Adaptation of Application-Based Smartphone Addiction Scale to Turkish Cultures

Abstract

The aim of this study is to adapt the Application Based Smartphone Addiction Scale, which has been developed to determine the smart phone addiction, which is becoming a common problem every day. The study was carried out with 474 students in 2017 - 2018 academic year at Bolu Abant Izzet Baysal University Faculty of Education. In exploratory factor analysis (EFA) for construct validity, the items were collected under a single factor in keeping with the original structure. The rate of explained variance was 52.658%. The eigenvalue of the factor was determined to be 3.159. Factor loadings of the items ranged between 0.531 and 0.835, and all of the error variances were less than 0.05. Asymptotic covariance and correlation matrices and Weighted Least Square (WLS) estimation method were preferred because of the structure of data in Confirmatory Factor Analysis (CFA). T-values of the items were found to be significant at the level of 0.01 (30.522-41.257). Factor loadings of the items were found to be high (0.50-0.81). When the model fit indices were examined, the fit values calculated as χ2/sd = 2.09, RMSEA = 0.068, GFI = 0.99, AGFI = 0.98, CFI = 0.98, NNFI = 0.96, NFI = 0.96, SRMR = 0.044 indicated acceptable or excellent fit. Cronbach Alpha internal consistency coefficient was found to be 0.81 within the scope of reliability studies. In addition, the test-retest correlation coefficient was found to be 0.92 at four-week intervals. In this study, it has been ensured that this scale related to smartphone addiction, which has recently become a serious problem, has been introduced to national literature.

Keywords

• Smartphone addiction,scale adaptation,validity,reliability

Uygulama Temelli Akıllı Telefon Bağımlılığı Ölçeğinin Türk Kültürüne Uyarlama Çalışması

Abstract

Bu çalışmanın amacı, her geçen gün yaygın bir sorun haline gelen akıllı telefon bağımlılığını tespit etmek amacıyla geliştirilmiş olan Uygulama Tabanlı Akıllı Telefon Bağımlılığı Ölçeğini ülkemiz kültürüne uyarlamaktır. Uygulama Bolu Abant İzzet Baysal Üniversitesi Eğitim Fakültesinde 2017 – 2018 eğitim öğretim yılında öğrenim gören 474 öğrenciyle gerçekleştirilmiştir. Yapı geçerliği için yapılan Açımlayıcı Faktör Analizinde (AFA) maddeler orijinal yapıdakine uygun olarak tek faktör altında toplanmıştır. Açıklanan varyans oranının %52.658 olduğu sonucuna ulaşılmıştır. Faktörün öz değerinin 3.159 olduğu belirlenmiştir. Madde faktör yüklerinin 0.531 ile 0.835 arasında değiştiği ve hata varyanslarının tümünün 0.05’ten küçük olduğu gözlenmiştir. Doğrulayıcı Faktör Analizinde (DFA) veri setinin yapısı nedeniyle, asimptotik kovaryans ve korelasyon matrisleri, Ağırlıklandırılmış En Küçük Kareler (Weigthed Least Square-WLS) kestirim yöntemi tercih edilmiştir. Madde t değerlerinin 0.01 düzeyinde anlamlı olduğu tespit edilmiştir (30.522-41.257). Madde faktör yüklerinin ise yüksek düzeyde olduğu sonucuna ulaşılmıştır (0.50-0.81). Model uyum indeksleri incelendiğinde ise χ2/sd=2.09, RMSEA =0.068 GFI = 0.99, AGFI=0.98, CFI=0.98, NNFI=0.96, NFI=0.96, SRMR =0.044 olarak hesaplanan uyum değerlerinin kabul edilebilir veya mükemmel uyuma işaret ettiği görülmüştür. Ölçeğe ilişkin güvenirlik çalışmaları kapsamında Cronbach Alfa iç tutarlılık katsayısının 0.81 olduğu belirlenmiştir. Ayrıca dört hafta arayla yapılan test tekrar test korelasyon katsayısının 0.92 olduğu bulunmuştur. Yapılan bu çalışmada son dönemde ciddi bir soruna dönüşmüş olan akıllı telefon bağımlılığına ilişkin bu ölçeğin ulusal literatüre kazandırılması sağlanmıştır.

