Fit for the one-factor model CFA
The fit values from the CFA for the one-factor model for the IGDS9-SF were as follows: MLR
χ2 (
df = 27) = 138.08,
p < .001; RMSEA = .075 (90% CI = .063–.087); CFI = .951, and TLI = 0.945. Based on Hu and Bentler’s [
14] guidelines, the CFI and TLI values can be interpreted as showing good fit, and RMSEA value as showing moderate fit for the unidimensional IGD structure. Additionally, all symptoms in the model loaded significantly (
p < .001) and saliently (ranging from .483 to .920 on the IGD factor). Table
1 shows the standardized path coefficients for the predictions of the external variables (i. e. gender distribution, age, hours spent on preferred videogame, anxiety, depression, stress, ADHD-IA, and ADHD-HI) by the IGD factor. As shown, except for age and gender, the standardized path coefficients for all other variables were significant and positive. These associations were as theoretically expected. Therefore, the one-factor CFA model for IGD had substantive meaning.
Table 1
Standardized coefficients of the predictors of the background and psychopathology variables by the IGD factor in the CFA and FMMA models
Gender | −0.054 | .040 | −0.087 |
Age | −0.063 | −.039 | −0.162* |
Hours/week on preferred games | 0.250*** | 0.191*** | 0.352*** |
Inattention | 0.490*** | 0.429*** | 0.466*** |
Hyperactivity/impulsivity | 0.460*** | 0.408*** | 0.520*** |
Stress | 0.441*** | 0.355*** | 0.538*** |
Anxiety | 0.384*** | 0.301*** | 0.456*** |
Depression | 0.449*** | 0.360*** | 0.509*** |
LCA models
Table
2 summarizes the BIC, LMRT, the bootstrapped LRT indexes for the LCA and the FMMA models assessed in the present study. Considering the LCAs, one-to-four profile models were evaluated. The four-class LCA model had the lowest BIC values. However, its
p-value for LMRT was significant, thereby suggesting that a class with one less model (three-class LCA model) would be preferable. The model with the second lowest BIC value was the three-class LCA model, and the
p-value for the LMRT for this model was not significant. Given these findings, the three-class LCA model was tentatively considered as the best LCA model. To evaluate further the substantive value of this model, the IGD symptom profiles of the classes in this model were examined. The findings indicated a class with high endorsement on most of the IGD symptoms, a class with low endorsement on most of the IGD symptoms, and an intermediate class with symptom profiles between the high and the low endorsement classes. As noted earlier, DSM-5 suggests IGD should be specified in terms of mild, moderate, and severe presentation types according to the degree of the IGD symptoms present [
1]. These types can be reinterpreted in the present study as low, intermediate, and high classes, respectively.
Table 2
CFA, LCA and FMMA: model comparisons and fit indices
Confirmatory factor analysis |
One-factor | − 8789 | 27 | 17,757 | | |
Latent class analysis |
One-class | −10,067 | 18 | 20,254 | | |
Two-class | − 9175 | 28 | 18,536 | 0.0000 | 0.903 |
Three-class | − 8795 | 38 | 17,842 | 0.0482 | 0.953 |
Four-class | − 8242 | 48 | 16,801 | 0. 2489 | 1.000 |
Factor mixture model |
Two-class/one-factor |
FMMA-1 | −9175 | 28 | 18,536 | 0.0000 | 0.803 |
FMMA-2 | − 8750 | 30 | 17,698 | 0.0104 | 0.636 |
FMMA-3 | − 8533 | 38 | 17,317 | 0.0000 | 0.984 |
FMMA-4 | − 8509 | 48 | 17,336 | 0.0000 | 0.977 |
Three-class/one-factor |
FMMA-1 | − 8809 | 31 | 17,824 | 0.0000 | 0.951 |
FMMA-2 | − 8719 | 33 | 17,657 | 0. 1422 | 0.983 |
FMMA-3 | − 8481 | 49 | 17,286 | 0. 2349 | 0.964 |
FMMA-4 | − 8400 | 68 | 17,249 | 0. 1223 | 0.948 |
There were 226 (60.4%), 126 (23.4%) and 23 (6.1%) males in the low, intermediate, and high endorsement classes, respectively; and there were 219 (60.2%), 110 (30.2%) and 35 (9.6%) females in the low, intermediate, and high endorsement classes, respectively. The results of the chi-square test indicated no significant difference[χ
2 (df = 2) =3.41,
p = 0.181] regarding the distribution of the two genders across the classes. Table
3 shows the results of the comparisons across the three classes for age, hours spent each week on preferred videogame, anxiety, depression, stress, ADHD-IA, and ADHD-HI. As shown, except for age, the high endorsement class scored significantly higher than the intermediate endorsement, which in turn scored higher than the low endorsement class. The differences were as theoretically expected.
