Johan Lyrvall
Biographic Data
| ID | 6630005 |
|---|---|
| NAME | Johan Lyrvall |
| GIVEN NAMES | Johan |
| FAMILY NAME | Lyrvall |
| SIGNATURE | LYRVALL J |
| AFFILIATIONS | University of Catania |
| ORCID | 0000-0002-1863-8147 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Meaningful Latent Class Analysis
This article integrates existing theoretical and applied teaching resources to provide an updated overview of best practices for latent class analysis (LCA), focusing on applied researchers. We first clarify the unique usefulness of LCA compared to related latent variable models. We then focus on three fundamental research questions relevant for the applied researcher: (1) do I have data with a possible clustering structure that can be analyzed u…
Multilevel Latent Class Analysis
Latent class (LC) analysis is a model-based clustering approach for categorical data, with a wide range of applications in the social sciences and beyond. When the data have a hierarchical structure, the multilevel LC model can be used to account for higher-level dependencies between the units by means of a further categorical LC variable at the group level. The research interest of LC analysis typically lies in the relationship between the LCs a…
Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence
We consider estimation of two-level latent class models for clustered data, when the measurement model for the observed measurement items includes non-equivalence of measurement with respect to some observed covariates. The parameters of interest are coefficients in structural models for the latent classes given covariates. We propose a two-step method of estimation. This extends previously proposed methods of two-step estimation for models witho…
Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates
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 th…
No prominent works on this page.
Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates
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 th…
Multilevel Latent Class Analysis
Latent class (LC) analysis is a model-based clustering approach for categorical data, with a wide range of applications in the social sciences and beyond. When the data have a hierarchical structure, the multilevel LC model can be used to account for higher-level dependencies between the units by means of a further categorical LC variable at the group level. The research interest of LC analysis typically lies in the relationship between the LCs a…
Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence
We consider estimation of two-level latent class models for clustered data, when the measurement model for the observed measurement items includes non-equivalence of measurement with respect to some observed covariates. The parameters of interest are coefficients in structural models for the latent classes given covariates. We propose a two-step method of estimation. This extends previously proposed methods of two-step estimation for models witho…
Meaningful Latent Class Analysis
This article integrates existing theoretical and applied teaching resources to provide an updated overview of best practices for latent class analysis (LCA), focusing on applied researchers. We first clarify the unique usefulness of LCA compared to related latent variable models. We then focus on three fundamental research questions relevant for the applied researcher: (1) do I have data with a possible clustering structure that can be analyzed u…
Latent class model (3 works) · Artificial Intelligence (2 works) · Cluster analysis (2 works) · Computer Science (2 works) · Mathematics (2 works) · Statistics (2 works) · Advanced Statistical Methods and Models (1 work) · Advanced Statistical Modeling Techniques (1 work) · Art (1 work) · Artificial Intelligence (1 work)