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Exploring the Factor Structure of Neurocognitive Measures in Older Individuals

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  • Nadine Correia Santos
  • Patrício Soares Costa
  • Liliana Amorim
  • Pedro Silva Moreira
  • Pedro Cunha
  • Jorge Cotter
  • Nuno Sousa

Abstract

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the “best fit” model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.

Suggested Citation

  • Nadine Correia Santos & Patrício Soares Costa & Liliana Amorim & Pedro Silva Moreira & Pedro Cunha & Jorge Cotter & Nuno Sousa, 2015. "Exploring the Factor Structure of Neurocognitive Measures in Older Individuals," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-18, April.
    • Handle: RePEc:plo:pone00:0124229
    DOI: 10.1371/journal.pone.0124229
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    Cited by:

    1. Chia-Yeh Chou & Chien-Yu Huang & Yi-Jing Huang & Gong-Hong Lin & Sheau-Ling Huang & Shu-Chun Lee & Ching-Lin Hsieh, 2017. "Comparison of construct validity of two short forms of Stroke-Specific Quality of Life scale," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    2. Gignac, Gilles E. & Kretzschmar, André, 2017. "Evaluating dimensional distinctness with correlated-factor models: Limitations and suggestions," Intelligence, Elsevier, vol. 62(C), pages 138-147.

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