International Journal of Psychology, 2015 DOI: 10.1002/ijop.12231
Impulsivity as a precedent factor for problematic Internet use: How can we be sure? Ssu-Kuang Chen, Meng-Ting Lo, and Sunny S. J. Lin Institute of Education, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
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revious research has suggested that problematic Internet use (PIU) is associated with impulse control disorder. Although researchers have suggested that impulsivity is a risk factor for PIU, the literature lacks longitudinal evidence on the relationship between impulsivity and PIU. We aimed to use a cross-lagged analytic framework to identify temporal order effects and hypothesised that impulsivity was the precedent factor for PIU. In a panel sample of college students (N = 367), trait impulsivity and PIU were measured in the spring of freshman year and in their junior year. The measures included a self-developed PIU Scale and the revised Impulsiveness Scale based on Barratt’s concept. We found that “non-planning impulsivity” was not associated with PIU. The “motor impulsivity” subfactor was thus adopted in the cross-lagged model. The results suggest that motor impulsivity and PIU were stable across time. Motor impulsivity at Time 1 positively predicted PIU at Time 2, but PIU at Time 1 did not predict motor impulsivity at Time 2. A further investigation using gender as a moderator found a gender difference in the temporal relationship. Because motor impulsivity is a risk factor for PIU, potential prevention strategies based on this result are suggested. Keywords: Impulsivity; Problematic Internet use; Cross-lagged analysis; Gender difference.
Background Problematic Internet use (PIU) among adolescents is a growing phenomenon. Substantial evidence has shown that although the Internet is a convenient tool for work, entertainment and communication, spending excessive amounts of time on the Internet may result in “PIU” and may have negative impacts on an individual’s social functions and psychological or physical health (Aboujaoude, 2010). Many studies have suggested that PIU has become a serious problem that is similar to pathological gambling, which is conceptualised as an impulse control disorder (Shapira, Goldsmith, Keck, Khosla, & McElroy, 2000; Young, 1998). Researchers have also found that individuals’ impulsivity levels are positively associated with PIU (Cao, Su, Liu, & Gao, 2007; Park et al., 2010). A study that involved small samples of clinical patients reached the conclusion that impulsivity is a marker of PIU development (Lee et al., 2012). However, the research suggesting that impulsivity is a risk factor for PIU has primarily relied on cross-sectional designs (Cao et al., 2007; Lee et al., 2012), a method that cannot provide strong evidence to determine the temporal ordering effect between
two variables (e.g., A is precedent to B). Information on the temporal order of the relationship between PIU and its risk factors is needed before PIU prevention strategies can be planned and studied, but researchers have thus far neglected the importance of temporal order. To fill this gap in the literature, the primary objective of this study was to examine the abovementioned temporal order effects. Longitudinal data across 24-month intervals were collected and examined using cross-lagged analyses. Although impulsivity is a stable characteristic, the relationship between impulsivity and PIU is not necessarily constant across time. Therefore, the stability of impulsivity and PIU, their temporal order and their synchronous relationship were analysed simultaneously. This study also considered gender1 as a critical factor in the relationship between impulsivity and PIU. Hence, after the temporal order effects were confirmed, the moderating effect of gender was investigated. Problematic Internet use According to Shapira et al., (2000), the concept of PIU describes an individual’s inability to control the impulse
Correspondence should be addressed to: Sunny S. J. Lin, Institute of Education National Chiao Tung University, Taiwan: 1001 Ta-Hsueh Rd, Hsinchu 300, Taiwan, R.O.C. (E-mail: [email protected]). This research was funded by Ministry of Science and Technology (NSC 97-2631-S-009-001, NSC-100-2631-S-009-001), Taiwan (R. O. C.). 1 We use gender, instead of sex, to prevent confusion between sex and sexual behaviour.
