# Survey Methods Filter: Weights when the sampling design is unknown

October 12, 2017 4:07 PM Subscribe

I'm trying to construct weights to use for the analysis of survey data. I know the population demographics, and have already calculated post-stratification weights to account for unit non-response. Unfortunately, I don't have information about the sampling design or sampling frame. Is it OK to only use the post-stratification weights?

In the ideal world, the survey weights would account for: 1) the probability of selection, and 2) unit non-response. I've already created weights to adjust for unit non-response. As a result, the sample demographics now match the population demographics. But I can't adjust for the probability of selection because I don't know the sampling design. My best guess is that it was either a convenience sample or an attempt to survey the entire population (i.e., a census).

My hunch is that it's better to use the post-stratification weights than none at all so that the sample is demographically representative of the population. But I haven't been able to find a source to verify this. How problematic is it if the weights don't adjust for the probability of selection?

If it matters, the analyses I plan to use are pretty straightforward: descriptive statistics, t-tests, correlations, and maybe some chi-square tests.

In the ideal world, the survey weights would account for: 1) the probability of selection, and 2) unit non-response. I've already created weights to adjust for unit non-response. As a result, the sample demographics now match the population demographics. But I can't adjust for the probability of selection because I don't know the sampling design. My best guess is that it was either a convenience sample or an attempt to survey the entire population (i.e., a census).

My hunch is that it's better to use the post-stratification weights than none at all so that the sample is demographically representative of the population. But I haven't been able to find a source to verify this. How problematic is it if the weights don't adjust for the probability of selection?

If it matters, the analyses I plan to use are pretty straightforward: descriptive statistics, t-tests, correlations, and maybe some chi-square tests.

*Can you add more information about how and why you adjusted for non-response?*

I adjusted for non-response because the means for several key outcome variables differed notably for demographic groups (e.g., gender) that were over or underrepresented in the sample.

*Saying "unit non-response" makes me think that this is clustered - is that correct?*

The data is not clustered.

posted by oiseau at 5:51 PM on October 12

You are not logged in, either login or create an account to post comments

If you are in fact an epi (just looked in your history!), I'm happy to discuss further!

posted by quadrilaterals at 5:29 PM on October 12