Validation of the Persian version of the Internet Addiction Questionnaire: Comparison of Multiple Indicator Multiple Causal Model (MIMIC) with Multiple Group Multiple Indicator Multiple Causal Model (MG-MIMIC) - Payesh (Health Monitor)
Mon, Dec 23, 2024
OPEN ACCESS
Volume 21, Issue 3 (May - June 2022)                   Payesh 2022, 21(3): 299-307 | Back to browse issues page

Ethics code: IR.MUMS.REC.1398.054


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Askarian F, Ghavami V, Shakeri M T, Jamali J. Validation of the Persian version of the Internet Addiction Questionnaire: Comparison of Multiple Indicator Multiple Causal Model (MIMIC) with Multiple Group Multiple Indicator Multiple Causal Model (MG-MIMIC). Payesh 2022; 21 (3) :299-307
URL: http://payeshjournal.ir/article-1-1887-en.html
1- Faculty of Health, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract:   (1559 Views)
Objective(s): It is believed that frequent use of Interne might be harmful for health. The Internet Addiction Test (IAT) is one of the most practical tools for diagnosing Internet addiction. The aim of the present study was to investigate validation of the Persian version of the questionnaire using Differential Item Functioning (DIF) analysis. DIF evaluates how people in different subgroups comprehend the same questionnaire item.
Methods: A cross section study on a sample of 317 students of Mashhad University of Medical was conducted. To collect data, the Internet Addiction Questionnaire and demographic information and Behavioral Features Checklist were administered. Multiple Indicator Multiple Causal Model (MIMIC) and Multiple Group Multiple Indicator Multiple Causal Model (MG-MIMIC) were used to investigate the differentiating of items. 
Results: The mean score of the IAT questionnaire was 40.92±11.70, which based on the cut-off point of 46, 29.5% of students were addicted to the Internet. The results of the MIMIC models indicated that there was no difference in each of the IAT questionnaire items. Also, considering the grouping structure did not have much effect on identifying DIF, because the results of the MG-MIMIC model were almost the same as the MIMIC model.
Conclusion: Due to the lack of significant DIF in IAT questions and having measurement invariance this study can be considered as empirical support for the IAT questionnaire as a valid and structurally stable tool.
Full-Text [PDF 626 kb]   (864 Downloads)    
type of study: Descriptive | Subject: Health Psychologhy
Received: 2022/04/30 | Accepted: 2022/05/5 | ePublished ahead of print: 2022/07/19 | Published: 2022/07/19

