Adaptation of Application-Based Smartphone Addiction Scale to Turkish Cultures

Year 2019, Volume: 9 Issue: 2, 261 - 281, 15.08.2019

Yunus Altundağ , Alperen Yandı , Ali Ünal

https://doi.org/10.19126/suje.516365

Cited By: 7

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

• 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.
• Alfawareh, H. M., & Jusoh, S. (2014). Smartphones Usage Among Unıversıty Students: Najran Unıversıty Case. International Journal of Academic Research, 6(2). 321-326
• 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.
• Bozkurt, H., Şahin, S., & Zoroğlu, S. (2016). İnternet Bağımlılığı: Güncel Bir Gözden Geçirme. Journal Of Contemporary Medicine, 6(2), 1-13.
• 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.
• 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.
• 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.
• Field, A. (2009). Discovering statistics using SPSS. Sage publications.
• 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.
• George, D., & Mallery, P. (2010). SPSS for Windows step by step. A simple study guide and reference (10. Baskı).
• Griffiths, M. (1999). Internet addiction: Fact or fiction? The Psychologist, 12(5), 246-250.
• Griffiths, M. (2000). Does Internet and computer" addiction" exist? Some case study evidence. CyberPsychology and Behavior, 3(2), 211-218.
• 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.
• 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.
• Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 2.
• 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.
• Kline, P. (2014). An easy guide to factor analysis. Routledge.
• Kline, R. B. (1998). Software review: Software programs for structural equation modeling: Amos, EQS, and LISREL. Journal of psychoeducational assessment, 16(4), 343-364.
• 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.
• Lee, S. (2007). Structural Equation Modelling: A Bayesian Approach. England: John Wiley& Sons, Ltd.
• 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.
• Öner, N. (2008). Türkiye'de kullanilan psikolojik testlerden örnekler, bir basvuru kaynagi (2. basim). Istanbul: Bogaziçi Üniversitesi Yayımevi.
• 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.
• Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321-325.
• 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.
• 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.
• Tabachnick, B. G, Fidel, L. S. (2007). Using Mulivariate Statistics. MA: Allyn&Bacon, Inc.
• Thompson, B. (2004).Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.
• Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons.
• Widyanto, L., & Griffiths, M. (2006). ‘Internet addiction’: a critical review. International Journal of Mental Health and Addiction, 4(1), 31-51.
• Xu, J. (2015). Investigating factors that influence conventional distraction and tech-related distraction in math homework. Computers & Education, 81, 304-314.
• 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.
• 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.