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Initial psychometric testing of the sleep condition indicator in a Swedish context
Linnaeus Univ, Dept Hlth & Caring Sci, Kalmar, Sweden.
Kristianstad Univ, Sch Hlth & Soc, PRO CARE Grp, Kristianstad, Sweden.
Jönköping University, School of Health and Welfare, HHJ, Dep. of Nursing Science. Jönköping University, School of Health and Welfare, HHJ. ADULT. Linkoping Univ, Dept Clin & Expt Med, Linkoping, Sweden.ORCID iD: 0000-0003-1884-5696
Linkoping Univ, Dept Clin & Expt Med, Linkoping, Sweden.
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2017 (English)In: Sleep Medicine, ISSN 1389-9457, E-ISSN 1878-5506, Vol. 40, no Suppl. 1, p. E129-E130Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

Introduction: There are several rating scales for insomnia; however, diagnostic criteria have changed over time. This means that the usefulness and validity of earlier scales may be compromised. The Sleep Condition Indicator (SCI) is a recently designed scale, developed in the UK, based on the DSM-V criteria of insomnia. The aim of this study was to translate and psychometrically evaluate the SCI in a Swedish context, focusing on its dimensionality.

Materials and methods: The SCI consists of eight items with 5 ordered response categories (scored between 0 and 4). A total score between 0 and 32 is calculated; higher score indicates better sleep. The SCI was distributed through a web-questionnaire to university students and 634 completed the questionnaire . First we replicated the methodology used in the original testing of the UK SCI using principal component analysis (PCA) as the extraction method with varimax rotation and Kaisers eigenvalue >1 criterion for determination of the number of factors. We then continued with a more appropriate method for ordinal data, an exploratory factor analysis (EFA), using an unweighted least squares (ULS) extraction method based on a polychoric correlation matrix. Parallel analysis was conducted to determine the number of factors. Internal consistency was estimated using an ordinal version of Cronbach's alpha.

Results: The PCA suggested a one factor model, with eigenvalues of 5.0 for the 1st and 0.9 for the 2nd factor, explaining 62% of the variance. Loadings varied between 0.62-0.86. With the EFA (ULS), 70% of the variance was explained by the first factor. Factor loadings varied between 0.66 and 0.92. Eigenvalues for factors 1 and 2 were 5.6 and 0.8, respectively. Corresponding 95th percentile eigenvalues from the parallel analysis were 5.3 (1st factor) and 0.4 (2nd factor). Reliability (ordinal alpha) of the total SCI score was 0.94.

Conclusions: Both models support a unidimensional SCI structure in our sample of university students. This is a prerequisite for the calculation and validity of a total SCI score. In addition, the total score exhibited good reliability. These observations support the psychometric integrity of the Swedish SCI and provide a starting point for further testing.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 40, no Suppl. 1, p. E129-E130
National Category
Neurology Psychology (excluding Applied Psychology)
Identifiers
URN: urn:nbn:se:hj:diva-41908DOI: 10.1016/j.sleep.2017.11.379ISI: 000444558902379OAI: oai:DiVA.org:hj-41908DiVA, id: diva2:1258926
Available from: 2018-10-26 Created: 2018-10-26 Last updated: 2018-10-26Bibliographically approved

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