• This record comes from PubMed

Validation of the Social Media Disorder Scale using network analysis in a large representative sample of Czech adolescents

. 2022 ; 10 () : 907522. [epub] 20220822

Language English Country Switzerland Media electronic-ecollection

Document type Journal Article, Research Support, Non-U.S. Gov't

BACKGROUND: The importance of studying the excessive use of social media in adolescents is increasing and so is the need for in-depth evaluations of the psychometric properties of the measurement tools. This study investigated the properties of the Social Media Disorder Scale (SMDS) in a large representative sample of Czech adolescents. METHODS: We analyzed the representative sample of 13,377 Czech adolescents (50.9% boys), 11-16 years old, who participated in the Health Behavior in School-aged Children (HBSC) survey (2017-18), using confirmatory factor analysis (CFA) and network models. Furthermore, we evaluated the measurement invariance and constructed the validity of the SMDS. RESULTS: We found support for a single dominant factor but not for strict unidimensionality. Several residual correlations were identified. The strongest were for: problems-conflicts-deceptions; persistence-escape; and preoccupation-tolerance-withdrawal. Girls, particularly 13- and 15-year-olds, scored higher than boys in the same age group, and 13- and 15-year-olds achieved higher scores than 11-year-olds, although some items were not invariant between the groups. The SMDS was positively related to other online activities, screen time, and falling asleep late, but negatively related to well-being and mental health. DISCUSSION AND CONCLUSIONS: The SMDS showed solid psychometric properties and construct validity. However, small violations of measurement invariance were detected. Furthermore, the network analysis showed important residual relationships between the items.

See more in PubMed

Allen KA, Ryan T, Gray DL, McInerney DM, Waters L. Social media use and social connectedness in adolescents: the positives and the potential pitfalls Educ Dev Psychol. (2014) 31:18–31. 10.1017/edp.2014.2 DOI

Keles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth. (2020) 25:79–93. 10.1080/02673843.2019.1590851 DOI

Müller KW, Dreier M, Beutel ME, Duven E, Giralt S, Wölfling K. A hidden type of internet addiction? Intense and addictive use of social networking sites in adolescents. Comput Hum Behav. (2016) 55:172–7. 10.1016/j.chb.2015.09.007 DOI

Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health. (2011) 8:3528–52. 10.3390/ijerph8093528 PubMed DOI PMC

American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders (DSM-5). 5th ed. Washington, DC: American Psychiatric Association; (2013).

World Health Organization . International Statistical Classification of Diseases and Related Health Problems. 11th ed. Geneva: World Health Organization; (2019).

Valkenburg PM, Meier A, Beyens I. Social media use and its impact on adolescent mental health: an umbrella review of the evidence. Curr Opin Psychol. (2022) 44:58– 8. 10.1016/j.copsyc.2021.08.017 PubMed DOI

Hussain Z, Griffiths MD. The associations between problematic social networking site use and sleep quality, attention-deficit hyperactivity disorder, depression, anxiety and stress. Int J Ment Health Addiction. (2021) 19:686–700. 10.1007/s11469-019-00175-1 DOI

Marino C, Gini G, Vieno A, Spada MM. The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: a systematic review and meta-analysis. J Affect Disord. (2018) 226:274–81. 10.1016/j.jad.2017.10.007 PubMed DOI

Hussin Z, Griffiths M.D. Problematic social networking site use and comorbid psychiatric disorders: a systematic review of recent large-scale studies. Front Psychiatry. (2018) 9:686. 10.3389/fpsyt.2018.00686 PubMed DOI PMC

D'Arienzo MC, Boursier V, Griffiths MD. Addiction to social media and attachment styles: a systematic literature review. Int J Ment Health Addiction. (2019) 17:1094–118. 10.1007/s11469-019-00082-5 DOI

Alonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and mental health in youth: a systematic review. Sleep Med Rev. (2021) 56. 101414. 10.1016/j.smrv.2020.101414 PubMed DOI

Sun Y, Zhang Y. A review of theories and models applied in studies of social media addiction and implications for future research. Addict behav. (2021) 114:106699. 10.1016/j.addbeh.2020.106699 PubMed DOI

Brand M, Rumpf H-Jü, Demetrovics Z, MÜller A, Stark R, King DL, et al. . Which conditions should be considered as disorders in the International Classification of Diseases (ICD-11) designation of “other specified disorders due to addictive behaviors”? J Behav Addict. (2020) 11:150–9. 10.1556/2006.2020.00035 PubMed DOI PMC

van den Eijnden RJ, Lemmens JS, Valkenburg P.M. The social media disorder scale. Comput Hum Behav. (2016) 61:478–487. 10.1016/j.chb.2016.03.038 DOI

Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a facebook addiction scale. Psychol Rep. (2012) 110:501–17. 10.2466/02.09.18.PR0.110.2.501-517 PubMed DOI

