SavedAdd your journal

Journals / Addicta: The Turkish Journal on Addictions / 2021 / Cilt: 8 - Sayı: 1

Use of Network Psychometrics Approach to Examine Social Media Disorder Symptoms

Akif AVCU
Pages
87–91
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, withdrawal and preoccupation were detected as the most importantsymptoms, whereas deception was less important.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The symptoms of behavioral addiction are generally regarded as a consequence of a latent construct.
📊
Key Findings & Data:This study, we aimed to investigate symptoms of social media disorder within this framework.This is the first study performed using this novel in the field of behavioral addiction, and conceptualizing social media disorder in this manner helps the professionals in gaining new insights on the construct.The data were collected by applying the Short Social Media Disorder Scale to 727 university students andwere analyzed with qgraph and EstimateGroupNetwork packages in R program.
💡
Conclusion & Impact:According to the results, withdrawal and preoccupation were detected as the most importantsymptoms, whereas deception was less important.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

The symptoms of behavioral addiction are generally regarded as a consequence of a latent construct. However, network psychometrics enable conceptualizing them as directly interacting with variables in a networkof symptoms. This study, we aimed to investigate symptoms of social media disorder within this framework.This is the first study performed using this novel in the field of behavioral addiction, and conceptualizing social media disorder in this manner helps the professionals in gaining new insights on the construct.The data were collected by applying the Short Social Media Disorder Scale to 727 university students andwere analyzed with qgraph and EstimateGroupNetwork packages in R program. Strength, closeness, andbetweenness centrality indices were used to evaluate the most important symptoms in the network. Thecentrality of the network model was further investigated with Zhang’s clustering coefficient and the smallworld Index was calculated. Finally, the estimated network structures were compared based on gender andage variables. According to the results, withdrawal and preoccupation were detected as the most importantsymptoms, whereas deception was less important. In addition, it was found that the estimated network hada small-world property. These findings were discussed in terms of their theoretical and practical significance.

🔖

Cite This Article

AVCU, A. (2021). Use of Network Psychometrics Approach to Examine Social Media Disorder Symptoms. Addicta: The Turkish Journal on Addictions, Cilt: 8 - Sayı: 1, 87–91. https://ajindex.com/articles/357453

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Avcu, A, Ulaş, E., & Sevim, E. (2019). Sosyal Medya Bozukluğu ile İlgili Yaşam Doyumu ve Psikolojik Dayanıklılıkta Arabulucu Faktörler Olarak Kişilik Özelliklerinin Yol Analiz Modeli. Online Journal of Technology Addiction and Cyberbullying, 6(2), 17-35.
  2. Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16, 5-13.
  3. Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95, 759-771.
  4. Costantini, G., & Perugini, M. (2014). Generalization of clustering coefficients to signed correlation networks. PloS One, 9(2), e88669. Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mottus, R., Waldorp, L. J., & Cramer, A. O. J. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13-29.
  5. Costantini, G., Richetin, R., Preti, E., Casini, E., Epskamp, S., & Perugini, M. (2019). Stability and variability of personality networks. A tutorial on recent developments in network psychometrics. Personality and Individual Differences, 136, 68-78.
  6. Danaher, P., Wang, P., & Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 373-397. Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. (2013). In Diagnostic and Statistical Manual of Mental Disorders, 5th Edition.
  7. Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195-212.
  8. Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). Qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1-18.
  9. Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A. M., Riese, H., & Cramer, A. O. J. (2018). Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections. Clinical Psychological Science, 6(3), 416-427.
  10. Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. Advances in Neural Information Processing Systems, 23, 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010.
  11. Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. Frie