Algoritmik Medya İçerik Farkındalık (AMİF) Ölçeğinin Türkçe Versiyonunun Psikometrik Özellikleri

Year 2024, , 171 - 191, 30.06.2024

Nehir Yasan Ak , Tuğba Kamalı Arslantaş

https://doi.org/10.52911/itall.1447270

Abstract

Hızlı teknolojik gelişmelerle birlikte, kullanıcıların özellikle algoritmik farkındalık alanında yeni beceriler kazanmalarına duyulan ihtiyaç giderek artmaktadır. Bu çalışma, Zarouali ve arkadaşlarının (2021) geliştirdiği Algoritmik Medya İçerik Farkındalık Ölçeği'nin (AMİF) Türkçeye uyarlanmasını ve geçerlik ile güvenirliğinin test edilmesini hedeflemektedir. Orijinal ölçek, İngilizce olarak 13 madde ve dört faktör içeren 5’li Likert tipi bir ölçektir. Araştırmaya, 2022-2023 bahar döneminde, kolay örnekleme yöntemiyle seçilen bir devlet üniversitesinin çeşitli fakültelerinden 414 lisans öğrencisi katılmıştır. Ölçeğin yapısal geçerliliğini belirlemek için doğrulayıcı faktör analizi (DFA) kullanılmış, güvenilirliğini testip etmek için ise Cronbach alfa değerleri kontrol edilmiştir. DFA sonuçları, dört faktörlü yapının iyi bir model uyumu sergilediğini göstermiştir (χ2/df = 2.902, CFI = .95, GFI = .939, TLI = .93, RMR = .035, SRMR = .047, RMSEA = .068). Güvenilirlik katsayıları, faktörlerde .74 ile .81 arasında değişirken, genel alpha .90 olarak yüksek bir güvenilirlik göstermiştir. Madde-toplam korelasyon analizi, tüm maddelerin ölçeğe önemli bir katkıda bulunduğunu göstermiştir. Ayrıca hem yakınsak hem de ayırıcı geçerlilik yeterli düzeydedir. Sonuç olarak, Türkçe AMİF ölçeği, lisans öğrencilerinin algoritmik okuryazarlığını ölçmede geçerli ve güvenilir bir araçtır ve medya okuryazarlığı araştırmalarına da katkı sunma potansiyeli vardır.

Keywords

Algoritmalar, Algoritmik farkındalık, Ölçek uyarlama, Ölçek doğrulama

Ethical Statement

3 Şubat 2023 tarihinde Etik Kurul İzni alınmıştır.

Supporting Institution

Akdeniz Üniversitesi

Project Number

571441

Thanks

Doç. Dr. Tuba Livberber'e değerli katkılarından dolayı teşekkür ederiz.

Psychometric Properties of the Turkish Version of the Algorithmic Media Content Awareness (AMCA) Scale

Abstract

Given the rapid technological advancements, there is an increasing need for users to acquire new skills, particularly in the realm of algorithmic awareness. This study aims to adapt and validate the Algorithmic Media Content Awareness Scale (AMCA), developed by Zarouali et al. (2021), to Turkish context and to test its validity and reliability. The original scale is a 5-point Likert type measure consisting of 13 items with four factors in English. Participants included 414 undergraduate students from various faculties of a state university in Türkiye, selected through convenience sampling during the spring term of 2022-2023. The study employed confirmatory factor analysis (CFA) to assess the scale's construct validity and utilized Cronbach's alpha to examine reliability. The CFA results revealed a good model fit for the proposed four-factor structure (χ2/df = 2.902, CFI = .95, GFI = .939, TLI =.93, RMR = .035, SRMR = .047, RMSEA = .068). Reliability coefficients ranged from .74 to. 81 across the factors, with an overall alpha of .90, indicating high reliability. The item-total correlation analysis revealed that all items significantly contributed to the measure. Additionally, both convergent and discriminant validity were found to be satisfactory. Consequently, all evidence suggests that the Turkish version of the AMCA scale is a valid and reliable tool for assessing algorithmic literacy among undergraduate students, contributing significantly to the field of media literacy research.

Keywords

Algorithms, algorithmic awareness, Scale adaptation, Scale validation

Ethical Statement

Ethical Committee approval was obtained on February 3, 2023.

Supporting Institution

Akdeniz University

Project Number

571441

Thanks

We would like to thank Assoc. Prof. Dr. Tuba Livberber for her valuable contributions.

