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Development and psychometric properties of the problematic mobile video gaming scale

  • Jia-Rong ShengEmail author
  • Jin-Liang Wang
Open Access
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Abstract

This study aimed to develop the Problematic Mobile Video Gaming Scale (PMVGS) and test its reliability and validity in two samples. Sample 1 contained 578 junior high school students, and Sample 2 contained 1501 college students. The data from Sample 1 was examined using exploratory factor analysis to define the factorial structure of the scale. As a result of the exploratory factor analysis, a three-factor structure (withdrawal symptoms, mood modification, and conflict) composed of 11 items was obtained, which explained 66.25% of the total variance. Confirmatory factor analysis was then performed to confirm that factorial structure using data from Sample 2; the obtained fit indices confirmed the second-order three-factor structure, indicating good construct validity. We used depression, loneliness, social anxiety, and game usage time to evaluate the scale’s criterion-related validity, and significant correlations between PMVGS and these criterion variables were revealed. The scores of the PMVGS and the Excessive Online Gaming Scale, which have similar structures, were strongly correlated, suggesting that the PMVGS has good convergent validity. The moderate correlation between the scores of the PMVGS and the Mobile Phone Addiction Index scale, which have different structures, indicates that the PMVGS has good discriminant validity. Further, the scale has a high level of internal consistency, with Cronbach’s alpha coefficients of .84 and .91 for the two samples. Thus, the PMVGS has good reliability and validity, and is an effective and reliable tool for assessing problematic mobile video gaming.

Keywords

Problematic mobile video gaming Scale development Psychometric properties Internet gaming disorder 

According to the China Internet Network Information Center’s (CNNIC) latest report, various online entertainment applications are being transferred to mobile terminals. The number of mobile online game users has reached 407 million, which accounts for 54.1% of mobile online users, and 44.1% of these are under the age of 24 (CNNIC 2018). It has been demonstrated that there is a correlation between excessive smartphone gaming and a range of mental health problems, including anxiety, depression, low self-control, worsened mood (Cheever et al. 2014; Jeong et al. 2016; Thomée et al. 2011), and dependence-like symptoms (Cheng and Leung 2016; Kim et al. 2011). Excessive smartphone gaming has gradually become the focus of social attention.

Due to the rapid development of mobile technology, mobile games are no longer limited to the smartphone platform, and other mobile devices, such as the iPad, are being used for gaming. Therefore, people are beginning to use the concept of “mobile video games” to replace the traditional concept of “mobile games.” Some researchers have defined mobile video games as those played online via mobile devices. They are particularly popular when they can be downloaded for free (e.g., “freemium games,” which are free but customers pay for extra features) and can be used as single-player or multiplayer games (Su et al. 2016). Recently, the number of mobile video game users has risen sharply, and the mobile video game market has expanded, which has led to a gradual change in the traditional game mode, that is from the previous game mode based on desktop computers to the game mode based on smartphones, tablets, and other mobile devices. Mobile video games have become the new and main driving force in the online game industry.

Previous studies of internet gaming disorder (IGD) have mainly focused on traditional online gaming addiction based on desktop computers, but recent research suggests that there is only a moderate correlation between the different forms of internet addiction (Sha et al. 2018). In addition, although problematic mobile video gaming shares some similarities with traditional desktop computer online gaming addiction, there are still significant differences. Specifically, mobile video games are characterized by portability, immediacy, and accessibility (Lee and Kim 2016). Other specific features of mobile video games that make them more attractive than traditional desktop computer online games include the shortened time of each round of the game, the ease of starting as a beginner, and the lower internet speed requirement. Therefore, compared with the traditional game mode, mobile video games may be more difficult to control and more prone to problematic use, making it easier for users to overindulge in them.

Given that problematic mobile video gaming is a relatively new form of IGD, more research is needed in this area. There is still a lot of debate about whether IGD is a real addiction, so it is worth further study about whether problematic mobile video gaming is a real disorder, or merely a symptom (Bean et al. 2017). Therefore, in the current study, we used a more conservative term “problematic mobile video gaming”, rather than “mobile video gaming addiction”. However, there is no instrument to assess or diagnose problematic mobile video gaming. Based on the existing research and the characteristics of mobile video games, this study developed the Problematic Mobile Video Gaming Scale (PMVGS) and tested its psychometric properties to ensure that the instrument had sufficient validity and reliability. In this study, problematic mobile video gaming was defined as a phenomenon in which users strongly rely on mobile games and cannot help playing them repeatedly over a comparatively long period (Sun et al. 2015). We believe that our study makes a significant contribution to future research in this field because the PMVGS is the first instrument to assess problematic mobile video gaming.

