6. The Simultaneous Decision(s) about the Number of Lower- and Higher-Level Classes in Multilevel Latent Class Analysis

Lukociene, O.; Varriale, Roberta; Vermunt, Jeroen K.

doi:10.1111/j.1467-9531.2010.01231.x

Cited by 120 publications

(120 citation statements)

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“…In this study, to obtain the optimal number of clusters, log-likelihood (LL), Akaike information criterion (AIC), AIC3 and the Bayesian information criterion (BIC) were used. The model was chosen based on the results of the study of Lukočienė, Varriale, and Vermunt (2010), which includes a simulation demonstrating why BIC is the right fit index. Thus, the BIC value was used as a criterion for model selection.…”

Section: Resultsmentioning

confidence: 99%

“…In the literature, it is stated that there are disadvantages to including predictors in models when determining the number of latent classes and it is suggested that a three-step analysis is employed in order to compensate for these disadvantages (Gudicha and Vermunt, 2013;Vermunt, 2010). For this reason, this study is significant both for the way it departs from the work reported in the literature in terms of the process of including predictor variables in the model, and presenting the model used in the work.…”

Section: Introductionmentioning

confidence: 99%

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Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

Yalçın¹

2017

International Journal of Research in Education and Science

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

Yalçın¹

2017

International Journal of Research in Education and Science

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“…Or, per the second approach discussed earlier, another latent classification variable can be added-here, a Level 2 classification variable-and Level 1 latent class membership can be made dependent on Level 2 latent class membership. This implies local independence of outcomes per person given a combination of Level 1 and Level 2 class membership (Lukociene, Varriale, & Vermunt, 2010). It is also possible to use these two approaches in one model.…”

Section: Additional Mixture Models Using the Two Approaches For Relaxmentioning

confidence: 99%

Understanding Linkages Among Mixture Models

Sterba

2013

Multivariate Behavioral Research

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The methodological literature on mixture modeling has rapidly expanded in the past 15 years, and mixture models are increasingly applied in practice. Nonetheless, this literature has historically been diffuse, with different notations, motivations, and parameterizations making mixture models appear disconnected. This pedagogical review facilitates an integrative understanding of mixture models. First, 5 prototypic mixture models are presented in a unified format with incremental complexity while highlighting their mutual reliance on familiar probability laws, common assumptions, and shared aspects of interpretation. Second, 2 recent extensionshybrid mixtures and parallel-process mixtures-are discussed. Both relax a key assumption of classic mixture models but do so in different ways. Similarities in construction and interpretation among hybrid mixtures and among parallel-process mixtures are emphasized. Third, the combination of both extensions is motivated and illustrated by means of an example on oppositional defiant and depressive symptoms. By clarifying how existing mixture models relate and can be combined, this article bridges past and current developments and provides a foundation for understanding new developments.

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“…The fact that the BIC performance needs to be scrutinized is illustrated by the fact that, for the application, the BIC selected seven clusters, which appears to be an overselection when comparing cluster-specific factors and considering the (lack of) interpretability and stability of the clustering. Adaptations that will be considered include the hierarchical BIC (Zhao, Jin, & Shi, 2015;Zhao, Yu, & Shi, 2013) and stepwise procedures like the one described by Lukočienė, Varriale, and Vermunt (2010). Their performances will be investigated and compared, also for the more intricate case wherein the number of factors might vary across clusters.…”

Section: Discussionmentioning

confidence: 99%

Mixture Simultaneous Factor Analysis for Capturing Differences in Latent Variables Between Higher Level Units of Multilevel Data

Roover

Vermunt

Timmerman

et al. 2017

Structural Equation Modeling: A Multidisciplinary Journal

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Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (e.g., personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that might explain the covariances among variables; for example, the Big Five personality structure. In the case of multilevel data, one might wonder whether or not the same covariance (factor) structure holds for each so-called data block (containing data of 1 higher level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups or blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.Keywords: factor analysis, latent variables, mixture model clustering, multilevel data Given multivariate data, researchers often wonder whether the variables covary to some extent and in what way. For instance, in personality psychology, there has been a debate about the structure of personality measures (i.e., the Big Five vs. Big Three debate; De Raad et al., 2010). Similarly, emotion psychologists have discussed intensely whether and how emotions as well as norms for experiencing emotions can be meaningfully organized in a lowdimensional space (e.g., Ekman, 1999;Fontaine, Scherer, Roesch, & Ellsworth, 2007;Russell & Barrett, 1999;Stearns, 1994). Factor analysis (Lawley & Maxwell, 1962) is an important tool in these debates as it explains the covariance structure of the variables by means of a few latent variables, called factors. When the researchers have a priori assumptions on the number and nature of the underlying latent variables, confirmatory factor analysis (CFA) is often used, whereas exploratory factor analysis (EFA) is applied when one has no such assumptions.Research questions about the covariance structure get further ramifications when the data have a multilevel structure; for instance, when personality measures are available for inhabitants from different countries. We refer to data organized according to the higher level units (e.g., the countries) as data blocks. For multilevel data, one can wonder whether or not the same structure holds for each data block. For example, is the Big Five personality structure found in each country or not (De Raad et al., 2010) Setiadi, & Markam, 2002;MacKinnon & Keating, 1989;Rodriguez & Church, 2003).When looking for differences and similarities in covariance structures, using EFA is very advantageous because it leaves more room for finding differences than CFA does. For instance, in the emotion norm example (Eid & Diener, 2001), one might very well expect two latent variables to show up in each country corresponding to approved and disap...

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6. The Simultaneous Decision(s) about the Number of Lower- and Higher-Level Classes in Multilevel Latent Class Analysis

Cited by 120 publications

References 43 publications

Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

Determining the Relationships between Selected Variables and Latent Classes in Students’ PISA Achievement

Understanding Linkages Among Mixture Models

Mixture Simultaneous Factor Analysis for Capturing Differences in Latent Variables Between Higher Level Units of Multilevel Data

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