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This paper focuses on the substantive application of multilevel LCA to theevolution of citizenship norms in a diverse array of democratic countries. Todo so, we present a two-stage approach to fit multilevel latent class models:in the first stage (measurement model construction), unconditional classenumeration is done separately on both low and high level latent variables,estimating only a part of the model at a time -- hence keeping the remainingpart fixed -- and then updating the full measurement model; in the second stage(structural model construction), individual and/or group covariates areincluded in the model. By separating the two parts -- first stage and secondstage of model building -- the measurement model is stabilized and is allowedto be determined only by it's indicators. Moreover, this two-step approachmakes the inclusion/exclusion of a covariate a relatively simple task tohandle. Our proposal amends common practice in applied social science research,where simple (low-level) LCA is done to obtain a classification of low-levelunit, and this is then related to (low- and high-level) covariates simplyincluding group fixed effects. Our analysis identifies latent classes thatscore either consistently high or consistently low on all measured items, alongwith two theoretically important classes that place distinctive emphasis onitems related to engaged citizenship, and duty-based norms.

Multilevel latent class analysis with covariates: Analysis of cross-national citizenship norms with a two-stage approach