© 2015 International Union of Psychological Science
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and growing tension to use the Internet, which ultimately leads to feelings of distress or discomfort and negative life outcomes. In a study of undergraduates, students who used the Internet at excessively high levels reported high levels of loneliness (Engelberg & Sjöberg, 2004). A large-scale survey in Taiwan found that college students who spent more time on the Internet tended to have problems with identity and intimacy (Huang, 2006). Additionally, a study that explored the relationship between PIU and attachment found that insecure attachment attitudes are related to the development of PIU among older adolescents (Schimmenti, Passanisi, Alessia, Manzella, & Famà, 2014). With regard to the harmful results of PIU, a study of adolescents reported that high levels of PIU might result in adverse consequences and changes in lifestyle, such as physical inactivity (Yen et al., 2010). Moreover, compared with non-addicts, the Korean Internet addicts in Kim and Chun’s (2005) study reported less sleep, poorer diet and less regular exercise. Although many studies have provided evidence of the psychological harm that can result from excessive Internet use, this evidence was based on different PIU assessments that lack consensus on the definition of PIU. Thus, further research is needed (Aboujaoude, 2010). Impulsivity Psychologists have conceptualised impulsivity as a personal trait with various definitions and dimensions (Eysenck & Eysenck, 1977; Dickman, 1990). In particular, Barratt (1985) defined impulsivity as a three-dimensional concept that includes motor, cognitive and non-planning dimensions (Patton, Stanford, & Barratt, 1995). Motor impulsivity was defined as lacking thorough consideration before acting; cognitive impulsivity involved rapid decision making; and non-planning was defined as behaviours that show concern only for the present and a lacking of planning for the future. Although impulsivity is considered to be stable, longitudinal evidence has suggested a possible developmental shift. For example, Collado, Felton, Macpherson, and Lejuez (2014) found a curvilinear trend of impulsivity among middle adolescents. Other evidence has shown a linear decrease in impulsivity from childhood into adulthood (Steinberg, 2010). Moreover, neuroimaging findings have revealed that decreases in impulsivity during adolescence may be associated with the maturation of brain regions that are associated with cognitive control (Eppinger, Nystrom, & Cohen, 2012). PIU and impulsivity In a study that examined the relationship between Internet addiction and trait impulsivity, Lee et al. (2012) found that Internet addiction and pathological gambling patients
had higher scores on an impulsivity scale than healthy controls. Some research has shown that Internet addicts are more impulsive than non-addicts and that impulsivity is positively correlated with Internet addiction (Cao et al., 2007; Park et al., 2010). In a study of Dutch Internet users with an average age of 40 years, Meerkerk, van den Eijnden, Franken, and Garretsen (2010) found that rash, spontaneous impulsiveness (performing actions without considering the consequences) predicted PIU. Moreover, in a neuroscience study, researchers (Park et al., 2010) found that the 11 excessive Internet users in a sample of 20 males had abnormal glucose metabolism in their orbitofrontal cortex and other brain regions, most of which were associated with impulse control. All these results support the hypothesis that PIU is associated with impulsivity. With respect to online game addiction, Billieux et al. (2015) found that among different types of online gamers classified based on game motivation, trait impulsivity and self-esteem indicators, the gamers with problematic use had relatively higher levels of impulsivity. Gender difference To the best of our knowledge, no previous research has attempted to investigate the moderating effect of gender on the relationship between impulsivity and PIU. However, the literature on gender differences in the relationship between impulsivity and health-risk behaviours (Stoltenberg, Batien, & Birgenheir, 2008) may provide insights for this study. According to the literature, the question of whether there are gender differences in the associations between impulsivity and certain health-risk behaviours is inconclusive (Stoltenberg et al., 2008). For example, impulsivity is associated with alcohol use in both men and women, but impulsivity is associated with nicotine use only in women. Additionally, impulsivity is related to smoking in women but not in men. The present study The aforementioned literature does not address or explain whether impulsivity influences PIU. Lin, Ko, and Wu (2011) noted that further studies should investigate whether impulsivity is a risk factor for or a result of PIU. Luijten, Meerkerk, Franken, van de Wetering, and Schoenmakers (2015) suggested that researchers should not exclude the possibility that any prolonged excessive and compulsive behaviour may change neural circuits; thus, longitudinal studies on temporal relationships are valuable. Hence, this study aims to provide evidence for the claim that impulsivity is a precedent factor for PIU using a proper study design and data analysis techniques. Because gender is an important factor in understanding the nature of the impulsivity–PIU relationship, we also © 2015 International Union of Psychological Science
IMPULSIVITY AS A PRECEDENT FACTOR Time 1
Time 2 e1
IMP1
c
IMP2 f b
a e d PIU 2
PIU 1
e2
Figure 1. The cross-lagged model of PIU.
