What is a latent class?
LCA defines latent classes by the criterion of “conditional independence.” This means that, within each latent class, each variable is statistically independent of every other variable. For example, within a latent class that corresponds to a distinct medical syndrome, the presence/absence of one symptom is viewed as unrelated to presence/absence of all others. To say this differently, latent classes are defined such that, if one removes the effect of latent class membership on the data, all that remains is randomness (understood here as complete independence among measures). Paul Lazarsfeld (Lazarsfeld & Henry, 1968; see also his earlier papers), the main originator of LCA, argued that this criterion leads to the most natural and useful groups. For some applications, conditional independence may be an inappropriate assumption. For example, one may have two very similar items, such that responses on them are probably always associated. For this and certain related situations, extension
Related Questions
- Can I obtain the predicted probability of membership in each latent class or latent status for each individual?
- Can I obtain the predicted probability of membership in each latent class/status for each individual?
- Can latent class conjoint models be estimated with traditional statistical modeling software?.
