

As a side note: I'm fully aware that this problem could be quite easily solved using random/numpy/pandas libraries but anyways I'm curious if samplics could offer more convenient solution. Additionally, a comprehensive and reliable tool to complex sample designs would be priceless.

[MamadouSDiallo]

Hi @meh-wzdech
I do not fully understand your question. Let me know if this is what you want to achieve. You have a total sample size of 2,000 and you want to allocate it across the 18 strata formed by gender, age, and education? If so, do you want allocate proportionally or else?

[meh-wzdech]

Hello @MamadouSDiallo. Yes, I would like to create a sample design with those strata (e.g. woman, 18-30, lower education; man, 50+, higher education etc.). The problem is to allocate each strata in a way that, as a result, in the total sample there would be roughly 64% women, 36% men; 20% aged 18-30, 50% aged 31-50, 30% aged 50+; 30% with lower education, 50% with middle education and 20% with higher education.