References

• Andrew, D. P. S., Pedersen, P. M., & McEvoy, C. D. (2011). Research methods in sport management. Champaign: Human Kinetics.
• Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35, 611-623. https://doi.org/10.1007/s00146-019-00931-w
• Beer, D. (2019). The social power of algorithms. The Social Power of Algorithms (pp. 1-13). Routledge.
• Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
• Cha, E.-S., Kim, K.H., & Erlen, J.A. (2007). Translation of scales in cross-cultural research: Issues and techniques. Journal of Advanced Nursing, 58(4), 386–395. https://doi.org/10.1111/j.1365-2648.2007.04242.x
• Cohen, J.N. (2018). Exploring echo-systems: How algorithms shape immersive media environments. Journal of Media Literacy Education, 10(2), 139151 https://doi.org/10.23860/JMLE-2018-10-2-8
• Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied psychology, 78(1), 98‐104. https://psycnet.apa.org/doi/10.1037/0021-9010.78.1.98
• Cresswell, J. (2012). Educational research: Planning, conducting and evaluating qualitative and quantitative research (4th ed.). Pearson Education Inc.
• de Groot, T., de Haan, M., & van Dijken, M. (2023). Learning in and about a filtered universe: young people’s awareness and control of algorithms in social media. Learning, Media and Technology, 48(4), 701-713. https://doi.org/10.1080/17439884.2023.2253730
• Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., ... & Sandvig, C. (2015, April). " I always assumed that I wasn't really that close to [her]" Reasoning about Invisible Algorithms in News Feeds. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 153-162).
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• García-Orosa, B., Canavilhas, J., & Vázquez-Herrero, J. (2023). Algorithms and communication: A systematized literature review. Comunicar, 31(74), 9-21. https://doi.org/10.3916/C74-2023-01
• Gillespie, T. (2014). The relevance of algorithms. Media technologies: Essays on communication, materiality, and society, 167(2014), 167. https://doi.org/10.7551/mitpress/9780262525374.003.0009
• Gran, A. B., Booth, P., & Bucher, T. (2021). To be or not to be algorithm aware: a question of a digital divide?. Information, Communication & Society, 24(12), 1779-1796. https://doi.org/10.1080/1369118X.2020.1736124
• Hargittai, E., Gruber, J., Djukaric, T., Fuchs, J., & Brombach, L. (2020). Black box measures? How to study people’s algorithm skills. Information, Communication & Society, 23(5), 764-775. https://doi.org/10.1080/1369118X.2020.1713846
• Jacques, J., Grosman, J., Collard, A. S., Ho, Y., Kim, A., & Jeong, H. S. (2020). In the Shoes of an Algorithm: A Media Education Game to Address Issues Related to Recommendation Algorithms. The Journal of Education, 3(1), 37-62. http://dx.doi.org/10.25020/JoE.2020.3.1.37
• Just, N. & Latzer, M. (2017). Governance by algorithms: reality construction by algorithmic selection on the Internet. Media, culture & society, 39(2), 238-258. https://doi.org/10.1177/0163443716643157
• Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/10.1080/1369118X.2016.1154087
• Klawitter, E. & Hargittai, E. (2018). “It’s like learning a whole other language”: The role of algorithmic skills in the curation of creative goods. International Journal of Communication, 12, 3490-3510. https://doi.org/10.5167/uzh-168021
• Kline, R. B. (2011). Convergence of structural equation modeling and multilevel modeling. The SAGE handbook of innovation in social research methods, 562-589. http://journals.sagepub.com/doi/pdf/10.4135/9781446268261.n31
• Krasmann, S. (2020). The logic of the surface: on the epistemology of algorithms in times of big data. Information, Communication & Society, 23(14), 2096-2109. https://doi.org/10.1080/1369118X.2020.1726986
• Latzer, M. & Festic, N. (2019). A guideline for understanding and measuring algorithmic governance in everyday life. Internet Policy Review, 8(2). https://doi.org/10.14763/2019.2.1415
• Light, B., Burgess, J. & Duguay, S. (2016). The Walk trough Method: An Approach to the Study of Apps. New Media and Society 20(3), 881–900. https://doi.org/10.1177/1461444816675438
• Mason, J., Classen, S., Wersal, J. & Sisiopiku, V., 2021. Construct validity and test–retest reliability of the automated vehicle user perception survey. Frontiers in Psychology. 12, 626791. https://doi.org/10.3389/fpsyg.2021.626791
• Merenda, P. F. (2006). An overview of adapting educational and psychological assessment instruments: Past and present. Psychological Reports, 99, 307–314. https://doi.org/10.2466/pr0.99.2.307-314
• Perrotta, C. & Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in education. Learning, Media and Technology, 45(3), 251-269. https://doi.org/10.1080/17439884.2020.1686017
• Proferes, N. (2017). Information flow solipsism in an exploratory study of beliefs about Twitter. Social Media + Society, 3(1). https://doi.org/10.1177/2056305117698493
• Shin, D., Kee, K. F., & Shin, E. Y. (2022). Algorithm awareness: Why user awareness is critical for personal privacy in the adoption of algorithmic platforms? International Journal of Information Management, 65, 102494.https://doi.org/10.1016/j.ijinfomgt.2022.102494
• Sireci, S. G. (2005). Using bilinguals to evaluate the comparability of different language versions of a test. In R. K. Hambleton, P. F. Merenda, & C. D. Spielberger (Eds.), Adapting educational and psychological tests for cross-cultural assessment (pp. 117-138). Mahwah, NJ: Erlbaum.
• Steven. J. P. (2009). Applied multivariate statistics for the social sciences (5th ed.). New York: Taylor & Francis Group.
• Streiner, D.L., Norman, G.R., Cairney, J., 2015. Health Measurement Scales: A Practical Guide to Their Development and Use. Oxford University Press, USA.
• Swart, J. (2021). Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social media+ society, 7(2), https://doi.org/10.1177/2056305121100882
• Thurman, N., Moeller, J., Helberger, N., & Trilling, D. (2019). My friends, editors, algorithms, and I: Examining audience attitudes to news selection. Digital journalism, 7(4), 447-469. https://doi.org/10.1080/21670811.2018.1493936
• Willson, M. (2019). Algorithms (and the) everyday. The Social Power of Algorithms (pp. 137-150). Routledge.
• Willis, G. B. (2004). Cognitive interviewing: A tool for improving questionnaire design. Sage Publications.
• Zarouali, B., Boerman, S. C., & de Vreese, C. H. (2021). Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics and Informatics, 62, 101607. https://doi.org/10.1016/j.tele.2021.101607
• Zarsky, T. (2016). The trouble with algorithmic decisions: an analytic road map to examine efficiency and fairness in automated and opaque decision making. Science Technology Human Values, 41, 118–132. https://doi.org/10.1177/0162243915605575
• Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Transparency in algorithmic and human decision-making: is there a double standard?. Philosophy & Technology, 32, 661-683. https://doi.org/10.1007/s13347-018-0330-6