An Exploratory Psychometric Network Analysis of Loneliness Scales in a Sample of Older Adults.

Thompson, Aaron; Tv, Pollet

doi:10.31234/osf.io/fauc4

Cited by 2 publications

(4 citation statements)

References 49 publications

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“…Lastly, consistent with previous research, we find a single-item, direct measure to be valid and useful for measuring loneliness (Thompson & Pollet, 2023).…”

Section: Loneliness and Social Isolationsupporting

confidence: 90%

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Extraverted, but Lonely: Noise- and Bias-free ‘True’ Correlations Between Loneliness and the Big Five Personality Domains

Maher,

Dragostinov,

Vainik

et al. 2024

Preprint

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We conducted the largest study to date to examine the true associations between loneliness and personality traits combining self and informant reports. Using a method described by Mõttus and colleagues (2023) we obtained noise- and bias-free ‘true’ correlations (rtrues), which were used to produce regression model estimates of the true independent associations (βtrues) of age, sex and Big Five personality domains with loneliness across three multicultural samples (Estonian-speaking, n = 20,893; Russian-speaking, n = 762; English-speaking, n = 600). Using this method, personality traits predicted 54–62% of variance in loneliness, compared to 26–33% when using methods limited to just self-report or informant-report data. Across samples, we found strong independent relationships between loneliness and Neuroticism (βtrues around 0.7) when controlling for other personality traits. Loneliness had robust, though comparatively weaker, associations with Extraversion (βtrues around 0.25) and Agreeableness (βtrues from 0.1 to 0.3). Women and older people tended to be lonelier, but these associations were entirely attributable to personality trait differences: once these were controlled for, the associations either reversed or became non-significant. Overall, we find that loneliness is far more closely related to Neuroticism than previously understood, and distinct from both social isolation and social disconnection.

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“…Lastly, consistent with previous research, we find a single-item, direct measure to be valid and useful for measuring loneliness (Thompson & Pollet, 2023).…”

Section: Loneliness and Social Isolationsupporting

confidence: 90%

“…This item assesses loneliness in the most face-valid way possible, and closely matches the UK National Health Service's definition of loneliness (Veazie et al, 2019). Direct single-item measures of loneliness have been supported by the literature (Thompson & Pollet, 2023).…”

Section: Methodsmentioning

confidence: 64%

Extraverted, but Lonely: Noise- and Bias-free ‘True’ Correlations Between Loneliness and the Big Five Personality Domains

Maher,

Dragostinov,

Vainik

et al. 2024

Preprint

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“…For instance, if limited healthcare access is identified as an overlapping node between the ‘socioeconomic disadvantages’ community and the ‘mental health epidemic’ community, limited healthcare access could be a gateway to co‐occurring mental health issues or factors that can mutually exacerbate both communities—a point for intervention. Clique percolation has been recently used in psychometric network analysis, where the overlapping node is interpreted as the item that shows high ‘cross‐loading’ on non‐targeted dimensions of the scale (Thompson & Pollet, 2021). However, to the best of the author's knowledge, clique percolation has not been applied in health disparity research.…”

Section: Introductionmentioning

confidence: 99%

“…Clique percolation has been recently used in psychometric network analysis, where the overlapping node is interpreted as the item that shows high 'cross-loading' on non-targeted dimensions of the scale (Thompson & Pollet, 2021). However, to the best of the author's knowledge, clique percolation has not been applied in health disparity research.…”

Section: Introductionmentioning

confidence: 99%

The application of clique percolation method in health disparities research

Lee

Cao

González-Guarda

2023

Journal of Advanced Nursing

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AimsClique percolation, one of the joint community detection algorithms in network science, is a novel and efficient approach to detecting overlapping communities in real networks. The current study illustrated how clique percolation can help to identify overlapping communities within the complex networks underlying health disparities, particularly highlighting nodes with strong associations with more than one community.DesignA cross‐sectional study.MethodsThe study used a dataset on Latinx populations (N = 1654; mean age = 43.3 years; 53.1% women) as an example to demonstrate the role of such overlapping nodes in the network of syndemic conditions and their common risk factors. Syndemic conditions in the network included HIV risk, substance abuse (smoking, heavy alcohol consumption and marijuana use) and poor mental health. Moreover, the risk factors encompassed individual (education and income) and sociostructural (adverse childhood experiences [ACEs] and access to services) factors. The network was estimated using the R‐package bootnet. Clique percolation was conducted on the estimated network using the R‐package CliquePercolation.ResultsA total of three communities were detected, with HIV risk and poor mental health not being assigned to any community. In general, Community 1 was comprised of ACE categories, Community 2 included education, income and access to services and Community 3 included other syndemic conditions. Of note, two nodes were assigned to two communities: ‘household dysfunction’ to Communities 1 and 2 and ‘smoking’ to Communities 2 and 3.ConclusionHousehold dysfunction might be the key connector, among other ACEs, to individual and structural barriers. Such barriers further exposed Latinx individuals to risky behaviours, especially smoking, which further linked to marijuana use and heavy alcohol consumption.ImpactClique percolation facilitated our understanding of the complex systems of factors shaping health disparities. The overlapping nodes are promising intervention targets for reducing health disparities in this historically marginalized population.Patient or Public ContributionNo patient or public contribution.

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An Exploratory Psychometric Network Analysis of Loneliness Scales in a Sample of Older Adults.

Cited by 2 publications

References 49 publications

Extraverted, but Lonely: Noise- and Bias-free ‘True’ Correlations Between Loneliness and the Big Five Personality Domains

Extraverted, but Lonely: Noise- and Bias-free ‘True’ Correlations Between Loneliness and the Big Five Personality Domains

The application of clique percolation method in health disparities research

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