Criterion-Related Validity of the PMVGS

Criterion-related validity is the degree of the relationship between the scale and an external criterion; the higher the correlation between them, the better the scale’s criterion-related validity (Wu 2010). Therefore, in this study, depression, loneliness, social anxiety, and game usage time were included and used as the external criteria to evaluate the PMVGS’s validity. Since problematic mobile video gaming is somewhat similar to traditional IGD, the relationship between the external criteria variables covered in this study and problematic mobile video gaming should be similar to the relationship between these variables and traditional IGD found in previous studies. As described below, earlier studies have found that IGD is positively correlated with depression (King and Delfabbro 2016; Lin et al. 2016; Liu et al. 2018; Stetina et al. 2011; Wu et al. 2018), loneliness (Lemmens et al. 2011; Lemmens et al. 2015; Qin et al. 2007; Spilkova et al. 2017; Van Rooij et al. 2014), social anxiety (Hussain et al. 2017; Nikolaidou et al. 2016; Van Rooij et al. 2014; Vanzoelen and Caltabiano 2016), and game usage time (Lemmens et al. 2015; Pontes and Griffiths 2015). Hence, to prove that the PMVGS has good criterion-related validity, the participants’ scores on the scale should be significantly and positively correlated with the level of depression, loneliness, social anxiety, and mobile video game usage time.

Depression

IGD is characterized by cognitive and emotional deficits. Previous studies have reported the co-occurrence of IGD and depression (Liu et al. 2018). Specifically, depression symptoms frequently occur in individuals with internet addiction and IGD (Kaess et al. 2014; Lau et al. 2018; Wu et al. 2018). Thus, we assumed that problematic mobile video gaming is also positively correlated with an individual’s depression level.

Loneliness

Loneliness is defined as an unpleasant experience that derives from important deficiencies in a person’s network of social relationships (Blazer 1983). Previous studies have consistently confirmed the positive relationship between loneliness and online gaming addiction (Lemmens et al. 2011; Lemmens et al. 2015; Qin et al. 2007; Spilkova et al. 2017; Van Rooij et al. 2014). Thus, we assumed a positive relationship between problematic mobile video gaming and loneliness.

Social Anxiety

Social anxiety, which is the most common anxiety disorder, is the state of tension or discomfort experienced by individuals in social situations (Rapee and Heimberg 1997). Internet addiction, smartphone addiction, and online gaming addiction are all associated with social anxiety (Fayazi and Hasani 2017; Hussain et al. 2017; Nikolaidou et al. 2016). Furthermore, individuals with a serious tendency for online gaming addiction have significantly higher social anxiety levels than those who use online games normally (Van Rooij et al. 2014; Vanzoelen and Caltabiano 2016). Therefore, we assumed that problematic mobile video gaming would be positively correlated with social anxiety.

Mobile Video Game Usage Time

Previous studies have found a correlation between online gaming addiction and time spent on games (Lemmens et al. 2015; Lemmens et al. 2009; Pontes et al. 2014). In addition, this variable has helped to inform the American Psychiatric Association’s (APA) definition of IGD since disordered gamers typically devote at least 30 h per week to gaming (APA 2013). Thus, as there is a positive relationship between online gaming addiction and game usage time, we assumed there is a positive relationship between problematic mobile video gaming and mobile video game usage time.

Convergent Validity and Discriminant Validity of the PMVGS

Based on the theory of measurement, the results of two scales with the same or similar structure should be strongly correlated, while the results of two scales with different structures should be relatively weakly correlated, that is, a scale should have good convergent validity and discriminant validity (Wu 2010). The PMVGS was developed by referring to a series of online game addiction scales with good reliability and validity, and utilizing items from those scale with appropriate revisions based on the characteristics of mobile video games. As a result, although there were differences, the structure of the PMVGS scale and the other scales is somewhat similar, and we expect those scales to be relatively strongly correlated.

More specifically, we used the Excessive Online Gaming Scale to investigate the scale’s convergent validity (Kardefelt-Winther 2014a, b). For discriminant validity, we used the Mobile Phone Addiction Index (MPAI) scale because the PMVGS and MPAI scale are differently constructed tests (Leung 2008). However, as problematic mobile video gaming based on smartphones is a specific form of pathological mobile phone use, there still exists a certain degree of connection between the two; as such, there should be a moderate correlation between them. Therefore, if there is a strong correlation between the PMVGS’s total score and the Excessive Online Gaming Scale, and there is a moderate correlation between the PMVGS’s total score and the MPAI scale, it will indicate that the PMVGS has good convergent validity and discriminant validity.

