Roberto Di Mari
Biographic Data
| ID | 7245973 |
|---|---|
| NAME | Roberto Di Mari |
| GIVEN NAMES | Roberto |
| FAMILY NAME | Di Mari |
| SIGNATURE | DI MARI R |
| AFFILIATIONS | University of Catania |
| ORCID | 0000-0001-5498-009X |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
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…
Students' proficiency evaluation
In higher education, students' assessment has a two-fold aim: (i) evaluate students' proficiency level concerning the topics of a specific course; (ii) identify students' weaknesses throughout the whole learning activity and, if any, relate them to a set of socio-demographic and psychological covariates/predictors. In this vein, this manuscript proposes a multilevel latent class model as an analytic strategy to detect homogeneous groups of studen…
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…
Changing citizenship norms among adolescents, 1999-2009-2016
Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models
We propose a general approach to detect measurement non-invariance in latent Markov models for longitudinal data. We define different notions of differential item functioning in the context of panel data. We then present a model selection approach based on the Bayesian information criterion (BIC) to choose both the number of latent states and the measurement structure. We show the practical relevance by means of an extensive simulation study, and…
Two-stage Multilevel Latent Class Analysis with Covariates in the Presence of Direct Effects
In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed two-stage and the classical one-stage approach for models where a direct effect exists between indicators o…
Dynamic Discrete Mixtures for High-Frequency Prices
The tick structure of the financial markets entails discreteness of stock price changes. Based on this empirical evidence, we develop a multivariate model for discrete price changes featuring a mechanism to account for the large share of zero returns at high frequency. We assume that the observed price changes are independent conditional on the realization of two hidden Markov chains determining the dynamics and the distribution of the multivaria…
No prominent works on this page.
Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models
We propose a general approach to detect measurement non-invariance in latent Markov models for longitudinal data. We define different notions of differential item functioning in the context of panel data. We then present a model selection approach based on the Bayesian information criterion (BIC) to choose both the number of latent states and the measurement structure. We show the practical relevance by means of an extensive simulation study, and…
Two-stage Multilevel Latent Class Analysis with Covariates in the Presence of Direct Effects
In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed two-stage and the classical one-stage approach for models where a direct effect exists between indicators o…
Dynamic Discrete Mixtures for High-Frequency Prices
The tick structure of the financial markets entails discreteness of stock price changes. Based on this empirical evidence, we develop a multivariate model for discrete price changes featuring a mechanism to account for the large share of zero returns at high frequency. We assume that the observed price changes are independent conditional on the realization of two hidden Markov chains determining the dynamics and the distribution of the multivaria…
Changing citizenship norms among adolescents, 1999-2009-2016
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…
Students' proficiency evaluation
In higher education, students' assessment has a two-fold aim: (i) evaluate students' proficiency level concerning the topics of a specific course; (ii) identify students' weaknesses throughout the whole learning activity and, if any, relate them to a set of socio-demographic and psychological covariates/predictors. In this vein, this manuscript proposes a multilevel latent class model as an analytic strategy to detect homogeneous groups of studen…
Mathematics (6 works) · Statistics (6 works) · Computer Science (4 works) · Econometrics (4 works) · Latent class model (4 works) · Latent variable (4 works) · Multilevel model (4 works) · Structural equation modeling (4 works) · Artificial Intelligence (3 works) · Covariate (2 works)