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Prevalence and underlying factors of mobile game addiction among university students in Bangladesh

Nowadays, the youth are more engaging with their more advanced smartphones having high-quality graphics and gaming features. However, existing literature depicts that adolescents suffer from several forms of psychological problems including mental health,

depression, loneliness, insomnia and low self-control due to mobile game addiction. Therefore, this study aims to find the prevalence and motivating factors for mobile game addiction among university students of Bangladesh.

A cross-sectional survey was carried out to collect the required information from 1125 students of three universities in Bangladesh. Descriptive statistics, χ2 test and ordinal regression model are employed to meet the objective of this study.

Design and procedure
The online survey was conducted among 1125 respondents from the selected three universities in Bangladesh. In this current pandemic situation, it is not feasible to do a face-to-face survey and that is why the data are collected online via the link of the designed google form.

Prior to participating in this survey, the authors inform the respondents about the purpose of this study and ensure that the information they provide would be kept confidential and oral consent is taken.

Respondents who gave consent to participate in this survey were then sent a link to the questionnaire and accompanying instructions to complete it. Despite the fact that this research is not related to human trials, it was carried out in accordance with the Declaration of Helsinki ethical standard. The survey was conducted over a 1-month period from 23 October 2020 to 27 November 2020.

Statistical methods
Quantitative research approach was applied in this explorative study. The cross-tabulation was carried out for descriptive analysis along with the χ2 test and Goodman and Kruskal’s γ (G) (Goodman and Kruskal, Reference Goodman and Kruskal1954),

for making comparisons among variables and ascertain the significant relationship between the considered variables and the level of mobile game addiction. Cronbach’s α has been reported to check the internal consistency of the variables in this study.

Here, the reliability of the data was checked using Cronbach’s α developed by Lee Cronbach in 1951 (Cronbach, Reference Cronbach1951) which lies between 0 and 1 and the value close to 1 provides more reliability (Nunnally and Bernstein, Reference Nunnally and Bernstein1994). The acceptable value of Cronbach’s α is 0.70 (Nunnally, Reference Nunnally1978;

Zikmund, Reference Zikmund1999). The Cronbach’s α can be defined as , where K is the number of components (K items), is the average variance of each component (item) and is the average of all covariances between the components across the current sample of persons, that is, without including the variances of each component.

Finally, as the outcome variable is classified according to their order of magnitude, ordinal logistic regression (OLR) analysis has been conducted to examine the influences of targeted variable on mobile game addiction. Let Y be an ordinal outcome with jcategories. Then P(Y ≤ j) is the cumulative probability of Yless than or equal to a

specific category j = 1, 2, …, J − 1. The odds of being less than or equal to a particular category can be defined as, P(Y ≤ j)/P(Y > j) for j = 1, 2, …, J − 1 since P(Y > J) = 0 and dividing by zero is undefined. The log odds is also known as the logit, so that log (P(Y ≤ j)/P(Y > j)) = logit(P(Y ≤ j). The general form of the OLR model can be written as,

where, logit() is the link function, θ j is the threshold for the jth category, p is the number of regression coefficients, X i1, X i2, …, X ip are the values of the predictors for the ith case and β 1, β 2, …, β p are regression coefficients (McCullagh and Nelder, Reference McCullagh and Nelder1989; Harrell, Reference Harrell2015).

In OLR, several link functions, i.e. cauchit, complementary , logit, negative and probit were used in several research (Liao, Reference Liao1994; Javali and Pandit, Reference Javali and Pandit2010;

Agresti, Reference Agresti2013; Fernández-Navarro, Reference Fernández-Navarro2017; Smith et al., Reference Smith, Walker and Mckenna2019; Singh et al.,

Reference Singh, Dwivedi and Deo2020). Among these link functions, negative log-log ( − log ( − log (y))) has been used in this study since lower categories are more probable to building up model. For better understanding and interpretations, odds ratio has also been calculated. All the analyses were performed using SPSS 25.