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Psychometric validation of the Persian Bergen Social Media Addiction Scale using classic test theory and Rasch models

Overview of attention for article published in Journal of Behavioral Addictions, November 2017
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Title
Psychometric validation of the Persian Bergen Social Media Addiction Scale using classic test theory and Rasch models
Published in
Journal of Behavioral Addictions, November 2017
DOI 10.1556/2006.6.2017.071
Pubmed ID
Authors

Chung-Ying Lin, Anders Broström, Per Nilsen, Mark D. Griffiths, Amir H. Pakpour

Abstract

Background and aims The Bergen Social Media Addiction Scale (BSMAS), a six-item self-report scale that is a brief and effective psychometric instrument for assessing at-risk social media addiction on the Internet. However, its psychometric properties in Persian have never been examined and no studies have applied Rasch analysis for the psychometric testing. This study aimed to verify the construct validity of the Persian BSMAS using confirmatory factor analysis (CFA) and Rasch models among 2,676 Iranian adolescents. Methods In addition to construct validity, measurement invariance in CFA and differential item functioning (DIF) in Rasch analysis across gender were tested for in the Persian BSMAS. Results Both CFA [comparative fit index (CFI) = 0.993; Tucker-Lewis index (TLI) = 0.989; root mean square error of approximation (RMSEA) = 0.057; standardized root mean square residual (SRMR) = 0.039] and Rasch (infit MnSq = 0.88-1.28; outfit MnSq = 0.86-1.22) confirmed the unidimensionality of the BSMAS. Moreover, measurement invariance was supported in multigroup CFA including metric invariance (ΔCFI = -0.001; ΔSRMR = 0.003; ΔRMSEA = -0.005) and scalar invariance (ΔCFI = -0.002; ΔSRMR = 0.005; ΔRMSEA = 0.001) across gender. No item displayed DIF (DIF contrast = -0.48 to 0.24) in Rasch across gender. Conclusions Given the Persian BSMAS was unidimensional, it is concluded that the instrument can be used to assess how an adolescent is addicted to social media on the Internet. Moreover, users of the instrument may comfortably compare the sum scores of the BSMAS across gender.

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Geographical breakdown

Country Count As %
Unknown 314 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 46 15%
Student > Ph. D. Student 31 10%
Student > Master 27 9%
Researcher 21 7%
Lecturer 17 5%
Other 48 15%
Unknown 124 39%
Readers by discipline Count As %
Psychology 79 25%
Medicine and Dentistry 22 7%
Nursing and Health Professions 21 7%
Social Sciences 17 5%
Computer Science 8 3%
Other 33 11%
Unknown 134 43%