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Assessment of Latent Class Detection in PLS Path Modeling: a Simulation Study to Evaluate the Group Quality Index performance

Abstract

Structural Equation Models assume homogeneity across the entire sample. In other words, all the units are supposed to be well represented by a unique model. Not taking into account heterogeneity among units may lead to biased results in terms of model parameters. That is why, nowadays, more attention is focused on techniques able to detect unobserved heterogeneity in Structural Equation Models. However, once unit partition obtained according to the chosen clustering methods, it is important to state if taking into account local models provides better results than using a single model for the whole sample. Here, a new index to assess detected unit partition will be presented: the Group Quality Index. A simulation study involving two different simulation schemes (one simulating the so called null hypothesis of homogeneity among units, and the other taking into account the heterogenous sample case) will be presented.
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Dates and versions

hal-00589567 , version 1 (29-04-2011)

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  • HAL Id : hal-00589567 , version 1

Cite

Laura Trinchera. Assessment of Latent Class Detection in PLS Path Modeling: a Simulation Study to Evaluate the Group Quality Index performance. Classification and Multivariate Analysis for Complex Data Structures - Studies in Classification, Data Analysis, and Knowledge Organization, Springer -Verlag Heidelberg, pp.281-289, 2011. ⟨hal-00589567⟩
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