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Author(s): Patricio Soares Costa
Nadine Correia Santos
Pedro Cunha
Joana Almeida Palha
Nuno Sousa
Title: The Use of Bayesian Latent Class Cluster Models to Classify Patterns of Cognitive Performance in Healthy Ageing
Issue Date: 2013
Abstract: The main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.
Subject: Psicologia
Scientific areas: Ciências sociais::Psicologia
Social sciences::Psychology
Document Type: Artigo em Revista Científica Internacional
Rights: openAccess
Appears in Collections:FPCEUP - Artigo em Revista Científica Internacional

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