Keywords

• Akıllı telefon bağımlılığı,ölçek uyarlama,geçerlik,güvenirlik

References

1. Aktaş, H., & Yılmaz, N. (2017). Üniversite gençlerinin yalnızlık ve utangaçlık unsurları açısından akıllı telefon bağımlılığı. International Journal of Social Sciences and Education Research, 3(1), 85-100.
2. Alfawareh, H. M., & Jusoh, S. (2014). Smartphones Usage Among Unıversıty Students: Najran Unıversıty Case. International Journal of Academic Research, 6(2). 321-326
3. Bian, M., & Leung, L. (2015). Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Social Science Computer Review, 33(1), 61-79.
4. Bozkurt, H., Şahin, S., & Zoroğlu, S. (2016). İnternet Bağımlılığı: Güncel Bir Gözden Geçirme. Journal Of Contemporary Medicine, 6(2), 1-13.
5. Choi, S. W., Kim, D. J., Choi, J. S., Ahn, H., Choi, E. J., Song, W. Y., ... & Youn, H. (2015). Comparison of risk and protective factors associated with smartphone addiction and Internet addiction. Journal of behavioral addictions, 4(4), 308-314.
6. Csibi, S., Demetrovics, Z., & Szabo, A. (2016). Hungarian adaptation and psychometric characteristics of Brief Addiction to Smartphone Scale (BASS). Psychiatria Hungarica, 31(1), 71-77.
7. Csibi, S., Griffiths, M. D., Cook, B., Demetrovics, Z., & Szabo, A. (2018). The Psychometric Properties of the Smartphone Application-Based Addiction Scale (SABAS). International journal of mental health and addiction, 16(2), 393-403.
8. Field, A. (2009). Discovering statistics using SPSS. Sage publications.

9. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological methods, 9(4), 466.
10. George, D., & Mallery, P. (2010). SPSS for Windows step by step. A simple study guide and reference (10. Baskı).
11. Griffiths, M. (1999). Internet addiction: Fact or fiction? The Psychologist, 12(5), 246-250.
12. Griffiths, M. (2000). Does Internet and computer" addiction" exist? Some case study evidence. CyberPsychology and Behavior, 3(2), 211-218.
13. Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-13.
14. Hawi, N. S., & Samaha, M. (2016). To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance. Computers & Education, 98, 81-89.
15. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 2.
16. Hu, S., & Oakland, T. (1991). Global and regional perspectives on testing children and youth: An empirical study. International Journal of Psychology, 26(3), 329-344.Jöreskog, K. G., & Sörbom, D. (1999). LISREL 8.30 and PRELIS 2.30. Chicago: Scientific Software International.
17. Kline, P. (2014). An easy guide to factor analysis. Routledge.
18. Kline, R. B. (1998). Software review: Software programs for structural equation modeling: Amos, EQS, and LISREL. Journal of psychoeducational assessment, 16(4), 343-364.
19. Kuss, D. J., Griffiths, M. D., & Pontes, H. M. (2017). Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder: Issues, concerns, and recommendations for clarity in the field. Journal of Behavioral Addictions, 6(2), 103-109. Kuss, D. J., Kanjo, E., Crook-Rumsey, M., Kibowski, F., Wang, G. Y., & Sumich, A. (2018). Problematic mobile phone use and addiction across generations: The roles of psychopathological symptoms and smartphone use. Journal of Technology in Behavioral Science, 3(3), 141-149.
20. Lee, S. (2007). Structural Equation Modelling: A Bayesian Approach. England: John Wiley& Sons, Ltd.
21. Lin, C. Y., Imani, V., Broström, A., Nilsen, P., Fung, X. C., Griffiths, M. D., & Pakpour, A. H. (2018). Smartphone Application-Based Addiction Among Iranian Adolescents: A Psychometric Study. International Journal of Mental Health and Addiction, Online First. 1-16.
22. Öner, N. (2008). Türkiye'de kullanilan psikolojik testlerden örnekler, bir basvuru kaynagi (2. basim). Istanbul: Bogaziçi Üniversitesi Yayımevi.
23. Parasuraman, S., Sam, A. T., Yee, S. W. K., Chuon, B. L. C., & Ren, L. Y. (2017). Smartphone usage and increased risk of mobile phone addiction: A concurrent study. International journal of pharmaceutical investigation, 7(3), 125-131.
24. Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321-325.
25. Selvaganapathy, K., Rajappan, R., & Dee, T. H. (2017). The effect of smartphone addiction on craniovertebral angle and depression status among university students. International Journal of Intagrative Medical Sciences, 4(5), 537-542.
26. Sha, P., Sariyska, R., Riedl, R., Lachmann, B., & Montag, C. (2018). Linking Internet Communication and Smartphone Use Disorder by taking a closer look at the Facebook and WhatsApp applications. Addictive Behaviors Reports, (Online First) doi.org/10.1016/j.abrep.2018.100148.
27. Tabachnick, B. G, Fidel, L. S. (2007). Using Mulivariate Statistics. MA: Allyn&Bacon, Inc.
28. Thompson, B. (2004).Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.
29. Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons.
30. Widyanto, L., & Griffiths, M. (2006). ‘Internet addiction’: a critical review. International Journal of Mental Health and Addiction, 4(1), 31-51.
31. Xu, J. (2015). Investigating factors that influence conventional distraction and tech-related distraction in math homework. Computers & Education, 81, 304-314.
32. Yildirim, C., Sumuer, E., Adnan, M., & Yildirim, S. (2016). A growing fear: Prevalence of nomophobia among Turkish college students. Information Development, 32(5), 1322-1331.
33. Zhao, S., Ramos, J., Tao, J., Jiang, Z., Li, S., Wu, Z., ... & Dey, A. K. (2016, September). Discovering different kinds of smartphone users through their application usage behaviors. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 498-509). ACM.