Kircaburun K, Griffiths MD. Instagram addiction and the Big Five of personality: The mediating role of self-liking. J Behav Addict. (2018) 7:158–70. 10.1556/2006.7.2018.15 PubMed DOI PMC

Balakrishnan J, Griffiths MD. An exploratory study of “Selfitis” and the development of the selfitis behavior scale. Int J Ment Health Addiction. (2017) 16:722–36. 10.1007/s11469-017-9844-x PubMed DOI PMC

Lou J, Liu H, Liu X. Development of the problematic social networking services use scale with college students. Soc Behav Pers. (2017) 45:1889–904. 10.2224/sbp.6179 DOI

Esgi N. Development of social media addiction test (SMAT17). J Edu Train Stud. (2016) 4:174–81. 10.11114/jets.v4i10.1803 DOI

Andreassen C, Billieux J, Griffiths M, Kuss D, Demetrovics Z, Mazzoni E, et al. . The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. (2016) 30:252–62. 10.1037/adb0000160 PubMed DOI

Fung S. Cross-cultural validation of the Social Media Disorder scale. Psychol Res Behav Management. (2019) 12:683–90. 10.2147/PRBM.S216788 PubMed DOI PMC

Dewi SY, Lestari YM. Validity and reliability of indonesian social media disorder (SMD) scale in adolescent. J Profesi Medika. (2020) 14:2. 10.33533/jpm.v14i2.2049 DOI

Afe T, Ogunsemi O, Ayotunde A, Olufunke A, Osalusi B, Afe B, et al. . Psychometric properties and validation of the 9-item social media scale among pre-university students in Nigeria. East Asian Archi Psychiat. (2020) 30:108–12. 10.12809/eaap1946 PubMed DOI

Boer M, Stevens G, Finkenauer C, Ina HMK, van den ER. Validation of the social media disorder scale in adolescents: findings from a large-scale nationally representative sample. Assessment. (2021). 10.31219/osf.io/2fphx. [Epub ahead of print]. PubMed DOI PMC

Sariçam H, Adam Karduz FF. The adaptation of the Social Media Disorder scale to Turkish culture: validity and reliability study. J Meas Eval Educ Psychol. (2018) 9:116–35. 10.21031/epod.335607 DOI

Savci M, Ercengiz M, Aysan F. Turkish adaptation of the social media disorder scale in adolescents. Noro Psikiyatr Ars. (2018) 55:248–55. 10.29399/npa.19285 PubMed DOI PMC

Avcu A. Use of network psychometrics approach to examine social media disorder symptoms. ADDICTA Turkish J Addict. (2021) 8:87–91. 10.5152/ADDICTA.2021.20098 DOI

Boer M, van den Eijnden RJ, Finkenauer C, Boniel-Nissim M, Marino C, Inchley J, et al. . Cross-national validation of the Social Media Disorder-scale: Findings from adolescents from 44 countries. Addiction. (2022) 117:784–95. 10.1111/add.15709 PubMed DOI PMC

Epskamp S, Rhemtulla M, Borsboom D. Generalized network psychometrics: Combining network and latent variable models. Psychometrika. (2017) 82:904–27. 10.1007/s11336-017-9557-x PubMed DOI

Kuss D, Griffiths M. Social networking sites and addiction: ten lessons learned. Int J Environ Res Public Health. (2017) 14:311. 10.3390/ijerph14030311 PubMed DOI PMC

Karacic S, Oreskovic S. Internet addiction through the phase of adolescence: a questionnaire study. JMIR Ment Health. (2017) 4:e11. 10.2196/mental.5537 PubMed DOI PMC

Inchley J, Currie D, Cosma A, Samdal O. Health Behaviour in School-Aged Children (HBSC) Study Protocol: Background, Methodology and Mandatory Items for the 2017/18 Survey. St Andrews: Child and Adolescent Health Research Unit (CEHRU) University of St Andrews; (2018).

Gariepy G, McKinnon B, Sentenac M, Elgar FJ. Validity and reliability of a brief symptom checklist to measure psychological health in school-aged children. Child Indicat Res. (2015) 9:471–84. 10.1007/s12187-015-9326-2 DOI

World Health Organization . Well-being Measures in Primary Health Care/The Depcare Project. Copenhagen: WHO Regional Office for Europe; (1998).

Mascheroni G, Ólafsson K. Different Media for Different Contacts. Net Child Go Mob Risks Oppor. 2nd ed. Milano: Educatt; (2014).

Livingstone S, Haddon L, Görzig A, Ólafsson K. Risks and Safety on the Internet: The Perspective of European Children. Full Findings. London: EU Kids Online; (2011).

van Buuren S, Groothuis-Oudshoorn K. Multivariate imputation by chained equations in R. J Stat Soft. (2011) 45:1–67. 10.18637/jss.v045.i03 DOI

Rosseel Y. lavaan: An R package for structural equation modeling. J Stat Soft. (2012) 48:1–36. 10.18637/jss.v048.i02 PubMed DOI

Jorgensen TD, Pornprasertmanit S, Schoemann AM, Rosseel Y. semTools: Useful Tools for Structural Equation Modeling. (2020). Available online at; https://CRAN.R-project.org/package=semTools (accessed March 15, 2022).