Table 3
Comparisons of the mean of the low (N = 626) and high (N = 112) classes for all continuous predictors
Age | 25.43 (7.78) | 25.11 (7.76) | 23.78 (6.15) | 1.20 | H = I = L | .003 |
Hours/week on preferred games | 3.25 (3.10) | 3.99 (3.25) | 5.62 (4.12) | 15.41*** | H > I > L | .040 |
Inattention | 12.82 (5.99) | 16.26 (5.21) | 12.62 (5.89) | 72.78*** | H > I > L | .165 |
Hyperactivity/ impulsivity | 12.58 (5.56) | 16.04 (5.56) | 20.24 (6.40) | 64.41*** | H > I > L | .149 |
Stress | 5.71 (3.93) | 7.89 (4.11) | 12.22 (4.76) | 77.26*** | H > I > L | .174 |
Anxiety | 4.41 (3.80) | 6.179 (4.31) | 9.40 (4.85) | 45.52*** | H > I > L | .110 |
Depression | 5.83 (5.07) | 8.92 (5.44) | 13.40 (5.03) | 69.03*** | H > I > L | .158 |
Overall, the findings showed the three-class LCA model had substantive meaning. Consequently, this model was considered the optimum LCA model. For the model, the entropy (an index of membership classification clarity-accuracy) was high (.953), as were the posterior probabilities for the accurate classification across the three classes (.964, .979, and .986 for the high, intermediate, and low classes, respectively). The proportions of individuals in the high, intermediate, and low endorsement profiles were 7.9, 31.8, and 60.3%, respectively.
FMMA models
Following the results of the CFA and LCA, for all FMMA models, one-to-three class solutions for the unidimensional IGD structure were examined. Therefore, for the two-class/one-factor, and three-class/one-factor model, the four versions of FMMA were assessed (i.e., FMMA-1, FMMA-2, FMMA-3, and FMMA-4). The one-class model would be equal to the one-factor structure.
Table
2 presents the results of all the FMMA models. As shown, the three-class/one factor FMMA-4 model had the lowest BIC value. However, the
p-value for its LMRT value was not significant, thereby suggesting that a model with one class less could be a better model. The model with the second lowest BIC value was the three-class/one factor FMMA-3 model. The
p-value for the LMRT value for this model was also not significant, thereby suggesting that this was not a suitable model. The model with the next lowest BIC value was the two-class/one-factor FMMA-3 model. The
p-value for the LMR T
value of this model was significant. Therefore, from a statistical viewpoint, the two-class/one-factor FMMA-3 model was considered as the best fitting FMMA model. To evaluate further the substantive value of this model, the IGD symptom profiles of the two classes in this model were examined.
Table
4 shows the mean scores (and standard errors) and intercepts scores (and standard errors) for all IGD-9 symptoms in the two classes in the FMMA-3 variant of the two-class/one-factor model. It also includes the unstandardized factor loadings for the symptoms in this model. Because these loadings were invariant across classes, these loadings were applicable to both classes. Supplementary Figure
S1 depicts the mean values (with their 95% confidence intervals) for each symptom in the two classes of this model. As can be seen in Table
3 and Figure
S1, all nine symptoms were higher in one class (Class A) than the other class (Class B). For classes B and A, the mean scores of the symptoms ranged from 1.20 to 3.33, and 2.52 to 3.80, respectively. The overall mean (SD) for the B and A classes were 2.17 (0.65) and 3.28 (0.42), respectively. The intercepts values for the symptoms for the B and A classes ranged from 1.57 to 3.08, and 2.32 to 7.05, respectively. The overall mean (SD) for the B and A classes were 2.25 (0.525) and 3.28 (0.42), respectively. Therefore, the two classes in the FMMA-3 variant of the two-class/one-factor model can be viewed in terms of high (Class A) and low (Class B) levels of endorsements of IGD symptoms.
