Latent structure and factorial invariance of a neuropsychological test battery for the study of preclinical Alzheimer's disease

Neuropsychology. 2010 Nov;24(6):742-756. doi: 10.1037/a0020176.

Abstract

Objective: To examine the latent structure of a test battery currently being used in a longitudinal study of asymptomatic middle-aged adults with a parental history of Alzheimer's disease (AD) and test the invariance of the factor solution across subgroups defined by selected demographic variables and known genetic risk factors for AD.

Method: An exploratory factor analysis (EFA) and a sequence of confirmatory factor analyses (CFA) were conducted on 24 neuropsychological measures selected to provide a comprehensive estimate of cognitive abilities most likely to be affected in preclinical AD. Once the underlying latent model was defined and the structural validity established through model comparisons, a multigroup confirmatory factor analysis model was used to test for factorial invariance across groups.

Results: The EFA solution revealed a factor structure consisting of five constructs: verbal ability, visuospatial ability, speed & executive function, working memory, and verbal learning & memory. The CFA models provided support for the hypothesized 5-factor structure. Results indicated factorial invariance of the model across all groups examined.

Conclusions: Collectively, the results suggested a relatively strong psychometric basis for using the factor structure in clinical samples that match the characteristics of this cohort. This confirmed an invariant factor structure should prove useful in research aimed to detect the earliest cognitive signature of preclinical AD in similar middle aged cohorts.

MeSH terms

  • Adult
  • Aged
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / physiopathology
  • Alzheimer Disease / psychology*
  • Cognition / physiology*
  • Factor Analysis, Statistical
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Models, Psychological
  • Neuropsychological Tests* / statistics & numerical data
  • Predictive Value of Tests
  • Principal Component Analysis / methods*
  • Reproducibility of Results