Complex latent variable modeling for multivariate hierarchical data
type: CreativeWork
description: This thesis extends, in four independent chapters, the stepwise multilevel latent class analysis with covariates methodological framework. In recent decades, the latent class analysis methodological scholarship has developed sophisticated methodologies, including innovative estimation approaches and open-source software implementations, yet many of these methodological advancements have not yet been extended to the multilevel modeling context. With the increasing availability of hierarchical data, such as cross-national surveys, multilevel latent class analysis is becoming increasingly more applicable, and the utility of extending state-of-the-art methods increasingly greater. The first chapter, co-authored by Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, and Jouni Kuha, makes two contributions. First, it compiles state-of-the-art methodologies for multilevel latent class analysis with covariates, describing benchmark model specifications and estimation approaches, and detailing initialization issues and model selection alternatives. Second, it proposes the novel R package multilevLCA package as the first open-source software implementing the described state-of-the-art methodologies, and the first software overall automatically implementing some stepwise and sequential routines. As such, it contributes both to the methodological statistics literature and to the open-source statistical software literature. This chapter is published as the article Multilevel Latent Class Analysis: State-of-the-Art Methodologies and Their Implementation in the R Package multilevLCA in the journal Multivariate Behavioral Research. The second chapter, co-authored with Zsuzsa Bakk, Jennifer Oser, and Roberto Di Mari, proposes a novel bias-adjusted three-step estimation approach for multilevel latent class analysis models with covariates. This contributes, to the methodological statistics literature, with the first modeling option for multilevel latent class analysis models with covariates …
abstract: This thesis extends, in four independent chapters, the stepwise multilevel latent class analysis with covariates methodological framework. In recent decades, the latent class analysis methodological scholarship has developed sophisticated methodologies, including innovative estimation approaches and open-source software implementations, yet many of these methodological advancements have not yet been extended to the multilevel modeling context. With the increasing availability of hierarchical data, such as cross-national surveys, multilevel latent class analysis is becoming increasingly more applicable, and the utility of extending state-of-the-art methods increasingly greater. The first chapter, co-authored by Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, and Jouni Kuha, makes two contributions. First, it compiles state-of-the-art methodologies for multilevel latent class analysis with covariates, describing benchmark model specifications and estimation approaches, and detailing initialization issues and model selection alternatives. Second, it proposes the novel R package multilevLCA package as the first open-source software implementing the described state-of-the-art methodologies, and the first software overall automatically implementing some stepwise and sequential routines. As such, it contributes both to the methodological statistics literature and to the open-source statistical software literature. This chapter is published as the article Multilevel Latent Class Analysis: State-of-the-Art Methodologies and Their Implementation in the R Package multilevLCA in the journal Multivariate Behavioral Research. The second chapter, co-authored with Zsuzsa Bakk, Jennifer Oser, and Roberto Di Mari, proposes a novel bias-adjusted three-step estimation approach for multilevel latent class analysis models with covariates. This contributes, to the methodological statistics literature, with the first modeling option for multilevel latent class analysis models with covariates …
author: LYRVALL, JOHAN OLOF
language: eng
keywords: Latent class analysi, Multilevel, Covariate, Stepwise estimation, Categorical data
date published: 2025-12-11
date modified: 2026-07-24T01:35:22Z
provider: Università degli Studi di Catania
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli studi di Catania

PropertyValue
property ID: Handle
value: https://hdl.handle.net/20.500.11769/704891