We do not recommend assigning individuals to latent classes or latent statuses based on their posterior probabilities unless there is no viable alternative. By assigning individuals to latent classes or latent statuses, you introduce error into your results. There are many different types of analyses that can be performed within the latent class modeling framework (e.g., predicting latent class membership) without having to assign individuals to latent classes or latent statuses. When possible, we recommend working within the latent class modeling framework because it incorporates measurement error into the model, which is ignored by class/status assignment. If you are planning to assign individuals based on posterior probabilies, one article of interest might be:
Goodman, L. A. (2007). On the assignment of individuals to latent classes. Sociological Methodology, 37(1), 1-22. doi: 10.1111/j.1467-9531.2007.00184.x
In this paper, Goodman describes two ways to assign individuals and two criteria that can be used to assess when class assignment is satisfactory and when it is not. If you assign individuals to classes/statuses, we recommend evaluating the amount of measurement error introduced by doing so.
Update: Recent work on measurement error weighting with modal class assignment based on posterior probabilities has proposed a high-quality way to reduce attenuation when assigning individuals to latent classes and conducting a follow-up analysis. In simulation studies, this approach has been shown to work quite well and is robust to violations of homoscedasticity across classes (e.g., in an outcome). This approach, commonly referred to as the “BCH approach”, is the currently recommended approach to latent class analysis with a continuous or binary outcome. You can read more about this approach in the following articles:
Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling, 23, 20-31. doi: 10.1080/10705511.2014.955104
Dziak, J. J., Bray, B. C., Zhang, J.-T., Zhang, M., & Lanza, S. T. (2016). Comparing the performance of improved classify-analyze approaches for distal outcomes in latent profile analysis. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 12, 107-116. doi: 10.1027/1614-2241/a000114 PMCID: In process