Psychometric validation of the Persian bergen social media addiction scale using classic test theory and Rasch models

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

Research output: Contribution to journalArticlepeer-review

159 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)620-629
Number of pages10
JournalJournal of Behavioral Addictions
Volume6
Issue number4
DOIs
Publication statusPublished - 2017 Dec

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Clinical Psychology
  • Psychiatry and Mental health

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