References
1. Lam LT. Internet gaming addiction, problematic use of the internet, and sleep problems: a systematic review. Current Psychiatry Reports 2014;16:444 [DOI:10.1007/s11920-014-0444-1]
2. Ho RC, Zhang MW, Tsang TY, Toh AH, Pan F, Lu Y, et al. The association between internet addiction and psychiatric co-morbidity: a meta-analysis. BMC Psychiatry 2014;14:183 [DOI:10.1186/1471-244X-14-183]
3. Lebni JY, Toghroli R, Abbas J, NeJhaddadgar N, Salahshoor MR, Mansourian M, et al. A study of internet addiction and its effects on mental health: A study based on Iranian University Students. Journal of Education and Health Promotion 2020;9:205 [DOI:10.4103/jehp.jehp_148_20]
4. Feizy F, Sadeghian E, Shamsaei F, Tapak L. The relationship between internet addiction and psychosomatic disorders in Iranian undergraduate nursing students: a cross-sectional study. Journal of Addictive Diseases 2020;38:164-9 [DOI:10.1080/10550887.2020.1732180]
5. Veisani Y, Jalilian Z, Mohamadian F. Relationship between internet addiction and mental health in adolescents. Journal of Education and Health Promotion 2020;9:303 [DOI:10.4103/jehp.jehp_362_20]
6. Yen JY, Ko CH, Yen CF, Wu HY, Yang MJ. The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. Journal of Adolescent Health 2007;41:93-8 [DOI:10.1016/j.jadohealth.2007.02.002]
7. Brenner V. Psychology of computer use: XLVII. Parameters of Internet use, abuse and addiction: the first 90 days of the Internet Usage Survey. Psychological Reports. 1997;80:879-82 [DOI:10.2466/pr0.1997.80.3.879]
8. Guan SS, Subrahmanyam K. Youth Internet use: risks and opportunities. Current Opinion in Psychiatry 2009;22:351-6 [DOI:10.1097/YCO.0b013e32832bd7e0]
9. Young KS. Caught in the net: How to recognize the signs of internet addiction--and a winning strategy for recovery: John Wiley & Sons, 1998
10. Tahir MJ, Malik NI, Ullah I, Khan HR, Perveen S, Ramalho R, et al. Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey. PloS One 2021;16:e0259594 [DOI:10.1371/journal.pone.0259594]
11. Karimy M, Parvizi F, Rouhani MR, Griffiths MD, Armoon B, Fattah Moghaddam L. The association between internet addiction, sleep quality, and health-related quality of life among Iranian medical students. Journal of Addictive Diseases 2020;38:317-25 [DOI:10.1080/10550887.2020.1762826]
12. Chen CY, Chen IH, Pakpour AH, Lin CY, Griffiths MD. Internet-Related Behaviors and Psychological Distress Among Schoolchildren During the COVID-19 School Hiatus. Cyberpsychology, Behavior and Social Networking 2021;24:654-63 [DOI:10.1089/cyber.2020.0497]
13. Drost EA, perspectives. Validity and reliability in social science research. Educational Research and Reviews 2011;38:105-23
14. Karami H. An introduction to differential item functioning. International Journal of Educational and Psychological Assessment 2012;11:59-76
15. Jafari P, Sharafi Z, Bagheri Z, Shalileh S. Measurement equivalence of the KINDL questionnaire across child self-reports and parent proxy-reports: a comparison between item response theory and ordinal logistic regression. Child Psychiatry & Human Development 2014;45:369-76 [DOI:10.1007/s10578-013-0407-5]
16. Jones RN. Identification of measurement differences between English and Spanish language versions of the Mini-Mental State Examination. Detecting differential item functioning using MIMIC modeling. Medical Care 2006;44:S124-33 [DOI:10.1097/01.mlr.0000245250.50114.0f]
17. Lai CM, Mak KK, Cheng C, Watanabe H, Nomachi S, Bahar N, et al. Measurement Invariance of the Internet Addiction Test Among Hong Kong, Japanese, and Malaysian Adolescents. Cyberpsychology, Behavior, and Social Networking 2015;18:609-17 [DOI:10.1089/cyber.2015.0069]
18. Lu X, Yeo KJ, Guo F, Zhao Z. Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables. Current Psychology 2020;39:769-81 [DOI:10.1007/s12144-019-00234-9]
19. Sadat Ahmadi H, Zadehmuhammadi F, Masoumbeigi M, Sohrabi F. Prevalence of Internet Addiction and Its Relationship with Demographic Characteristics among Allameh Tabataba'i University Students. journal of educational psychology 2012;8:20-30 [Persian]
20. Hajizadeh Meymandi M, Vakili Ghasemabad S, Mirmongereh A. A survey of the relationship between socio-psychological factors and internet addiction (Case study: Girl students of Yazd University) Journal of Woman in Culture Arts 2016;8:473-92 [Persian]
21. Vizshefer F. Assessment of Internet addiction in users of Internet cafes in Lar. Journal of Mental Health 2005;7:27-33
22. Vahidi far H, Nabavi zadeh H, Ardebily fard M. Assessment of internet addiction among college students in North Khorasan University of Medical Sciences in Bojnoord, Iran. Journal of North Khorasan University of Medical Sciences 2014;5:1081-8 [Persian] [DOI:10.29252/jnkums.5.5.S5.1081]
23. Shahbazirad A, Mirderikvand f. The relationship of internet addiction with depression, mental health and demographic characteristic in the students of Kermanshah University of Medical Sciences. Journal of Ilam University of Medical Sciences 2014;22:1-8 [Persian]
24. Alavi SS, Alaghemandan H, Maracy MR, Jannatifard F, Eslami M, Ferdosi M. Impact of addiction to internet on a number of psychiatric symptoms in students of isfahan universities, iran, 2010. International Journal of Preventive Medicine 2012;3:122-7
25. KhatibZanjani N, Agah H. The prevalence of internet addiction among the students of Payam Noor University, Semnan Province. Interdisciplinary Journal of Virtual Learning in Medical Sciences 2014;5:1-7 [Persian]
26. Jamwali A, Shekhar C, Choudhary N. Internet addiction as a predictor of depression, anxiety and stress (DASS). International Journal of Applied Home Science 2016;3:110-7
27. Seifi A, Ayati M, Fadaei M. The study of the relationship between internet addiction and depression, anxiety and stress among students of Islamic Azad University of Birjand. International Journal of Management, Economics and Social Sciences 2014;3:28-32
28. Gholamian B, Shahnazi H, Hassanzadeh A. The Prevalence of Internet Addiction and its Association with Depression, Anxiety, and Stress, among High-School Students. International Journal of Pediatrics 2017;5:4763-70
29. Solhi M, Farhandi H, Armoon B. Internet addiction among B.Sc. students in Health Faculty, Tehran University of Medical Sciences. Razi Journal of Medical Sciences 2013;20:40-7 [Persian]
30. Li CH. Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior research methods 2016;48:936-49 [DOI:10.3758/s13428-015-0619-7]
31. Beauducel A, Herzberg PY. On the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA. Structural Equation Modeling 2006;13:186-203 [DOI:10.1207/s15328007sem1302_2]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and Permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 All Rights Reserved | Payesh (Health Monitor)

Designed & Developed by : Yektaweb