Modeling the Structural Relationships of Internet Addiction Based on Executive Functions, Interpersonal Problems, and Alexithymia in Seventh-Grade Female Students

Mohebi, Masoume; Asadzade , Hassan; Farrokhi, Noorali

Volume 13 - Articles-1402 MEJDS (2023) 13: 94 | Back to browse issues page

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Mohebi M, Asadzade H, Farrokhi N. Modeling the Structural Relationships of Internet Addiction Based on Executive Functions, Interpersonal Problems, and Alexithymia in Seventh-Grade Female Students. MEJDS 2023; 13 :94-94
URL: http://jdisabilstud.org/article-1-2157-en.html

Modeling the Structural Relationships of Internet Addiction Based on Executive Functions, Interpersonal Problems, and Alexithymia in Seventh-Grade Female Students

Masoume Mohebi¹

, Hassan Asadzade ^*²

, Noorali Farrokhi³

1- PhD Student in Educational Psychology, Faculty of Educational Sciences and Psychology, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Associate Professor, Department of Educational Psychology, Faculty of Psychology and Educational Sciences, Allameh Tabataba'i University, Tehran, Iran
3- Associate Professor, Department of Measurement, Faculty of Psychology and Educational Sciences, Allameh Tabataba'i University, Tehran, Iran

Abstract: (1539 Views)

Abstract
Background & Objectives: Today, the use of the Internet has brought many benefits to society, but this development has caused problems and disabilities in many dimensions for individuals, a problem known as internet addiction disorder. Many intrapersonal factors affect Internet addiction among adolescents. Various factors impact the tendency to become addicted to the Internet, among which interpersonal problems can be mentioned. Also, alexithymia plays a significant role in teenagers' tendency to use the Internet. Executive functions are cognitive and metacognitive processes that help teenagers pay attention to and finish the task's main points. Internet addiction is a disorder that causes disability and functional defects and creates psychological, social, academic, and work disorders. This study aims to model the structural relationships of internet addiction based on executive functions, interpersonal problems, and alexithymia in seventh–grade female students.
Methods: This descriptive–correlational study was based on regression structural equation modeling. The statistical population comprised all female students in the seventh–grade schools of districts 1, 7, and 8 of Tehran City, Iran, in the academic year of 2018–2019. We used Loehlin (2004) volume estimation method to determine the sample size. Basically, we considered 30 to 50 samples for each latent variable. So, considering 4 latent variables and the possibility of incomplete questionnaires, 260 students were selected using the cluster random sampling method. In this study, the inclusion criteria comprised female students in the seventh grade who were living and studying in districts 1, 7, and 8 of Tehran, were interested in cooperating in research, and lacked any mental illnesses. The exclusion criteria comprised a lack of cooperation in research and becoming sick. Data were gathered via the Cognitive Abilities Questionnaire (Nejati, 2013), the Inventory of Interpersonal Problems (Barkham et al., 1996), the Toronto Alexithymia Scale (Bagby et al., 1994), and the Internet Addiction Test (Young, 1996). Data analysis was also performed using structural equation modeling in SPSS 24 and AMOS 24 software.
Results: The findings showed that the effects of total interpersonal problems (p<0.001, β=0.646) and executive functions (p<0.001, β=–1.257) on the Internet addiction variable were significant. Also, the direct effect of interpersonal problems (p<0.001, β=0.401), executive functions (p<0.001, β=–0.297), and emotional malaise (p<0.001, β=0.629) on the variable Internet addiction was significant. On the other hand, the indirect effects of interpersonal problems (p<0.001, β=0.245) and executive functions (p<0.001, β=–0.960) on the Internet addiction variable were estimated using the Bootstrap estimation method. The presented model had a good fit (AGFI=0.987, RMSEA=0.042, GFI=0.993, CFI=0.957, χ2/df=2.924).
Conclusion: According to the findings, the present research model has statistically acceptable fit indices. Executive functions and interpersonal problems are the most important factors in predicting Internet addiction. The results also showed how the emotional numbness variable can act as the mediating variable.

Keywords: Structural relationship modeling, Internet addiction, Executive functions, Interpersonal problems, Alexithymia

Full-Text [PDF 642 kb] (388 Downloads)

Type of Study: Original Research Article | Subject: Psychology

References

1. Di Z, Gong X, Shi J, Ahmed HOA, Nandi AK. Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine. Addict Behav Rep. 2019;10:100200. [DOI]

2. Sariyska R, Lachmann B, Markett S, Reuter M, Montag C. Individual differences in implicit learning abilities and impulsive behavior in the context of Internet addiction and Internet Gaming Disorder under the consideration of gender. Addict Behav Rep. 2017;5:19–28. [DOI]

