Extension to the PATHMOX approach to detect which constructs differentiate segments and to test factor invariance
In this paper we propose an extension to the PATHMOX segmentation algorithm to detect which endogenous latent variables and predictors are responsible for heterogeneity. We also address the problem of factor invariance in the terminal nodes of PATHMOX. We demonstrate the utility of such methodology...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:uabarcelona_::f3cfdfc33c4a169ef1df862156b370de |
| Acceso en línea: | https://ddd.uab.cat/record/322127 https://dx.doi.org/urn:doi:10.1007/978-3-319-40643-5_19 |
| Access Level: | acceso abierto |
| Palabra clave: | Latent variables PATHMOX Segmentation SDG 3 - Good Health and Well-being |
| Sumario: | In this paper we propose an extension to the PATHMOX segmentation algorithm to detect which endogenous latent variables and predictors are responsible for heterogeneity. We also address the problem of factor invariance in the terminal nodes of PATHMOX. We demonstrate the utility of such methodology on real mental health data by investigating the relationship between dementia, depression and delirium. |
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