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
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).
