Exploratory graph analysis (EGA) based on network theory has been introduced as a highly reliable and effective method to assess scales' dimensionality. We estimated the dimensional network structure of the Revised University of California Los Angeles Loneliness Scale using EGA among a cross-sectional cohort of Korean older adults living alone ( = 1,041). We also evaluated the stability of estimates using a bootstrap version of EGA (bootEGA) and verified the overall fit structure using confirmatory factor analysis (CFA). EGA revealed a two-dimensional structure of the scale initially. The bootEGA result revealed that Item 4 ("I do not feel alone") did not sufficiently load on any dimension, and Item 20 ("There are people I can turn to") was replicated in two or more dimensions. Removing these items resulted in better stability of the dimensions, leading to excellent structural consistency. CFA confirmed a satisfactory fit of the improved structure. [(1), 15-20.].
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http://dx.doi.org/10.3928/19404921-20230104-03 | DOI Listing |
Educ Psychol Meas
January 2025
Faculty of Psychology and Educational Sciences, KU Leuven, Campus KULAK, Kortrijk, Belgium.
Multidimensional Item Response Theory (MIRT) is applied routinely in developing educational and psychological assessment tools, for instance, for exploring multidimensional structures of items using exploratory MIRT. A critical decision in exploratory MIRT analyses is the number of factors to retain. Unfortunately, the comparative properties of statistical methods and innovative Machine Learning (ML) methods for factor retention in exploratory MIRT analyses are still not clear.
View Article and Find Full Text PDFGen Hosp Psychiatry
December 2024
National Sleep Foundation, Washington, DC 20036, USA.
Objective: The RU_SATED scale is increasingly used across the globe to measure sleep health. However, there is a lack of consensus around its psychometric and diagnostic performance. We conducted an empirical investigation into the psychometrics of the Chinese version of the RU_SATED (RU_SATED-C) scale, with a focus on structural validity and diagnostic performance.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
Connectomes' topological organization can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to 10 graph theoretic nodal properties. These properties assign a score to nodes, reflecting their meaning in the network.
View Article and Find Full Text PDFAdv Mater
December 2024
Laboratory for Atomistic and Molecular Mechanics (LAMM), Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
A key challenge in artificial intelligence (AI) is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, SciAgents, an approach that leverages three core concepts is presented: (1) large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses human research methods.
View Article and Find Full Text PDFBrain Struct Funct
December 2024
Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
To investigate the microstructural integrity, tract volume analysis, and functional connectivity (FC) alterations of the left uncinate fasciculus (UF) in patients with amyotrophic lateral sclerosis (ALS) compared to healthy controls (HCs). Fourteen limb-onset ALS patients were recruited at baseline and ten at follow-up, along with 14 HCs. All participants underwent 3D T1-weighted, diffusion tensor imaging and kurtosis imaging (DTI/DKI), and resting-state functional MRI (rs-fMRI) using a 3 Tesla scanner with 64-channel coils.
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