Methods

Participants and Procedure

Sample 1

This sample was used for item analysis, exploratory factor analysis (EFA), and reliability and validity tests. The PMVGS, depression scale, loneliness scale, social anxiety scale, mobile video game usage times, MPAI and excessive online gaming scale were used for testing in this sample. A stratified sampling method was employed to identify participants. Six hundred students enrolled in the seventh, eighth, and ninth grades of a junior high school in Guizhou Province, China were selected at the target population of Sample 1. The survey was conducted within classrooms, and authorization from both the school’s principal and students’ parents was obtained. Prior to answering the items, participants read information about the implications of participation and data protection. The information stressed that participation was completely voluntary and anonymous. Excluding missing or incomplete data, 578 survey responses were collected (mean age = 15 years, SD = 1.05). Among the participants, 56.7% were male (n = 328) and 43.3% were female (n = 250).

Sample 2

This sample was used for the confirmatory factor analysis (CFA) and reliability test. The PMVGS was used for testing in this sample. To further verify the stability of the factor structure obtained by the EFA and to ensure the generalizability of the scale, 1518 undergraduates from the Southwest University of Chongqing in China were randomly selected as the target population of Sample 2, and the survey was conducted within classrooms. Excluding missing or incomplete data, a total of 1501 valid questionnaires were collected (mean age = 19.39 years, SD = 1.21). Among the respondents, 35.4% were male (n = 531) and 64.6% were female (n = 970).

Development of the PMVGS

In the latest version of the Diagnostic and Statistical Manual of Mental Disorders (5th ed. [DSM–5]), the APA included IGD as a tentative disorder in the appendix (APA 2013), which widely aroused researchers’ attention. Researchers have developed a series of assessment tools to measure IGD, which have mainly focused on traditional online gaming addiction on desktop computers. For example, Lemmens et al. (2009) developed the Gaming Addiction Scale (GAS), which consists of 21 items, based on the seven dimensions of the diagnostic criteria for gambling addiction (salience, tolerance, withdrawal symptoms, relapse, mood modification, problems, and conflicts). Pontes et al. (2014) developed the 20-item Internet Gaming Disorder Test (IGD-20 T) based on six dimensions of the addiction model framework—salience, tolerance, withdrawal symptoms, relapse, mood modification, and conflicts (including displacement, problems, and deception)—which was the first scale to evaluate online gaming behavior. Subsequently, Lemmens et al. (2015) used the nine diagnostic criteria for IGD of the DSM-5 as nine dimensions (preoccupation, tolerance, withdrawal, persistence, escape, displacement, problems, deception, and conflict) to develop the Internet Gaming Disorder Scale (IGDS), which contains 27 items. Finally, Király et al. (2017) developed the 10-item Internet Gaming Disorder Test.

Despite the similarities between problematic mobile video gaming and traditional desktop computer online gaming addiction, there are still great differences between the two (Griffiths et al. 2015; Lee and Kim 2016; Lopez-Fernandez et al. 2018). First, in contrast to traditional desktop computer online games, mobile video games are characterized by portability, immediacy, and accessibility (Lee and Kim 2016). At the same time, some existing mobile video games are highly restored traditional desktop computer online games and have specific features that make them more attractive than traditional desktop computer online games, including the shortened time of each round of the game, the ease of starting as a beginner, and a lower internet speed requirement. Therefore, for game players, playing mobile video games is no longer limited by time and place, can result in satisfaction in a shorter time, and has a more effective immediate reward function. Consequently, compared with the traditional game mode, mobile video games may be more difficult to control and more prone to problematic use.