Table 4
IGDS9-SF item mean and intercept values for the FMMA3-Class one-factor model and factor loadings for all classes
Brief item description | X | SE | β | SE | X | SE | β | SE | ƛ | SE |
Preoccupation (1) | 2.89 | 0.05 | 2.66 | 0.07 | 3.77 | 0.09 | 3.09 | 0.13 | 1.00 | 0.00 |
Negative emotions (2) | 2.11 | 0.05 | 1.85 | 0.04 | 2.96 | 0.13 | 2.22 | 0.11 | 1.26 | 0.09 |
Increasing time (3) | 2.34 | 0.05 | 2.10 | 0.05 | 3.26 | 0.12 | 2.47 | 0.12 | 1.28 | 0.08 |
Lacking control (4) | 1.91 | 0.04 | 1.95 | 0.04 | 2.99 | 0.12 | 2.65 | 0.14 | 1.02 | 0.07 |
Giving up activities (5) | 2.23 | 0.03 | 3.08 | 0.09 | 3.21 | 0.09 | 3.39 | 0.18 | 1.12 | 0.07 |
Continuation (6) | 2.00 | 0.04 | 1.80 | 0.04 | 3.39 | 0.12 | 2.70 | 0.14 | 1.08 | 0.08 |
Deception (7) | 1.52 | 0.04 | 1.57 | 0.03 | 2.52 | 0.13 | 2.36 | 0.13 | 0.84 | 0.08 |
Escape (8) | 3.33 | 0.05 | 2.76 | 0.08 | 3.80 | 0.11 | 2.88 | 0.13 | 0.96 | 0.09 |
Jeopardizing (9) | 1.20 | 0.02 | 2.51 | 0.06 | 3.62 | 0.07 | 7.05 | 0.26 | 0.32 | 0.04 |
Table
5 provides the findings of the comparisons, using
t-tests, across the two classes for age, hours spent on one’s preferred videogame, anxiety, depression, stress, ADHD-IA, and ADHD-HI. Prior to this, chi-square was used to examined difference gender distribution across the classes. There were 292 (46.6%) and 72 (64.2%) females in the low and high endorsement classes, respectively; and there were 334 (53.4%) and 40 (35.7%) males in the low and high endorsement classes, respectively. The results of the chi-square test indicated significant difference, (χ
2 = 11.83,
p < .01), with more females in the high endorsement class. The eta squared effect size for these differences was medium (η2 = .1) based on benchmarks for eta squared: small = 0.01, medium = 0.06, and large = 0.14 [
6].
Table 5
Comparisons of the mean of the low (N = 626) and high (N = 112) classes for all continuous predictors
Age | 25.26 (7.93) | 24.90 (6.45) | 0.45 | L = H | 0.46 |
Hours/week on preferred games | 3.51 (3.15) | 4.57 (3.95) | 3.16** | H > L | 0.32 |
Inattention | 13.76 (6.01) | 18.16 (5.91) | 7,15*** | H > L | 0.73 |
Hyperactivity/impulsivity | 13.78 (5.88) | 17.08 (6.50) | 5.34*** | H > L | 0.55 |
Stress | 6.45 (4.14) | 9.13 (4.76) | 6.17*** | H > L | 0.63 |
Anxiety | 4.98 4.08) | 7.49 (4.83) | 5.82*** | H > L | 0.60 |
Depression | 6.83 (5.45) | 10.68 (5.66) | 6.85*** | H > L | 0.70 |
Table
1 also includes the standardized path coefficients for the predictions of gender distribution, age, hours spent on preferred videogame, anxiety, depression, stress, ADHD-IA, and ADHD-HI, by the IGD factors in Classes 1 and 2. As shown, except for age (in Class 1) and gender, the standardized path coefficients for all other variables were significant and positive. The associations for both classes were as theoretically expected. Therefore, the CFA IGD factor in both classes had substantive meaning. Overall, the two-class/one factor FMMA-3 model had substantive meaning. Consequently, this model was considered the optimum FMMA model. Its entropy was high (0.984), while the posterior probabilities of individuals correctly clustered in high- and low-severity profiles were 0.99, and 0.999, respectively. The number of participants classified in the high- and low-severity profiles were 112 (15.18%) and 626 (84.828%), respectively.
For this model, the mean ratings for six of the nine IGD symptoms for the class with high levels of endorsements were above 3. The three symptoms that did not have as high level of endorsement were negative emotions (symptom 2), lacking control (symptom 4), and deception (symptom 7). The mean ratings for these symptoms were 2.96, 2.99, and 2.36, respectively. However, these mean scores indicate that the ratings for negative emotions and losing-lacking control were both very close to 3. With the exception of one symptom (symptom 8 relating to escape, which had a rating mean value of 3.33), the mean ratings for all the other eight symptoms in the low endorsement class were below 3. Therefore, overall (and based on a symptom cut-off score of > 3 for inferring the presence of a symptom [
33,
35];), the classes with high and low levels of endorsements can be viewed as classes that are affected and unaffected by IGD symptoms.