tested the moderating effect of gender and assumed that the impulsivity–PIU relationship is different between females and males. This study adopted a cross-lagged model of impulsivity and PIU to investigate the temporal order effect. Cross-lagged analysis is a method that examines the covariation between variables that are measured at two time points and identifies the temporal order of the variables (Martin & Liem, 2010). In this study, we assumed that impulsivity would predict subsequent PIU across time. Therefore, data on impulsivity and PIU were repeatedly collected at two time points in a sample of university students. As shown in Figure 1, IMP1 is the measure of impulsivity at Time 1, PIU1 represents the PIU level at Time 1, and IMP2 and PIU2 are the measures at Time 2. The effects of “a” and “b” represent the synchronous correlations in which “a” examines the relationships between impulsivity and PIU at Time 1 and “b” is the correlation of measurement error for the two latent variables at Time 2. The effects of “c” and “d” represent auto-regressive path coefficients that examine the variables’ test-retest reliability over time. The path coefficients “e” and “f” represent the cross-lagged relationships between the latent variables that examine the relative salience of impulsivity in predicting subsequent PIU compared with the relative salience of PIU in predicting subsequent impulsivity. A significant coefficient estimate of “e” and “f” suggests that the cross-lagged effect is sufficiently strong to claim the existence of a temporal order after controlling for the synchronous correlations and auto-regressive relationships that usually primarily account for the variance in Time 2 latent variables. METHOD Participants and procedure The research was part of a longitudinal study of the well-being of college students in Taiwan that is financially supported by the Ministry of Science and Technology of Taiwan. A total of 430 freshmen were recruited © 2015 International Union of Psychological Science
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through instructors from several different departments in a college. The students voluntarily participated in the first survey in April 2012 (Time 1) and in the second survey after 24 months (Time 2). The final sample comprised 367 college students (59% males) who completed both surveys (85% retention rate). The average age of the participants was 19 years (SD = .55) at Time 1. The survey instrument collected data on demographics (age, gender and major), impulsivity, PIU and the other variables that were not used in this study (e.g., Internet use habits and PIU-related issues such as lifestyle change and severe consequences). The questionnaires were distributed to all the participants in a class and were returned immediately after the group survey. All questionnaires were valid with only a few missing responses. Informed consent was obtained for all participants prior to their participation in the study. After each survey, all participants received a small gift as compensation (e.g., stationery valued at $6). Measures Revised impulsivity scale We used the two-factor version of an impulsivity scale (Lo, 2013) that was revised from the 31-item scale of Li, Ko, Weng, Liau, and Lu (2002), which was designed based on Barratt’s impulsivity concept. The items are rated using a 4-point scale, with higher scores indicating greater impulsivity. In study by Lo (2013), items were revised to correspond to the characteristics of college students. A principal axis factor (PAF) analysis with varimax rotation of the 31-revised items from Li et al.’s impulsivity scale was performed for 526 college students. The number of extracted factors was determined by the standard of eigenvalue with values greater than or equal to one; items were retained if their factor loadings were greater than or equal to .40. The two-factor version measures motor impulsivity (10 items) and non-planning (8 items). Motor impulsiveness concerns the extent to which individuals do not thoroughly consider an action, and an example item is “I say things without thinking.” Non-planning impulsiveness concerns the extent to which individuals care about the present and do not plan for the future, and an example item is “I am more interested in the present than the future.” In this study, confirmatory factor analysis (CFA) results supported single-factor models of motor impulsiveness (eight items) and non-planning (seven items). We examined the single-factor model because it would be used in the subsequent cross-lagged analysis. Detailed CFA results are presented in the results section. PIU scale In the study, we adopted the self-developed PIU scale for college students that includes five self-reported items
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regarding five core behavioural symptoms (i.e., tolerance, withdrawal, impulsivity, preoccupation and craving). The item regarding tolerance states as follows: “I need to use the Internet for increasing amounts of time to achieve satisfaction.” Withdrawal is measured by the following item: “When I tried to stop using the Internet, I was anxious.” The item involving impulsivity states as follows: “When I used the Internet, I felt excited and satisfied.” The item measuring preoccupation states as follows: “I would spend a tremendous amount of time arranging to use the Internet.” Craving is rated based on the following statement: “I rush to the Internet whenever I have a chance.” Responses to each question were provided on a 5-point Likert scale. In the PAF analysis, one factor was extracted, and 57.44% of the total variance was explained. All factor loadings were above .69.