Recognizing the typical characteristics of problematic mobile video gaming that differentiate it from traditional forms of online gaming addiction and with reference to existing research, this study developed the PMVGS. First, several online gaming addiction scales with good reliability and validity were referred to, including the GAS (Lemmens et al. 2009), IGD-20 T (Pontes et al. 2014), IGDS (Lemmens et al. 2015), and IGDT-10 (Király et al. 2017; Pontes and Griffiths 2016), from which 29 items corresponding to the six dimensions of the addiction model framework (salience, tolerance, withdrawal symptoms, relapse, mood modification, and conflict) were selected. Since the use of mobile video games overlap somewhat with smartphone use, the Smartphone Addiction Scale (SAS) and MPAI scale were used as references (Leung 2008; Min et al. 2013), and two items were selected and modified (“There is nothing more fun to do than playing mobile video games” and “I get irritated when bothered while playing mobile video games”). Then, according to the mobile video games’ characteristics, an interview outline was created, and 10 college students from Southwest University who were thought to play games on mobile devices frequently were selected for the semi-structured interviews. Four items about the characteristics of problematic mobile video gaming were compiled based on the interview results (“It’s great for you to meet different friends by playing mobile video games,” “If you are not allowed to play mobile video games, you will choose to watch the live game,” “You will make full use of any spare time to play the mobile video games,” and “You often download the uninstalled mobile video games again”). Finally, experts in related fields and postgraduate students majoring in psychology were invited to review and revise the questionnaire, thus completing the PMVGS’s preliminary preparation. The initial PMVGS was composed of 35 items. The items were answered using a 5-point Likert scale: 1 (never), 2 (rarely), 3 (sometimes), 4 (often), and 5 (very often). The scores were obtained by summing the responses, with higher scores indicating severe problematic mobile video gaming.

Measures

Depression Scale

The depression subscale from the Brief Symptom Inventory (BSI; Derogatis and Melisaratos 1983) was used to assess depression symptoms. The scale contains six items, and each item is rated on a 5-point Likert scale, ranging from 1 (not at all) to 5 (extremely serious). Higher scores indicate more severe depression symptoms. The scale has been proved to have good reliability and validity (Boulet and Boss 1991), and the Cronbach’s alpha coefficient in Sample 1 was .84.

Children’s Loneliness Scale

The revised version of the Children’s Loneliness Scale was adopted to evaluate loneliness (Wang et al. 1999). The scale contains 16 items. The items are answered using a 5-point Likert scale ranging from 1 (always) to 5 (never); higher scores indicate higher levels of loneliness. The scale has been proved to have good reliability and validity (Asher et al. 1984), and the Cronbach’s alpha coefficient in Sample 1 was .86.

Child Social Anxiety Scale

The modified version of the Child Social Anxiety Scale was used to assess participants’ social anxiety (Wang et al. 1999). The term “children” in the original scale was changed to “classmate” for our purposes. The scale contains 10 items, and each item was rated using a 3-point Likert scale, ranging from 1 (never) to 3 (always). Higher scores indicate a higher level of social anxiety. The scale has been proved to have good reliability and validity (Storch et al. 2004), and the Cronbach’s alpha coefficient in Sample 1 was .80.

Mobile Video Game Usage Time

In line with previous studies, the participants were asked to recall the time spent using mobile video games during the past 12 months and to state the amount of gaming time on school days and holidays (Király et al. 2017). The answer options were as follow: 1 (less than half an hour), 2 (half an hour to one hour), 3 (one hour to two hours), 4 (two hours to three hours), 5 (three hours to six hours), and 6 (over six hours). The time spent on mobile video games during the school period and holidays was aggregated to form the final index.

The Mobile Phone Addiction Index (MPAI) Scale

The MPAI (Leung 2008) was used to evaluate the students’ mobile phone addiction tendency. The scale contains 17 items, and each item is answered using a 5-point Likert scale ranging from 1 (never) to 5 (always); higher scores indicate more severe mobile phone addiction. The scale has been proved to have good reliability and validity (Zhaojie et al. 2015), and the Cronbach’s alpha coefficient in Sample 1 was .92.

Excessive Online Gaming Scale

The Excessive Online Gaming Scale (Kardefelt-Winther 2014a, b) was used to assess online gaming addiction tendency. Each item was answered on a 5-point Likert scale ranging from 1 (not at all true) to 5 (very true); higher scores indicate a higher level of online gaming addiction. The scale has been proved to have good reliability and validity (Kardefelt-Winther 2014a, b), and the Cronbach’s alpha coefficient in Sample 1 was .80.

Data Management and Statistical Analyses

Data management involved (1) cleaning the data set by inspecting cases with missing values above the conventional threshold of 10% in all relevant scales; (2) checking for the univariate normality of all of the PMVGS items using standard guidelines (with the absolute value of skewness or kurtosis greater than 1; Kline 2015); and (3) screening for univariate outliers that scored ±3.29 standard deviations from the PMVGS z-scores (Field 2013). After data management, no participants were excluded; thus, the final data set of Sample 1 contained 578 valid cases and Sample 2 contained 1501 valid cases that were eligible for the subsequent analyses.

