Impact of Modern Technology and Electronic Screen Usage on Children: A Factor Analysis Study in Erbil City
Main Article Content
Abstract
This study looks into how children in Erbil City are affected by electronic screens and modern technology, with a particular emphasis on identifying important elements that affect their use. Eight primary components were identified by factor analysis, accounting for 59.906% of the variance in the data. The amount of time spent on screens, parental control measures, effects on education and health, and social factors were all significant variables. The sample's suitability for factor analysis was validated using the Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. The results show how technology affects kids in many ways and offer guidance for focused interventions and laws that encourage responsible technology use.
Downloads
Article Details
References
Blbas, Hazhar, & Kadir, Dler Hussein. (2019). An application of factor analysis to identify the most effective reasons that university students hate to read books. International Journal of Innovation, Creativity and Change, 6(2), 251-265.
Griffiths, Mark D, Kuss, Daria J, Billieux, Joël, & Pontes, Halley M. (2016). The evolution of Internet addiction: A global perspective. Addictive behaviors, 53, 193-195.
Hill, Brent Dale. (2011). The sequential Kaiser-Meyer-Olkin procedure as an alternative for determining the number of factors in common-factor analysis: A Monte Carlo simulation: Oklahoma State University.
King, Daniel L, Delfabbro, Paul H, Griffiths, Mark D, & Gradisar, Michael. (2012). Cognitive‐behavioral approaches to outpatient treatment of Internet addiction in children and adolescents. Journal of clinical psychology, 68(11), 1185-1195.
Langsrud, Øyvind, & Næs, Tormod. (2003). Optimised score plot by principal components of predictions. Chemometrics and intelligent laboratory systems, 68(1-2), 61-74.
Lawley, David N, & Maxwell, Adam E. (1962). Factor analysis as a statistical method. Journal of the Royal Statistical Society. Series D (The Statistician), 12(3), 209-229.
Norris, Megan, & Lecavalier, Luc. (2010). Evaluating the use of exploratory factor analysis in developmental disability psychological research. Journal of autism and developmental disorders, 40, 8-20.
Pett, Marjorie A, Lackey, Nancy R, & Sullivan, John J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research: sage.
Rummel, Rudolph J. (1988). Applied factor analysis: Northwestern University Press.
Tobias, Sigmund, & Carlson, James E. (1969). Brief report: Bartlett's test of sphericity and chance findings in factor analysis. Multivariate behavioral research, 4(3), 375-377.
Wells, Ingrid E. (2010). Psychology of emotions, motivations and actions: Psychological well-being. New York: Nova Science Publisher, Inc, 6(9), 111334.
Wold, Svante, Esbensen, Kim, & Geladi, Paul. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3), 37-52.
Baban, Prof. Dr. Waleed Khaled Abdul Karim, & Saeed, Eng. M. Joan Ahmed Hamad. (2023). Structural equivalence of the cognitive representation scale using exploratory and affirmative factor analysis. Journal of Intelligence Research, 17(35), 129-155.
Tayoub, Mahmoud. (1998). How do we explain the results of cross-factor analysis?. Tishreen University Journal-Economic and Legal Sciences Series, 20(1).