How can one allow conditional dependence in LCA?
The usual latent class model assumes that variables are independent within latent classes. This is sometimes an untenable assumption. For example, two items may be alternative measures of same basic construct (e.g., “cigarettes smoked in the last 30 days” and “cigarettes smoked in the last year” or measure closely related traits (e.g., anxiety and agitation.) In such cases, manifest variables would be assumed to be associated within latent classes, a situation termed conditional dependence or local dependence. The standard LCA model must be modified to account for this. Progress has been made in recent years in methods for detecting conditional dependence and in relaxing the conditional independence assumptions of LCA. For more detailed discussion, including example programs, click here.