The statistical analyses comprised the following steps. (1) Item analysis of Sample 1 was conducted, including discriminant analysis and the homogeneity test. (2) Sample 1 was used for the EFA to define the factorial structure of the PMVGS. A CFA was performed to confirm the factorial structure obtained by the EFA of Sample 2. (3) The scale’s criterion-related validity was assessed by calculating the Pearson correlation coefficient between the PMVGS score and the depression, social anxiety, loneliness, and game usage time scores of Sample 1. (4) The scale’s convergent validity and discriminant validity were tested by calculating the Pearson correlation coefficient of the total score of the PMVGS, the total score of the Excessive Online Gaming Scale, and the total score of the MPAI scale in Sample 1. (5) The scale’s reliability was analyzed using Cronbach’s internal consistency coefficient and the Spearman–Brown odd-even split-half coefficient in Sample 1 and Sample 2. All statistical analyses were performed using SPSS 22.0 (IBM Corp., Armonk, NY, USA) and Amos 22.0 (IBM Corp., Armonk, NY, USA).

Results

Item Analysis

Using the data of Sample 1 (N = 578), discriminant analysis and the homogeneity test were conducted for item analysis (see Table 1 for the results).
Table 1

Discriminant Analysis and Homogeneity Test Results (N = 578)

Item

t

r

1

−14.52***

0.62***

2

−15.55***

0.61***

3

−10.27***

0.42***

4

−22.51***

0.68***

5

−16.94***

0.60***

6

−19.13***

0.69***

7

−12.42***

0.45***

8

−14.64***

0.52***

9

−18.65***

0.65***

10

−21.13***

0.74***

11

−22.77***

0.71***

12

−10.86***

0.43***

13

−13.07***

0.47***

14

−22.49***

0.68***

15

−18.29***

0.62***

16

−13.46***

0.57***

17

−19.99***

0.72***

18

−15.29***

0.58***

19

−19.10***

0.64***

20

−18.57***

0.66***

21

−13.20***

0.53***

22

−12.57***

0.62***

23

−22.58***

0.71***

24

−18.89***

0.70***

25

−22.73***

0.72***

26

−20.27***

0.73***

27

−15.83***

0.58***

28

−22.96***

0.73***

29

−14.67***

0.61***

30

−14.95***

0.59***

31

−16.53***

0.69***

32

−23.83***

0.73***

33

−21.29***

0.73***

34

−19.74***

0.71***

35

−17.39***

0.66***

*p < .05. **p < .01. ***p < .001

Discriminant Analysis

The total scores of the scale were sorted in ascending order, and then the participants were divided into a low score group (≤ 52) and high score group (≥ 82), with a score in the top and bottom 27% used as the boundaries. An independent samples t-test was conducted to examine the score difference between the two groups for each item. The results revealed significant differences for all 35 items (p < .001), indicating that all items have good discrimination.

Homogeneity Test

Significant correlations between the scale’s items and the total score indicate the high homogeneity of the scale (Wu 2010). The Pearson correlation coefficients between each item and the total score were calculated. The results showed that the correlation coefficients ranged from .42 to .73 (p < .001), indicating that all items in the PMVGS could measure the characteristics of problematic mobile video gaming.

Validity Analysis

Content Validity

An expert group composed of two psychology professors in related fields and four postgraduate students in psychology discussed and evaluated the preliminary version of the PMVGS. The expert group reached a consensus that the 35 items contained in this scale could measure the content of problematic mobile video gaming, indicating that the scale has good content validity.

Construct Validity

For the EFA, as a first step, the Kaiser-Meyer-Olkin (KMO) coefficient and Bartlett’s test of sphericity were performed with Sample 1 to determine whether the PMVGS is appropriate for principal component analysis. As a result of the analysis, it was found that the dataset was suitable for dimensional structure analysis (KMO = 0.91, χ2 = 10,943.791, p < .001; Hair et al. 2010; Pallant and Manual 2001). Therefore, an EFA using principal component analysis with varimax (orthogonal) rotation of the 35 items of the PMVGS was performed in Sample 1 (N = 578) to examine its factorial structure. The analysis revealed five factors that explained 57.23% of the total variance of the construct. According to the criteria used in previous research studies for removing items, 24 items that loaded on more than one factor and had less than a 0.20 factor load difference between factor loads or had less than a 0.40 factor load were removed from the scale (i.e., items 32, 33, 24, 30, 25, 23, 21, 15, 5, 18, 16, 10, 35, 29, 27, 11, 1, 14, 2, 4, 20, 29, 22, and 26; Yılmaz et al. 2017). The final scale retained three factors that included 11 items, which cumulatively explained 66.22% of the total variance. The three factors were withdrawal symptoms (withdrawal symptoms when mobile video gaming is taken away), mood modification (use of mobile video games to escape or relieve a negative mood), and conflict (continued excessive use of mobile video games despite the knowledge that overuse of mobile video games can produce a series of negative effects). The factor loadings of each item are shown in Table 2.
Table 2

