Analyzing Gaming Attitudes and Behaviors

First, collected data were processed through data input, data cleansing, and data reversing [41 ]. Second, frequencies, percentages, maximum score, minimum score, mode, median, mean, and standard deviation were used to describe the demographic characteristics and the participants’ habits and attitudes regarding internet use and video gaming [52 (link)]. To facilitate the analysis of the current and favorite video games played, the entries were recategorized into different game genres. We utilized an extensive gaming archive [56 ] to sort the entries into 10 mutually exclusive game genres. Lastly, a two-tailed independent t test was used to describe the possible differences in the normally distributed variables of the gaming attitudes. The Pearson’s correlation coefficient was used to quantify the linear relationship between the categories of the gaming attitudes. Statistical significance was established at P=.05. For the dimensions of the categories of the gaming attitudes that were the most intercorrelated, principal analysis with 3 methods of rotation (varimax, equamax, and promax) was used to confirm the convergence of the 9 categories of gaming attitudes into fewer principle factors for easy interpretation. The SPSS software for Windows 22.0 (IBM Corp) was used for the data analysis.