Epskamp S. Psychonetrics: Structural Equation Modeling Confirmatory Network Analysis. (2021). Available online at: https://CRAN.R-project.org/package=psychonetrics.

Rutkowski L, Svetina D. Measurement invariance in international surveys: Categorical indicators & fit measure performance. Appl Meas Educ. (2017) 30:39–51. 10.1080/08957347.2016.1243540 DOI

Svetina D, Rutkowski L, Rutkowski D. Multiple-group invariance with categorical outcomes using updated guidelines: An Illustration using Mplus and the lavaan/semTools packages. Struct Equ Model A Multidiscipl J. (2020) 27:111–30. 10.1080/10705511.2019.1602776 DOI

Mansolf M, Jorgensen TD, Enders CK. A multiple imputation score test for model modification in structural equation models. Psychol Methods. (2020) 25:393–411. 10.1037/met0000243 PubMed DOI

Li C-H. Statistical estimation of structural equation models with a mixture of continuous and categorical observed variables. Behav Research Methods. (2021) 53:2191–213. 10.3758/s13428-021-01547-z PubMed DOI

Haslbeck JM, Epskamp S, Marsman M, Waldorp LJ. Interpreting the Ising model: the input matters. Multivar Behav Res. (2020) 56:303–13. 10.1080/00273171.2020.1730150 PubMed DOI

Deserno MK, Isvoranu AM, Epskamp S, Blanken T.F. Chapter 3. Descriptive analyses of network structures. In: Isvoranu AM, Epskamp S, Waldorp LJ, Borsboom D, editors. Network Psychometrics With R: A Guide for Behavioral and Social Scientists. Routledge: Taylor & Francis Group; (2022).

Watson JC, Prosek EA, Giordano AL. Investigating psychometric properties of social media addiction measures among adolescents. J Counsel Dev. (2020) 98:458–66. 10.1002/jcad.12347 PubMed DOI

Borsboom D. A network theory of mental disorders. World Psychiatry. (2017) 16:5–13. 10.1002/wps.20375 PubMed DOI PMC

Valkenburg PM, Peter J. The differential susceptibility to media effects model. J Commun. (2013) 63:221–43. 10.1111/jcom.12024 DOI

Petry NM, Zajac K, Ginley MK. Behavioral addictions as mental disorders: to be or not to be? Annu Rev Clin Psychol. (2018) 14:399–423. 10.1146/annurev-clinpsy-032816-045120 PubMed DOI PMC

Kardefelt-Winther D, Heeren A, Schimmenti A, van Rooij A, Maurage P, Carras M, et al. . How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction. (2017) 112:1709–15. 10.1111/add.13763 PubMed DOI PMC

King DL, Delfabbro PH. The cognitive psychology of Internet gaming disorder. Clinic Psychol Rev. (2014) 34:298–308. 10.1016/j.cpr.2014.03.006 PubMed DOI

Orben A. Teenagers, screens and social media: a narrative review of reviews and key studies. Soc Psychiatry Psychiatr Epidemiol. (2020) 55:407–14. 10.1007/s00127-019-01825-4 PubMed DOI

Kardefelt-Winther D. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Hum Behav. (2014) 31:351–4. 10.1016/j.chb.2013.10.059 DOI

Starcevic V, Billieux J. Does the construct of Internet addiction reflect a single entity or a spectrum of disorders? Clin Neuropsychiatry. (2017) 14:5–10.

Papini S, Rubin M, Telch MJ, Smits JA, Hien D. A Pretreatment posttraumatic stress disorder symptom network metrics predict the strength of the association between node change and network change during treatment. J Trauma Stress. (2020) 33:64–71. 10.1002/jts.22379 PubMed DOI

Jones PJ, Ma R, McNally RJ. Bridge centrality: a network approach to understanding comorbidity. Multivar Behav Res. (2021) 56:353–67. 10.1080/00273171.2019.1614898 PubMed DOI

Eschenbeck H, Kohlmann C-W, Lohaus A. Gender differences in coping strategies in children and adolescents. J Individ Differ. (2007) 28:18–26. 10.1027/1614-0001.28.1.18 DOI

Zimmer-Gembeck MJ, Skinner E. A Review: The development of coping across childhood and adolescence: an integrative review and critique of research. Int J Behav Dev. (2011) 35:1–17. 10.1177/0165025410384923 DOI

Chen FF. Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct Equ Model A Multidisciplinar J. (2007) 14:464–504. 10.1080/10705510701301834 DOI

Cheung GW, Rensvold RB. Evaluating goodness-of-git indexes for testing measurement invariance. Struct Equ Model A Multidisciplinar J. (2002) 9:233–55. 10.1207/S15328007SEM0902_5 PubMed DOI

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...