Latent factor linear mixed model (LFLMM) for modelling Flanders data

Yenni Angraini, Khairil Anwar Notodiputro, Asep Saefuddin, Toni Toharudin

Abstract


Latent factor linear mixed model (LFLMM) is a method that generally used for the analysis of change in high- dimensional longitudinal data. The LFLMM framework works under the linear mixed model framework. Analysis of change from several latent variables such as Individualism (I), Nationalism (N), Ethnocentrism (E), and Authoritarianism (A) in Flanders, Belgium, is interesting as Belgium is feared to fall apart as a nation. Two main research questions in the Flanders case are how Individualism (I), Nationalism (N), Ethnocentrism (E), and Authoritarianism (A) develop over time and whether there exist association between the Individualism (I), Nationalism (N), Ethnocentrism (E), and Authoritarianism (A) developments. Although these latent variables have been the subject of several studies in Flanders, an analysis of all four concepts using Latent factor linear mixed model has not been performed. Hence, it is the interest of this paper to discuss such model using the Flanders data. Two stages of modelling have been carried out. The first stage involved modelling Individualism (I), Nationalism (N), and Ethnocentrism (E) and in the next stage Authoritarianism (A) was added to the model. The results showed that I, N, and A increased over time while E decreased over time. The correlation of random effects in LFLMM suggest several interesting findings, including the positive correlation between E and A; I and E; and also between I and A.

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Published: 2020-05-28

How to Cite this Article:

Yenni Angraini, Khairil Anwar Notodiputro, Asep Saefuddin, Toni Toharudin, Latent factor linear mixed model (LFLMM) for modelling Flanders data, Commun. Math. Biol. Neurosci., 2020 (2020), Article ID 27

Copyright © 2020 Yenni Angraini, Khairil Anwar Notodiputro, Asep Saefuddin, Toni Toharudin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

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