Adaptation of The Nomophobia Scale to The Field of Physical Education and Sports: Alternative Models Strategy Measurement Model Test

Abstract

This research is; It is the testing of measurement models with the alternative models' strategy of the Nomophobia Scale, which was developed by Yildirim and Correia. For this purpose, data obtained from a sample of 303 physical education and sports undergraduate students in the fall semester of the 2020-2021 academic year. Exploratory and confirmatory factor analysis was applied for the construct validity of the scale. Cronbach Alpha internal consistency and CR coefficients were calculated for reliability. The decomposition and convergence validities of the scale were tested with three alternative models with 3, 2 and 1 factors, using the X 2 difference tests of the predicted four-factor model (inability to access four-item information, losing five-item connection, inability to communicate with six items, not feeling comfortable with five items). The fact that the AVE values of the factors are lower than the CR values and the AVE values above .5 indicate that the factors have a combined validity. The fact that the AVE values of the factors are higher than the MSV and ASV values means that the factors have discrimination validity. In addition, the fact that the AVE scores of the factors are higher than the inter-factor correlations indicate that there is discrimination validity. From these results, the assumed fourfactor model is the model that provides the best fit with the data and the scale can be used as a highly valid and reliable tool for the physical education and sports undergraduate student stage.

Refined Management of Sports in Colleges and Universities and Information-based Innovative Education Driven by Big Data

“…(2) Dimensional analysis of data validity In the big data environment, due to its own characteristics of scale and high speed, the probability of data quality problems becomes greater [19][20]. Therefore, in order to evaluate the validity of data scientifically and reasonably in the big data environment, this paper mainly selects the integrity, correctness and compatibility of the data as the evaluation dimensions to evaluate it.…”