Model 1 IMP 1
IMP 2
PIU 1
PIU 2
Model 2 IMP 1
IMP 2
PIU 1
PIU 2
Statistical analysis The descriptive statistics (mean and SD), Cronbach’s α coefficients and inter-correlations of the Time 1 and Time 2 subtotal and total scores were examined. Because a cross-lagged model consists of measurement models across time, a preliminary examination of the measurement model is required. First, CFAs were conducted to ensure the model-data fit of the impulsivity and PIU measurement models. Second, measurement invariance across time was also evaluated (Kline, 2010). A series of nested models were compared and tested to confirm that the measurement model is invariant across time. The initial analysis was a test of configural invariance that assumed that the numbers of factors and the pattern of factor loadings were the same between two time points. The second was a test of metric invariance that constrained the unstandardised factor loadings of each indicator to be equal across time. Third, in a test of scalar invariance, the intercept of each indicator was set to be equal across time. The last analysis was a test of uniqueness invariance in which the error variances of each indicator were constrained to be equal across time. The chi-square difference was used to test H0 , stating that the constrained model is not worse than the less constrained model, versus H1 , positing that the constrained model is worse than the less constrained model. For the cross-lagged model, we not only tested whether the hypothesised models showed a good fit to our data but also evaluated nested models (Models 1–3). As shown in Figure 2, Model 1 hypothesises significant synchronous correlations and auto-regressive relationships between impulsivity and PIU as well as reciprocal relationships (the baseline model). However, Models 2 and 3 hypothesise a single-lagged relationship between impulsivity and PIU. Model 2 hypothesises that impulsivity at Time 1 influences PIU across time (freely estimated), and the path coefficient from PIU1 to IMP2 was fixed to zero. Model
Model 3 IMP 1
IMP 2
PIU 1
PIU 2
Figure 2. Cross-lagged models of impulsivity and PIU.
3 hypothesises that PIU at Time 1 influences impulsivity across time (free estimated); the path coefficient from IMP1 to PIU2 was fixed to zero. Model 1 was compared with Models 2 and 3 to determine the model that best fits the data. A possible moderating effect of gender was also investigated. The interaction between IMP1 and gender, the interaction between PIU1 and gender and the gender effect on each latent variable (IMP1, IMP2, PIU1 and PIU2) would be added to the best fitting model among the three models. We used Mplus (Muthén & Muthén, 2010) to conduct the analyses of model fit. All parameters were estimated using the full information maximum likelihood method. Because the chi-square test of fit is sensitive to sample size, the criteria for assessing the model fit included the following (Kline, 2010): (a) a comparative fit index (CFI) greater than .90, which suggests the relative improvement © 2015 International Union of Psychological Science
IMPULSIVITY AS A PRECEDENT FACTOR
in the fit of the researcher’s model over that of a baseline model; (b) a root mean squared error of approximation (RMSEA) of less than .08, which indicates a moderate fit, with an RMSEA of .05 or less suggesting a good fit and (c) a standardised root mean squared residuals (SRMR) of less than .08, with smaller values suggesting a better model fit. RESULTS Descriptive statistics Table 1 shows the descriptive statistics, internal consistencies and inter-correlations of motor impulsivity, non-planning and PIU scale/subscale scores at Time 1 and Time 2. The results indicated adequate reliabilities for each scale/subscale (Cronbach’s 𝛼s ranged from .73 to .87). Additionally, positive correlations were observed among most of the scales/subscales. In general, the PIU1 and PIU2 scores were not correlated with the non-planning1 and non-planning2 scores.
