Hello fellow StackExchange users,
Preamble: I have been tasked with performing a cluster analysis (or possibly a latent class analysis, as I am pondering) to find non-overlapping groups of like subjects presenting with similar psycho-social traits, as measured in the dataset. There are a priori hypotheses about the number of groups and their characteristics, but the analysis is to be exploratory rather than confirmatory nonetheless. The dataset contains several thousand subjects and about two-dozen variables of interest. So far, so good.
However, there is a catch: the survey comes from a complex multistage stratified design, which obviously violates the SRS assumption and its resulting traditional standard errors. Such designs often require bootstrapping to provide valid inferences. Observation weights have been provided by the architects of the survey.
My question: given that the statistics are exploratory and not confirmatory, and that no formal inferences of any kind are to be produced in this patient-centered analysis (not even precision estimates such as confidence intervals), can the complex survey design be ignored in good faith?
The only caveat may be one of external validity; certain demographic groups have been oversampled. But using the observation weights when calculating the distance/covariance matrices should account for this, no?