3. Chen Q, Quan X, Lu H, Fei P, Li M. Comparison of the personality and othr psychological factors of students with Internet addiction who do and do not have associated social dysfunction. Shanghai Arch Psychiatry. 2015;27(1):36–41. [DOI]

4. Wu JYW, Ko HC, Wong TY, Wu LA, Oei TP. Positive outcome expectancy mediates the relationship between peer influence and Internet gaming addiction among adolescents in taiwan. Cyberpsychology, Behavior, and Social Networking. 2016;19(1):49–55. [DOI]

5. Tang JH, Chen MC, Yang CY, Chung TY, Lee YA. Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telemat Inform. 2016;33(1):102–8. [DOI]

6. Carbal L, Duarte J, Oliveira A, Neves L, Fonseca M, Marques S, et al. The impact of Internet addiction on the mental health of college students from the central area of Portugal. Primary Care. 2016;48(1):253–8.

7. Schimmenti A, Passanisi A, Caretti V, La Marca L, Granieri A, Iacolino C, et al. Traumatic experiences, alexithymia, and Internet addiction symptoms among late adolescents: a moderated mediation analysis. Addict Behav. 2017;64:314–20. [DOI]

8. Tang W, Xu D, Xu J. The mediating role of alexithymia between earthquake exposure and psychopathology among adolescents 8.5 years after the wenchuan earthquake. Pers Individ Dif. 2020;159:109881. [DOI]

9. Di Tella M, Adenzato M, Catmur C, Miti F, Castelli L, Ardito RB. The role of alexithymia in social cognition: Evidence from a non-clinical population. J Affect Disord. 2020;273:482–92. [DOI]

10. Li X, Li B, Lu J, Jin L, Xue J, Che X. The relationship between alexithymia, hostile attribution bias, and aggression. Pers Individ Dif. 2020;159:109869. [DOI]

11. Spensieri V, Valastro C, Cerutti R. PO-31: technology addiction and alexithymia features in a sample of Italian adolescents. Journal of Behavioral Addictions. 2016;5(S1):58–9.

12. Gao T, Li J, Zhang H, Gao J, Kong Y, Hu Y, et al. The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress. J Affect Disord. 2018;225:761–6. [DOI]

13. Bolat N, Yavuz M, Eliaçık K, Zorlu A. The relationships between problematic Internet use, alexithymia levels and attachment characteristics in a sample of adolescents in a high school, Turkey. Psychology, Health & Medicine. 2018;23(5):604–11. [DOI]

14. Luu K, Hall PA. Examining the acute effects of hatha yoga and mindfulness meditation on executive function and mood. Mindfulness. 2017;8(4):873–80. [DOI]

15. Leyland A, Emerson LM, Rowse G. Testing for an effect of a mindfulness induction on child executive functions. Mindfulness. 2018;9(6):1807–15.‌ [DOI]

16. Noone C, Bunting B, Hogan MJ. Does mindfulness enhance critical thinking? evidence for the mediating effects of executive functioning in the relationship between mindfulness and critical thinking. Front Psychol. 2016;6. [DOI]

17. Loehlin JC. Latent variable models. 4th edition. London, NJ: Lawrence Erlbaum Associates Publishers; 2004.

18. Nejati V. Cognitive Abilities Questionnaire: development and evaluation of psychometric properties. Advances in Cognitive Sciences. 2013;15(2):11–19. [Persian] [Article]

19. Barkham M, Hardy GE, Startup M. The IIP-32: a short version of the Inventory of Interpersonal Problems. Br J Health Psychol. 1996;35(1):21–35. [DOI]

20. Fath N, Azad Fallah P, Rasoul Zadeh Tabatabaei SK, Rahimi C. Validity and reliability of the Inventory of Interpersonal Problems (IIP-32). Journal of Clinical Psychology. 2013;5(3):69–80. [Persian] [Article]

21. Bagby RM, Parker JDA, Taylor GJ. The twenty-item Toronto Alexithymia Scale—I. item selection and cross-validation of the factor structure. J Psychosom Res. 1994;38(1):23–32. [DOI]

22. Mahdavi E, Manshaee G. Comparing alexithymia and emotional expressiveness in patients with coronary heart disease and healthy people. Feyz. 2016;20(3):260–6. [Persian] [Article]

23. Young K. Psycholgy of Computer use: addictive use of the Internet: a case that breaks the stereotype. Psychol Rep. 1996;79(3):899–902. [DOI]

24. Alavi SS, Jannatifard F, Eslami M, Rezapour H. Evaluation of diagnostic criteria of DSM-IV-TR for diagnosis of Internet addiction disorder. Zahedan J Res Med Sci. 2011;13(6):e93833. [Persian] [Article]

25. Becker JM, Klein K, Wetzels M. Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Planning. 2012;45(5–6):359–94. [DOI]