#psychometrics community: I found a paper that developed a short scale and tested it via #LPA and #kmeans clustering. (Paper here: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281021). Now for me, this is odd as it uses two clustering techniques to assess scale quality. But then again this is a sociology paper and I know that sociologists and psychologists have a different world view. In case you didn't know: Sociologists tend to look at groups within society or societies at large, whereas psychologists tend to see individuals and groups as aggregates of individuals. Obviously, coming from a sociological perspective, using such clustering methods makes sense. However, I still have mixed feelings about this approach. I still feel a IRT approach would be better since obviously k-means and LPA does NOTHING to evaluate items, for example.
The basic rationale is to use random split-half data to identify what's "true" versus sampling error. Scores are based on similarities between eigenvectors or cluster centres, rather than, e.g., the shape of the eigenvalue plot.