Predicting users’ behavior: Gender and age as interactive antecedents of students’ Facebook use for research data collection

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Abstract

Previous studies have extensively examined how teachers and students utilize Facebook for instructional engagement, writing, research dissemination, and e-learning. However, there is a lack of research focusing on Facebook as a tool for collecting research data. This study aims to fill this gap by analyzing how final-year students utilize Facebook for research data collection (RDC). The study also assesses demographic differences in students’ use of Facebook for RDC. An online survey was conducted on a sample of 11,562 final-year students from tertiary institutions in Nigeria. The researchers followed global best practices in designing and validating the online survey. The survey items demonstrated clarity and relevance, with item content validity indices ranging from .71 to .99. Dimensionality and goodness of fit were assessed through exploratory and confirmatory factor analyses. Convergent validity was evaluated using average variance extracted, while discriminant validity was assessed using the Fornel-Larcker criterion and Heterotrait-Monotrait ratio. The composite reliability indices (.97, .94, and .90) confirmed the usability of the instrument’s three sub-scales. The study’s main findings revealed a significantly low usage of Facebook for RDC among students. Age was a significant predictor, indicating that older students used Facebook more for RDC. While males reported higher usage, the gender difference was negligible. The interaction of age and gender was significant in predicting students’ use of Facebook for RDC. These results have implications for future research, which are further discussed.

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APA

Petters, J. S., Owan, V. J., Okpa, O. E., Idika, D. O., Ojini, R. A., Ntamu, B. A., … Essien, V. E. (2024). Predicting users’ behavior: Gender and age as interactive antecedents of students’ Facebook use for research data collection. Online Journal of Communication and Media Technologies, 14(1). https://doi.org/10.30935/ojcmt/14104

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