Psychometric properties of the German version of the Self-Image Scale (SIS-D).

Brederecke J, Scott JL, de Zwaan M, et al. Psychometric properties of the German version of the Self-Image Scale (SIS-D). PloS one. 2020;15(3): e0230331.BACKGROUND: The Self-Image Scale is a self-report measure originally developed for use in women with cancer. Two subscales assess appearance satisfaction (self-acceptance) and perceptions of partners' acceptance of their appearance (partner-acceptance). This study aimed to increase the Self-Image Scale's utility by 1) confirming the two-factor structure of the German version of the Self-Image Scale, 2) testing measurement invariance across sex and age groups and validity, and 3) gathering general population normative data.; METHODS: Confirmatory factor analysis methods were used to examine the proposed two-factor model in a random sample of adults from the general German population (N = 1367). Measurement invariance, scale reliability, and validity were assessed.; RESULTS: The original factor structure and measurement invariance across sexes and age groups were supported. Women showed significantly lower self-acceptance than men. Adolescent and young adult women showed higher self-acceptance than senior women. For both sexes, partner-acceptance lowered across successive age cohorts. Internal consistencies were good.; CONCLUSIONS: Results support the use of the German version of the Self-Image Scale in research and clinical practice. Research directions include validation in further diseases, collecting normative data across countries, and dyadic research, particularly exploring partner-acceptance across the life span

Structural validation of the Self-Compassion Scale with a German general population sample (Data Sharing)

Structural Validation of the SCS scal

Loneliness Before and During the COVID-19 Pandemic: A Systematic Review with Meta-Analysis

The COVID-19 pandemic and measures aimed at its mitigation, such as physical distancing, have been discussed as risk factors for loneliness, which increases the risk of premature mortality and mental and physical health conditions. To ascertain whether loneliness has increased since the start of the pandemic, this study aimed to narratively and statistically synthesize relevant high-quality primary studies. This systematic review with meta-analysis was registered at PROSPERO (ID CRD42021246771). Searched databases were PubMed, PsycINFO, Cochrane Library/Central Register of Controlled Trials/EMBASE/CINAHL, Web of Science, the WHO COVID-19 Database, supplemented by Google Scholar and citation searching (cutoff date of the systematic search 05/12/2021). Summary data from prospective research including loneliness assessments before and during the pandemic were extracted. Of 6,850 retrieved records, 34 studies (23 longitudinal, 9 pseudo-longitudinal, 2 reporting both designs) on 215,026 participants were included. Risk of bias (RoB) was estimated using the ROBINS-I tool. Standardized mean differences (SMD, Hedges’ g) for continuous loneliness values and logOR for loneliness prevalence rates were calculated as pooled effect size estimators in random-effects meta-analyses. Pooling studies with longitudinal designs only (overall N = 45,734), loneliness scores (19 studies, SMD = 0.27 [95% confidence interval = 0.14-0.40], Z = 4.02, p < .001, I2 = 98%) and prevalence rates (8 studies, logOR = 0.33 [0.04-0.62], Z = 2.25, p = .02, I2 = 96%) increased relative to pre-pandemic times with small effect sizes. Results were robust with respect to studies’ overall RoB, pseudo-longitudinal designs, timing of pre-pandemic assessments, and clinical populations. The heterogeneity of effects indicates a need to further investigate risk and protective factors as the pandemic progresses to inform targeted interventions

Structural validation of the Self-Compassion Scale with a German general population sample

<div><p>Background</p><p>Published validation studies have reported different factor structures for the Self-Compassion Scale (SCS). The objective of this study was to assess the factor structure of the SCS in a large general population sample representative of the German population.</p><p>Methods</p><p>A German population sample completed the SCS and other self-report measures. Confirmatory factor analysis (CFA) in MPlus was used to test six models previously found in factor analytic studies (unifactorial model, two-factor model, three-factor model, six-factor model, a hierarchical (second order) model with six first-order factors and two second-order factors, and a model with arbitrarily assigned items to six factors). In addition, three bifactor models were also tested: bifactor model #1 with two group factors (SCS positive items, called SCS positive) and SCS negative items, called SCS negative) and one general factor (overall SCS); bifactor model #2, which is a two-tier model with six group factors, three (SCS positive subscales) corresponding to one general dimension (SCS positive) and three (SCS negative subscales) corresponding to the second general dimension (SCS negative); bifactor model #3 with six group factors (six SCS subscales) and one general factor (overall SCS).</p><p>Results</p><p>The two-factor model, the six-factor model, and the hierarchical model showed less than ideal, but acceptable fit. The model fit indices for these models were comparable, with no apparent advantage of the six-factor model over the two-factor model. The one-factor model, the three-factor model, and bifactor model #3 showed poor fit. The other two bifactor models showed strong support for two factors: SCS positive and SCS negative.</p><p>Conclusion</p><p>The main results of this study are that, among the German general population, six SCS factors and two SCS factors fit the data reasonably well. While six factors can be modelled, the three negative factors and the three positive factors, respectively, did not reflect reliable or meaningful variance beyond the two summative positive and negative item factors. As such, we recommend the use of two subscale scores to capture a positive factor and a negative factor when administering the German SCS to general population samples and we strongly advise against the use of a total score across all SCS items.</p></div

Socio-demographic characteristics of the study sample (n = 2448).

<p>Socio-demographic characteristics of the study sample (n = 2448).</p

Summary of models tested via confirmatory factor analysis (CFA) with the items of the Self-Compassion Scale.

<p>Summary of models tested via confirmatory factor analysis (CFA) with the items of the Self-Compassion Scale.</p

Correlations between the study measures and the six SCS factors and the two SCS factors.

<p>Correlations between the study measures and the six SCS factors and the two SCS factors.</p

Factor loadings and Confidence Intervals (CI) for the two-factor structure of the Self-Compassion Scale (SCS).

<p>Factor loadings and Confidence Intervals (CI) for the two-factor structure of the Self-Compassion Scale (SCS).</p