Internet is an important medium of interaction for individuals. The Internet has several advantages such as easy access to information, more frequent contact with friends, hosting educational materials, entertainment and playful use. Besides, there are various online risks such as security threats, cyber bullying, sexual contents on the internet (Baştürk Akça, Sayımer & Ergül, 2015).  It can be said that the reasons for the vulnerability of adolescents and children to online risks are lack of experience, level of awareness, strategies to cope with risks and critical thinking skills that they can use to get rid of online risks (OECD, 2012). In this case, it has become very important how the internet can be used effectively and safely without damaging individuals physically, emotionally and psychologically. In the OECD (2012) report, it is stated that individuals need to increase their awareness of online risks to protect themselves from online risks. Trainings of safely usage of Internet and the effective integration of media or internet literacy with school curricula have been suggested to be useful strategies for individuals to use the Internet safely and to make the internet useful for them. In particular, parents, teachers or young adults who can be defined as adults, generally do not know or have a very basic level of knowledge about which behaviors may pose a risk to themselves and their surroundings on the Internet and that they may create material or moral problems as a result of these behaviors. In a study of online risky behaviors of adolescents and university students, they also pointed out that university students are also taking various online risks. In addition, it is suggested that in-depth study of why university students are taking various online risks while adolescents perform various risky behaviors because of the developmental period characteristics of puberty, their thoughts that "nothing happens to me" (White, Gummerum & Hanoch, 2016). When reviewing the limited number of studies on this suggestion in the literature, it is noted that similar online risk perceptions of both adolescents and adults (Baumgartner, Valkenburg & Peter, 2010), or similar behavior in both groups, are observed in the context of various online risky behaviors such as online sex appeal behavior or the provision of personal information to social networks (Walrave, Vanwesenbeeck & Heirman, 2012; Christofides, Desmarais & Muise, 2010). Studies that examine the causes of risky behaviors of young adults in an online setting, in other words, the holistic view of what factors influence these decisions are very limited. It is worth noting that there is a limited number of studies on digital skills that individuals need to have in order to ensure their security in online environments (Erol, Şahin, Yılmaz & Haseski, 2015) or the work done in this respect is at a basic level and according to the general user level (Van Deursen, Helsper & Eynon, 2014). It has been examined which measurement tools are used in various studies related to digital security skill and it was seen that the questionnaire mostly was used (Sonck, Livingstone, Kuiper & Haan, 2011). In this sense, the development of a valid and reliable digital safety self-efficacy scale for university students will contribute to the literature. For this reason, it is aimed to develop a valid and reliable measurement tool that will measure the digital safety self-efficacy skills of university students.

In this study, “Digital Safety Self-Efficacy Scale” development process steps described by DeVellis (2012) were followed. Firstly, the literature related to online security, digital safety and self-efficacy was examined to generate initial item pool. Besides, eight peer researchers who were experienced within the scope of the research were consulted on the structure of the latent variable. Based on the literature review and responses from peer researchers, 35 items were written in the initial item pool. After, eight experts experienced in internet kids and digital skills evaluated the structure of scale and the suitability of items. Based on the expert opinions, 12 items were eliminated and some of items were revised. Consequently, the scale as a self-report instrument comprised of 23 items on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Sample items might be given as, “I can configure my privacy and security settings on my social network accounts like Facebook, Instagram.”, “I can use security software on my digital devices.” Exploratory factor analysis (EFA) was performed to determine the factorial structure of the scale. The sample consisted of 490 university students including different faculties and grade levels. Among them 291 (59.39%) were female, 199 (40.61%) were male. In the literature, sample size is required to be five to ten times the number of items on a scale (Kass & Tinsley, 1979; Kline, 1994). Confirmatory factor analysis (CFA) will be conducted in order to test the factor structure of the scale. Data were collected for CFA. This collected data is being prepared for analysis.

