Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research

Please always quote using this URN: urn:nbn:de:bvb:20-opus-318932
  • Objective Alzheimer’s disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of compositeObjective Alzheimer’s disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline. Methods An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart). Results EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual–spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach. Conclusion The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.show moreshow less

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Metadaten
Author: Sophia Haberstumpf, André Forster, Jonas Leinweber, Stefanie Rauskolb, Johannes Hewig, Michael Sendtner, Martin Lauer, Thomas Polak, Jürgen Deckert, Martin J. Herrmann
URN:urn:nbn:de:bvb:20-opus-318932
Document Type:Journal article
Faculties:Medizinische Fakultät / Institut für Klinische Neurobiologie
Medizinische Fakultät / Klinik und Poliklinik für Psychiatrie, Psychosomatik und Psychotherapie
Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Psychologie
Language:English
Parent Title (English):Journal of Neuropsychology
Year of Completion:2022
Volume:16
Issue:2
First Page:324
Last Page:352
Source:Journal of Neuropsychology 2022, 16(2):324-352. DOI: 10.1111/jnp.12269
DOI:https://doi.org/10.1111/jnp.12269
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:Alzheimer’s disease; early-onset predictors; mild cognitive impairment
Release Date:2023/08/16
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International