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TABLE 1 Descriptive statistics, internal consistencies and correlations among three factors Time 1 variables
Time 2 variables
Motor1 Nonplan1 PIU1 Motor2 Nonplan2 PIU2 Time 1 Motor1 Nonplan1 PIU1 Time 2 Motor2 Nonplan2 PIU2 Mean SD N Item Alpha
— .28** .22**
— −.05
—
.55** .27** .24** 9.01 3.59 425 8 .73
.13* .53** .05 10.34 3.25 423 7 .78
.21** .01 .57** 4.88 4.30 421 5 .87
Motor = motor impulsivity, PIU = problematic Internet use. *p < .05. **p < .01.
— .24** .36** 9.31 3.40 360 8 .75
— .13* 9.45 3.06 364 7 .77
— 4.67 3.91 364 5 .87
Nonplan = non-planning,
CFA at Time 2 Confirmatory factor analysis CFA at Time 1 For motor impulsivity at Time 1, a CFA based on the 10 items in Lo’s (2013) scale was conducted. However, the modification index suggested that there were residual correlations between items, and some items needed to be deleted to improve the model-data fit. After deleting two items (e.g., “I often have extraneous thoughts when thinking”), the model fit was improved (χ2 [20] = 57.90, p < .001; RMSEA = .066; CFI = .926; SRMR = .041). The factor loadings ranged from .41 to .72, ps < .001. Therefore, only eight items for motor impulsivity were adopted in the subsequent study. For non-planning at Time 1, after one item was deleted (i.e., “I plan trips well ahead of time”), the model fit statistics showed moderate goodness of fit (χ2 [14] = 37.10, p < .001; RMSEA = .062; CFI = .965; SRMR = .035). The factor loadings ranged from .45 to .78, ps < .001. There were seven items in the non-planning subscale. For PIU at Time 1, the CFA also supported a one-factor model (χ2 [5] = 7.03, p = .22; RMSEA = .031; CFI = .998; SRMR = .012). All factor loadings were between .68 and .80, ps < .001. In the model that included the motor impulsivity, non-planning and PIU factors and their indicators as mentioned above, we found that the factor correlation between non-planning and PIU was not statistically significant (r = −.09, p = .13). Because of the lack of significant correlations between the PIU and non-planning score (Table 1) and between the PIU and non-planning factor scores, non-planning was excluded from further analysis. © 2015 International Union of Psychological Science
After the measurement models were finalised for the data at Time 1, CFA was also performed for the data at Time 2. The motor impulsivity results revealed that the model fit was moderate, χ2 (20) = 58.10, p < .001, RMSEA = .072; CFI = .926; SRMR = .045. All indicators had significant factor loadings ranging from .41 to .70, ps < .001. For PIU, the CFA results for the Time 2 sample also revealed a moderate model-data fit (χ2 [5] = 15.27, p < .01; RMSEA = .075; CFI = .987; SRMR = .021). In addition, all indicators had significant factor loadings ranging from .71 to .81, ps < .001. Measurement invariance across time Motor impulsivity measurement invariance First, the configural invariance model fitted the data reasonably well (Table 2). Second, the chi-square difference test between the metric invariance model and the configural invariance model was not significant (Δχ2 [7] = 2.07, n.s.), indicating that the factor loadings were equivalent across the two time points. Third, the chi-square difference of scalar invariance and metric invariance yielded a nonsignificant result (Δχ2 [7] = 2.46, n.s.), indicating that the intercept of each indicator was equivalent across time. Finally, we found a significant change in the chi-square difference test between the uniqueness invariance model and the scalar model (Δχ2 [8] = 38.33, p < .001). This result suggested that the uniqueness invariance model was statistically worse than the scalar invariance model; the error variance of each