EFA was performed using principal component analysis (PCA) to extract components with eigenvalues (Worthington & Whittaker, 2006). For EFA, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were conducted. The KMO value was found to be .968. Tabachnick and Fidell (2012) recommends that the KMO value be higher than 0.6, and the calculated KMO value (.968) in this study was higher than that. Bartlett’s test of sphericity (χ2(595) =11738,450; p<.001) is appropriate for factor analysis. Prior to this analysis, the assumptions of EFA, which were outliers, univariate normality, and multicollinearity were checked. In order to determine the correlations between factors and to help interpret the factors, Varimax vertical axis rotation was used (Tabachnick & Fidell, 2012). The factor load lower limit of each item was taken as .40 (Tabachnick & Fidell, 2012), and all items with a factor loading above 0.4 were included. An eigenvalue greater than 1 was the criterion for determining the number of factors extracted. As a result, two factors were extracted. According to the result of the EFA, the first factor consisted of eleven items ranging from .65 to .81 and the second factor load consisted of twelve items ranging from .52 to .75. Whole factors explained 56.82% of the total variance. The first factor explained 31.14% of the total variance and was labeled “Safety Skills in Online Applications”. The second factor explained 25.68% of the total variance and was labeled “Safety Skills in Digital Devices”. The Cronbach’s alpha coefficient for the whole scale was found to be .947, whereas the values of Cronbach’s alpha coefficient for individual factors of the scale ranged between .939 and .906. It is considered sufficient for the reliability ratio to be .70 or higher (Nunnally, 1978). Data is being analyzed for CFA.

Baştürk Akça, E., Sayımer, İ., & Ergül, S. (2015). Ortaokul öğrencilerinin sosyal medya kullanımları ve siber zorbalık deneyimleri: Ankara örneği. Global Media Journal: Turkish Edition, 5(10). Baumgartner, S. E., Valkenburg, P. M., & Peter, J. (2010). Unwanted online sexual solicitation and risky sexual online behavior across the lifespan. Journal of Applied Developmental Psychology, 31(6), 439-447. Christofides, E., Desmarais, S., & Muise, A. (2010). Privacy and Disclosure on Facebook: Youth and Adult's Information Disclosure and Perceptions of Privacy Risks. Guelph: University of Guelph. DeVellis, R.F., (2012). Scale Development: Theory and Applications (Third Edition). Sage Publications, California. Erol, O., Şahin, Y. L., Yılmaz, E., & Haseski, H. İ. (2015). Personal Cyber Security Provision Scale development study Kişisel Siber Güvenliği Sağlama Ölçeği geliştirme çalışması. Journal of Human Sciences, 12(2), 75-91. OECD, 2012. The Protection of children online. Report on risks faced by children online and policies to protect them. Available at: http://www.oecd.org/sti/ieconomy/childrenonline_with_cover.pdf Kass, R. A., & Tinsley, H. E. A. (1979). Factor analysis. Journal of Leisure Research, 11, 120-138. Kline, P. (1994). An easy guide to factor analysis. New York: Routledge. Nunnally, J. C. (1978). Psychometric testing. New York: McGraw-Hill. Sonck, N., Livingstone, S., Kuiper, E. & De Haan, J. (2011) Digital literacy and safety skills. EU Kids Online, London School of Economics & Political Science, London, UK. Tabachnick, B. G. ve Fidell, L. S. (2012). Using multivariate statistics (6th ed.). New Jersey:Pearson. Van Deursen, A.J.A.M., Helsper, E.J. & Eynon, R. (2014). Measuring Digital Skills. From Digital Skills to Tangible Outcomes project report. Available at: www.oii.ox.ac.uk/research/projects/?id=112 Walrave, M., Vanwesenbeeck, I., & Heirman, W. (2012). Connecting and protecting? Comparing predictors of selfdisclosure and privacy settings use between adolescents and adults. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 6(1), article 3. doi: 10.5817/CP2012-1-3 White, C. M., Gummerum, M., & Hanoch, Y. (2016). Framing of Online Risk: Young Adults’ and Adolescents’ Representations of Risky Gambles. Decision. Advance online publication. http://dx.doi.org/10.1037/dec0000066 Worthington, R. L., ve Whittaker, T. A. (2006). Scale development research a content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806-838.