We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for measurement error introduced in the second step. Simulation studies and an empirical example show that the three-step method is a legitimate modeling option compared to the existing one-step and two-step methods. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates
Johan Lyrvall
;Roberto Di Mari
2024-01-01
Abstract
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for measurement error introduced in the second step. Simulation studies and an empirical example show that the three-step method is a legitimate modeling option compared to the existing one-step and two-step methods. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.File | Dimensione | Formato | |
---|---|---|---|
Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates.pdf
accesso aperto
Tipologia:
Versione Editoriale (PDF)
Licenza:
Creative commons
Dimensione
1.91 MB
Formato
Adobe PDF
|
1.91 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.