Abstract

Background

To the best of our knowledge, the Cyberchondria Severity Scale-12 (CSS-12) has not been translated into Arabic; therefore, our objective was to assess the psychometric properties of the Arabic version of the CSS (CSS-12-Ar) among a sample of Lebanese adults.

Methods

Participants were enrolled in January 2021. A confirmatory factor analysis (CFA) was carried out using the MPlus software v.7.2, reporting several goodness-of-fit indicators: Relative Chi-square (χ2/df), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and Tucker Lewis Index (TLI). To evaluate measurement invariance across gender, we conducted higher-order multiple group confirmatory analysis using lavaan software.

Results

449 participants enrolled in this study (mean age: 24.34 ± 8.22 years, 70.6% females). Since the correlations between the four-factor model were very high (r > 0.8), we ran the higher-order CFA in which all first-order latent variables were loading a general factor. The analyzed model was well-fitted to the data χ2(50) = 173.34; p < 0.001; CFI = 0.926; RMSEA = 0.074 [0.062, 0.086]. The Cronbach’s alpha values were good for the total score (0.92), as well as for excessiveness (0.80), distress (0.77), reassurance (0.81) and compulsion (0.76). The results provided evidence of full scalar invariance across gender. The comparison of latent mean scores revealed no significant differences across gender, in either the cyberchondria total score or its facets. The CSS-12 score was positively associated with anxiety (r = 0.10; p = 0.003) (convergent validity), OCD (r = 0.11; p = 0.016) and stress (r = 0.35; p < 0.001) (concurrent validity).

Conclusion

The CSS-12-Ar was deemed a suitable scale to measure the severity of cyberchondria among Lebanese university students. We hope that researchers and clinicians can benefit now from this scale.

Details

Title
Validation of the Arabic version of the cyberchondria severity scale 12 items (CSS-12-Ar) among a sample of Lebanese adults
Author
Hallit, Souheil; Rogoza, Radosław; Carl Abi Semaan; Azzi, Vanessa; Sawma, Toni; Obeid, Sahar
Pages
1-8
Section
Research
Publication year
2023
Publication date
2023
Publisher
BioMed Central
e-ISSN
1471244X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2865397499
Copyright
© 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.