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CHEN, LO, LIN TABLE 2 Longitudinal invariance of the motor impulsivity and PIU measurement model
Motor impulsivity Equal configural Equal factor loadings Equal indicator intercepts Equal indicator error variances PIU Equal configural Equal factor loadings Equal indicator intercepts Equal indicator error variances
χ2
df
Δχ2
Δdf
RMSEA
SRMR
CFI
115.99 118.06 120.52 158.85
40 47 54 62
2.07 2.46 38.33***
7 7 8
.069 .062 .056 .063
.043 .044 .044 .065
.926 .931 .935 .906
22.31 27.93 49.95 64.64
10 14 18 23
5.62 22.02*** 14.69**
4 4 5
.056 .050 .064 .067
.016 .027 .034 .042
.993 .992 .982 .976
Δχ2 = nested χ2 difference; RMSEA = root mean square error of approximation; SRMR = standardised root mean square residual; CFI = comparative fit index. **p < .01. ***p < .001. TABLE 3 The goodness-of-fit statistics for the nested models on the predictive relationships between motor impulsivity and PIU χ2
df
Model comparisons
Δχ2
Δdf
RMSEA
SRMR
CFI
505.46 507.06 512.51
280 281 281
Model 2 vs. Model 1 Model 3 vs. Model 1
1.61 7.05**
1 1
.043 .043 .044
.054 .055 .058
.937 .937 .935
Model Model 1 Model 2a Model 3 a The
best fit model. **p < .01.
indicator was not equivalent across time. In sum, our findings demonstrated strong measurement invariance for motor impulsivity, which suggested that the factor loadings and intercepts of the items remained invariant across time (Table 2).
TABLE 4 The estimated unstandardised path coefficients for the model with gender moderating effect Dependent variable
Independent variable
b
SE
Motor2
PIU measurement invariance The equal configural model was tested, and the results showed an acceptable model-data fit (Table 2). Concerning the test of metric invariance, the chi-square difference test was not significant (Δχ2 [4] = 5.62, n.s.), indicating that the factor loadings across two time points were equivalent. A significant result was obtained from the chi-square difference test of scalar invariance (Δχ2 [4] = 22.02, p < .001), suggesting that the intercept of each indicator was not equivalent across time. Therefore, for PIU, weak measurement invariance was found, and the factor loadings of the items remained invariant across time (Table 2). We then proceeded with the cross-lagged analysis. Cross-lagged analysis We conducted model comparisons to examine the cross-lagged relationships between impulsivity and PIU. The results suggested that Models 1 to 3 fit the data well (Table 3). As shown in Table 3, the differential chi-square test statistics between Models 3 and 1 were significant
Motor1 Gender × Motor1 Gender (male = 0, female = 1)
.67*** −.07 −.02
.11 .12 .03
PIU1 Gender × PIU1 Motor 1 Gender × Motor1 Gender
.66*** −.24* .15 .57* −.07
.08 .11 .16 .27 .07
PIU2
Motor 1 Gender
.01
.04
Gender
−.01
.08
PIU1 Motor = motor impulsivity, PIU = problematic Internet use. *p < .05. ***p < .001.
(Δχ2 [1] = 7.05, p < .01), which indicates that Model 3 was statistically a poorer fit than Model 1. The chi-square different test between Model 2 and Model 1 was statistically nonsignificant (Δχ2 [1] = 1.61, n.s.), which indicates that the fit of Model 2 was not worse than that of Model 1. Because Model 2 was more parsimonious than Model 1, Model 2 was the best fit for the data. © 2015 International Union of Psychological Science
IMPULSIVITY AS A PRECEDENT FACTOR
Model 2 .61(.05)
Motor 1
.24(.06)
Motor 2
.38(.06)
.16(.06)
PIU 1
PIU 2 .57(.04)
Figure 3. Standardised path coefficients of Model 2. Note. Parentheses represent the corresponding standard error.
