Stepwise latent class analysis
The main methodological aspects of the use of stepwise LCA estimators under different circumstances.
Zsuzsa Bakk's main research interest is in the area of stepwise latent class modeling.
Latent class analysis (LCA) is used by social and behavioral scientists as a statistical method for building typologies, taxonomies, and classifications based on a set of observed characteristics. Examples include attitudinal typologies of citizens based on survey questions measuring their attitudes toward freedom of speech, subtypes of schizophrenia patients derived from recorded mood symptoms, or taxonomies of temporal project networks based on characteristics of these projects and the related organizations.
Zsuzsa Bakk's work focuses on a stepwise estimation approach of LC models with external variables. The approach proceeds as follows: first the underlying latent construct is estimated based on a set of observed indicator variables, then in the second step individuals are assigned to the latent classes, and in the third step the class assignments from step two are used in further analyses.
The approach can be used to estimate complex LC models that include multiple predictors & distal outcomes, to correct for measurement error when no golden standard is available etc.
She is furthermore interested in the application of biplot methodology as a multivariate visualization tool to graph LC models with more then 3 classes.