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      Construction and Validation of a Scale to Measure Loneliness and Isolation During Social Distancing and Its Effect on Mental Health

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          Abstract

          A variety of factors contribute to the degree to which a person feels lonely and socially isolated. These factors may be particularly relevant in contexts requiring social distancing, e.g., during the COVID-19 pandemic or in states of immunodeficiency. We present the Loneliness and Isolation during Social Distancing (LISD) Scale. Extending existing measures, the LISD scale measures both state and trait aspects of loneliness and isolation, including indicators of social connectedness and support. In addition, it reliably predicts individual differences in anxiety and depression. Data were collected online from two independent samples in a social distancing context (the COVID-19 pandemic). Factorial validation was based on exploratory factor analysis (EFA; Sample 1, N = 244) and confirmatory factor analysis (CFA; Sample 2, N = 304). Multiple regression analyses were used to assess how the LISD scale predicts state anxiety and depression. The LISD scale showed satisfactory fit in both samples. Its two state factors indicate being lonely and isolated as well as connected and supported, while its three trait factors reflect general loneliness and isolation, sociability and sense of belonging, and social closeness and support. Our results imply strong predictive power of the LISD scale for state anxiety and depression, explaining 33 and 51% of variance, respectively. Anxiety and depression scores were particularly predicted by low dispositional sociability and sense of belonging and by currently being more lonely and isolated. In turn, being lonely and isolated was related to being less connected and supported (state) as well as having lower social closeness and support in general (trait). We provide a novel scale which distinguishes between acute and general dimensions of loneliness and social isolation while also predicting mental health. The LISD scale could be a valuable and economic addition to the assessment of mental health factors impacted by social distancing.

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            lavaan: AnRPackage for Structural Equation Modeling

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              Comparative fit indexes in structural models.

              Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
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                Author and article information

                Contributors
                Journal
                Front Psychiatry
                Front Psychiatry
                Front. Psychiatry
                Frontiers in Psychiatry
                Frontiers Media S.A.
                1664-0640
                05 April 2022
                2022
                : 13
                : 798596
                Affiliations
                [1] 1Translational Social Neuroscience Unit, Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University of Würzburg , Würzburg, Germany
                [2] 2Institute of Psychology, Department of Psychology I: Differential Psychology, Personality Psychology and Psychological Diagnostics, University of Würzburg , Würzburg, Germany
                Author notes

                Edited by: Giorgia Silani, University of Vienna, Austria

                Reviewed by: Jakob Pietschnig, University of Vienna, Austria; Philipp Kanske, Technical University Dresden, Germany

                *Correspondence: Marthe Gründahl Gruendah_M@ 123456ukw.de

                This article was submitted to Social Cognition, a section of the journal Frontiers in Psychiatry

                Article
                10.3389/fpsyt.2022.798596
                9017747
                35449561
                eb3dd0ed-2ab9-48e5-9cc5-166cc1e9e67d
                Copyright © 2022 Gründahl, Weiß, Maier, Hewig, Deckert and Hein.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 October 2021
                : 21 February 2022
                Page count
                Figures: 1, Tables: 4, Equations: 0, References: 135, Pages: 16, Words: 13931
                Categories
                Psychiatry
                Original Research

                Clinical Psychology & Psychiatry
                loneliness,social isolation,social distancing,depression,anxiety
                Clinical Psychology & Psychiatry
                loneliness, social isolation, social distancing, depression, anxiety

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