Summary of the Exploratory Factor Analysis Results Obtained from the Problematic Mobile Video Gaming Scale (N = 578)

Factor name

Item number

Item

Factor loadings

Withdrawal symptoms

6

During the last year, have you felt tense or restless when you were unable to play mobile video games or played less than usual?

0.774

17

During the last year, have you felt miserable when you were unable to play mobile video games or played less than usual?

0.834

31

During the last year, have you felt angry or frustrated when you were unable to play mobile video games or played less than usual?

0.829

34

During the last year, have you felt stressed when you were unable to play mobile video games or played less than usual?

0.808

Mood modification

3

During the last year, have you played mobile video games to release stress?

0.756

7

During the last year, have you played mobile video games to feel better?

0.797

12

During the last year, have you played mobile video games to relieve a negative mood (e.g., helplessness, guilt, or anxiety)?

0.837

13

During the last year, have you played mobile video games so that you would not have to think about annoying things?

0.727

Conflict

8

During the last year, have you ever jeopardized your school or work performance because of playing mobile video games?

0.756

9

During the last year, have you played mobile video games throughout the night or almost the whole night?

0.704

19

During the last year, have you been spending less time with friends, your partner, or family in order to play mobile video games?

0.720

All factor loadings were statistically significant (i.e., p < .0001)

The three-factor model (withdrawal symptoms, mood modification, and conflict) obtained from the EFA of Sample 1 was used as the preliminary statistical model. To confirm the three-factor structure, a CFA using the maximum likelihood (ML) estimation method in Amos 22.0 was performed on the 11 items of the PMVGS using Sample 2 (N = 1501), and no residual errors were correlated between items. The goodness-of-fit indices were χ2/df = 6.924, root mean square error of approximation (RMSEA) = 0.063, 90% confidence interval (CI) [0.056, 0.070], standardized root mean square residual (SRMR) = 0.031, comparative fit index (CFI) = 0.974, and Tucker-Lewis fit index (TLI) = 0.963 (see Table 3), indicating that the three-factor model obtained by the EFA of Sample 1 was well fitted in Sample 2. In Sample 2, the correlations between each factor and the total score of the PMVGS were further investigated, and the results are shown in Table 4. The results showed that there was a strong correlation between each factor and the total score of the scale, with correlation coefficients of 0.886, 0.837, and 0.856 (p < .01), indicating that the three factors had a strong contribution to the total score of the scale. In addition, there was a modest to strong correlation between these three factors, with the correlation coefficients of 0.535, 0.554, and 0.749 (p < .01). Therefore, this study establishes a second-order model that uses a higher-order factor to explain the strong correlation between the three factors (Wang 2014).
Table 3

Goodness-of-Fit Indices for the Confirmatory Factor Analysis of the Problematic Mobile Video Gaming Scale (N = 1501)

 

χ2

df

p

χ2/df

RMSEA

90% CI

SRMR

CFI

TLI

First-order model

283.884

41

< .001

6.924

0.063

[0.056, 0.070]

0.031

0.974

0.965

Second-order model

283.884

41

< .001

6.924

0.063

[0.056, 0.070]

0.031

0.974

0.965

χ2 Satorra–Bentler chi-square; df degrees of freedom; p general model significance; χ2/df normed chi-square; RMSEA root mean square error of approximation; SRMR standardized root mean square residual; 90% CI 90% confidence interval; CFI comparative fit index; TLI Tucker-Lewis fit index

Table 4

Correlations among the Factors and the Total Score of the Problematic Mobile Video Gaming Scale (N = 1501)

 

