Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research. Issue 2 (13th December 2021)

• Record Type: Journal Article
• Authors: Haberstumpf, Sophia; Forster, André; Leinweber, Jonas; Rauskolb, Stefanie; Hewig, Johannes; Sendtner, Michael; Lauer, Martin; Polak, Thomas; Deckert, Jürgen; Herrmann, Martin J.
• Is Part Of: Journal of neuropsychology. Volume 16:Issue 2(2022)
• Page Start: 324
• Page End: 352
• Publication Date: 2021-12-13
• Subjects: latent factors -- measurement invariance -- composite -- neuropsychiatric test battery -- cognitive decline; Neuropsychology -- Periodicals; Brain -- Diseases -- Psychological aspects -- Periodicals; 612.82305
• DOI: 10.1111/jnp.12269
• Languages: English
• ISSNs: 1748-6645

Abstract

Abstract : 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 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.