PMVGS

Withdrawal symptoms

Mood modification

Conflict

PMVGS

1

0.886**

0.837**

0.856**

Withdrawal symptoms

0.886**

1

0.554**

0.749**

Mood modification

0.837**

0.554**

1

0.535**

Conflict

0.856**

0.749**

0.535**

1

PMVGS the total score of the Problematic Mobile Video Gaming Scale

*p < .05. **p < .01. ***p < .001

To confirm the second-order structure, a CFA using the ML estimation method was also performed on the PMVGS using Sample 2. The goodness-of-fit indices were χ2/df = 6.924, RMSEA = 0.063, 90% CI [0.056, 0.070], SRMR = 0.031, CFI = 0.974, and TLI = 0.963 (see Table 3), indicating that the second-order model was also well fitted in Sample 2. The results show that the first-order model obtained from the EFA and second-order model of the PMVGS are equivalent. Therefore, in the case where the model fitting index is the same, and considering that problematic mobile video gaming and traditional online gaming addiction are impulse control obstacles based on the use of different terminals and with different manifestations, most of the online gaming addiction scales adopted the second-order model to complete the construction of the scale (Lemmens et al. 2009; Lemmens et al. 2015; Pontes et al. 2014). This study tends to support the second-order model (see Fig. 1). This implies that the PMVGS can be characterized by the confirmed three dimensions, while an overall problematic mobile video gaming factor captures a meaning common to all dimensions.
Fig. 1

Graphical summary of the second-order model obtained from the confirmatory factor analysis results (N = 1501). Factor loadings are standardized scores. PMVGS = the Problematic Mobile Video Gaming Scale

In conclusion, the results indicate that the second-order three-factor model of the PMVGS based on the EFA results of Sample 1 and previous relevant studies and theories was well fitted in Sample 2. This indicates that the second-order model was stable between the two independent samples and confirms the generalizability of the scale, thus proving that the PMVGS has good construct validity.

Criterion-Related Validity

In this study, we used depressive symptoms, loneliness, social anxiety, and game usage time as the external criteria to evaluate the validity of the PMVGS. To prove that the PMVGS has good criterion-related validity, the PMVGS scores of the participants should be positively correlated with depression, loneliness, social anxiety, and mobile video game usage time. The results showed significant correlations between the total score of the PMVGS and the level of depression, loneliness, social anxiety, and game usage time, with the correlation coefficients of 0.31, 0.21, 0.25, and 0.34, respectively (p < .01; see Table 5). These results indicate that the PMVGS has fair criterion-related validity.
Table 5

Correlations between the Problematic Mobile Video Gaming Scale and the Measures of the Criterion Validity (N = 578)

 

Depression

Loneliness

Social anxiety

Mobile video game usage time

PMVGS

0.31**

0.21**

0.25**

0.34**

PMVGS the total score of the Problematic Mobile Video Gaming Scale

*p < .05. **p < .01. ***p < .001

Convergent Validity and Discriminant Validity

The Pearson correlation coefficients between the PMVGS, Excessive Online Gaming Scale, and MPAI scale were calculated. The results showed strong correlations between the PMVGS and Excessive Online Gaming Scale, which have similar measurement structures (r = 0.65, p < .01), while there was a modest correlation between the PMVGS and the MPAI, which have different measurement structures (r = 0.52, p < .01). These results suggest that the PMVGS has good convergent validity and discriminant validity.

Reliability Analysis

The internal consistency coefficient of the PMVGS was computed. In Sample 1, the results showed that the Cronbach’s alpha coefficient was .84 for the total scale and the internal consistency coefficients of the three dimensions were .87, .81, and .63. In Sample 2, the results showed that the Cronbach’s alpha coefficient was .91 for the total scale and the internal consistency coefficients of the three dimensions were .90, 0.84, and 0.76 (see Table 6). The internal consistency coefficient of the PMVGS and the three dimensions reached acceptable levels in both samples. We also calculated the odd-even split-half reliability, and the results showed that the Spearman-Brown odd-even split-half coefficients of the scale were .79 and .90.
Table 6

The Internal Consistency Coefficients of the Problematic Mobile Video Gaming Scale

 

PMVGS

Withdrawal symptoms

Mood modification

Conflict

Sample 1 (N = 578)

0.84

0.87

0.81

0.63

Sample 2 (N = 1501)

0.91

0.90

0.84

0.76

PMVGS the total score of the Problematic Mobile Video Gaming Scale

Discussion

Based on previous studies of traditional desktop computer online gaming addiction as well as the distinctions between problematic mobile video gaming and traditional forms of online gaming addiction, this study developed the PMVGS. Sample 1 (578 junior high school students) and Sample 2 (1501 undergraduates) were used to verify the scale’s psychometric properties to develop an effective and reliable measurement tool to promote research into problematic mobile video gaming. Therefore, this study evaluated the reliability and validity of the preliminary PMVGS and found that the developed PMVGS has fair reliability and validity, meeting psychometric standards.

First, we developed the PMVGS based on a review of the literature and the distinctive characteristics of problematic mobile video gaming. According to the six dimensions of the addiction model framework (salience, tolerance, withdrawal symptoms, relapse, mood modification, and conflict), several online gaming addiction scales with good reliability and validity were referred to, from which 29 items were selected. Meanwhile, six items were compiled based on the SAS and MPAI scale and the semi-structured interview results of students who play mobile video games frequently. Thus, the initial version of PMVGS containing 35 items was formed. Moreover, item analysis showed that all of the scale’s items have good discrimination and can measure the characteristics of problematic mobile video gaming. Therefore, there was no need to delete items to complete the preliminary screening of the items.

Second, the PMVGS has good validity. We examined the scale’s content validity, construct validity, criterion-related validity, convergent validity, and discriminant validity. The expert group agreed that the 35 items contained in this scale could measure the content of problematic mobile video gaming, indicating that the scale has good content validity. Sample 1 was used to perform the EFA to define the factorial structure of the PMVGS. As a result of the EFA, after deleting 24 items, a three-factor structure (withdrawal symptoms, mood modification, and conflict) composed of 11 items was obtained, which explained 66.25% of the total variance. To explain the higher level of correlation between the three factors, a second-order model was established. Sample 2 was used to confirm the second-order three-factor model of the PMVGS. The obtained fit indices from the CFA confirmed the second-order three-factor structure, indicating that the second-order model was stable between the two independent samples, and confirming the generalizability of the scale. Specifically, the PMVGS’s 11 items can be divided into three different dimensions, and they can measure the content related to problematic mobile video gaming. Furthermore, the high-order factor extracted by these three dimensions can be used to illustrate the degree of problematic mobile video gaming. These results indicate that the PMVGS has good construct validity. In addition, a significant positive correlation was found between problematic mobile video gaming and the level of depression, loneliness, social anxiety, and game usage time, which is consistent with previous findings of online gaming addiction (Lemmens et al. 2015; Stetina et al. 2011; Wei et al. 2012), which indicates that the PMVGS has fair criterion-related validity. This study also found a strong correlation between the total scores of the PMVGS and the Excessive Online Gaming Scale, which have similar measurement structures, while there was only a moderate correlation between the total scores of the PMVGS and the MPAI scale, which have different measurement structures; this suggests that the PMVGS has good convergent validity and discriminant validity.

Finally, the results showed that the PMVGS has fair reliability. In Sample 1 and Sample 2, the three dimensions’ internal consistency coefficients were all above .63 and 0.76 respectively, which is ideal considering that the number of items in each dimension was three or four, and the total scale’s internal consistency coefficient in the two samples was .84 and .91, respectively; this supports the scale’s full internal consistency. Moreover, the Spearman–Brown odd-even split-half coefficients in the two samples were .79 and .90. All of the reliability indexes were fair and met the psychometric requirements.

Limitations and Future Directions

Although the findings concerning the PMVGS’s psychometric properties were strong overall, there are some potential limitations worth noting. First, the data were all self-reported and prone to various biases (e.g., memory recall biases, social desirability, etc.). Second, as the cut-off score for diagnosis was not calculated in this study, it was impossible to screen individuals with problematic mobile video gaming. Third, this study did not test the predictive nature of the PMVGS, that is whether it can effectively distinguish problematic mobile video gaming users from normal users.

Despite these limitations, the present study involved development of a concise scale with good validity and reliability, which paves the way for new research into problematic mobile video gaming. In future studies, the cut-off score for diagnosis should be determined. Furthermore, researchers should conduct a study with a larger nationally representative sample to advance reliable estimates of the prevalence rates of problematic mobile video gaming in China.

Notes

Authors’ Contributions

Jia-Rong Sheng: study concept and design, analysis and interpretation of data, statistical analysis.

Jin-Liang Wang: obtained funding, study supervision.

Funding

This work was supported by the Key Cultivating Project in Southwest University [grant number is SWU1809006].

Compliance with Ethical Standards

Conflict of Interest

The authors report no financial or other relationship relevant to the subject of this article.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. The Institutional Review Board of the Southwest University approved the study. All subjects were informed about the study and parental consent was sought for those younger than 18 years of age.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Center for Mental Health Education, School of PsychologySouthwest UniversityChongqingChina

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