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shown in Table 4. The auto-regressive path coefficients were significant (Path Motor1-Motor2: b = .67, p < .001; Path PIU1-PIU2: b = .66, p < .001). Gender did not moderate the path from Motor1 to Motor2, but it moderated the path from PIU1 to PIU2 (b = −0.24, p < .05). The negative coefficient suggests that the effect of PIU1 on PIU2 decreased for female participants (given that gender equals 0 for males and 1 for females). The results showed that the prediction from Motor1 to PIU 2 was not significant, but gender moderated the path of Motor1-PIU2 (b = 0.57, p < .05). Therefore, Motor1 predicted PIU2 for females but not for males. DISCUSSION
Time 1 Motor 1
Gender
Motor 2
c
a
b
e
PIU 1
Relationships between motor impulsivity, non-planning and PIU
Time 2
PIU 2 d
Figure 4. Extended Model 2 with gender as a moderator.
In Model 2, the synchronous correlations between motor impulsivity and PIU were positive (Correlation Motor1-PIU1 = .24, p < .001; Correlation Motor2PIU2 = .38, p < .001). The standardised auto-regressive coefficients of motor impulsivity and PIU were significant (Path Motor1-Motor2 = .61, p < .001; Path PIU1-PIU2 = .57, p < .001), which suggests that the measures of motor impulsivity and PIU were stable over 24 months. For the cross-lagged relationships, Motor1 positively predicted PIU2 (Standardised Path Motor1-PIU2 = .16, p < .01), but PIU1 did not predict Motor2 (Figure 3). Gender as moderator After the cross-lagged model comparison suggested that motor impulsivity is a precedent factor of PIU, the subsequent analysis included gender as a moderator in the predicted relationship between Motor1 and Motor2 (“c” path in Figure 4), Motor1 and PIU2 (“e” path in Figure 4) and PIU1 and PIU2 (“d” path in Figure 4). We also assumed that gender predicts the four latent factors in this model (Figure 4). Mplus (Muthén & Muthén, 2010) provided the unstandardised path coefficients and the statistically significant test of H0 : b = 0. The results are © 2015 International Union of Psychological Science
The Pearson’s correlation showed that motor impulsivity at Times 1 and 2 was positively correlated with PIU at both time points, whereas non-planning at Times 1 and 2 was uncorrelated with PIU at both time points. This result supports the findings of Cao et al. (2007) that an Internet-addicted group had significantly higher motor impulsivity scores than did a control group, but the difference in the non-planning scores of the two groups was not significant. Therefore, PIU is strongly associated with motor impulsivity (defined as lacking thorough consideration before acting) but not non-planning (defined as behaviours that solely consider the present rather than the future). The reasons why non-planning is of little relevance to PIU have not been discussed in the literature. Future research may consider whether a mediating or moderating factor is involved in the relationship between non-planning and PIU. For example, the relationship between non-planning and PIU may be significant for students with attention deficit/hyperactivity disorder. Cross-lagged analysis The results suggested significant synchronous correlations between various latent variables; the auto-regressive path coefficients were also significant. Moreover, motor impulsivity was a prospective predictor of PIU; the reciprocal relationship was not supported by our data. Our synchronous correlation results are consistent with the findings of Cao et al. (2007) who reported that high school students with greater impulsivity are more likely to have Internet addiction problems. Our results also partially support Park et al.’s (2010) finding that those with excessive Internet game use display greater impulsivity than normal users and that impulsivity is positively correlated with the severity of excessive Internet gaming. The findings of this study add to the literature by confirming the unidirectional temporal relationship between motor
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impulsivity and PIU in a sample of college students; PIU did not lead to the subsequent development of a change in motor impulsivity. Although not directly observed in this study, a possible reason for the abovementioned relationship must be discussed. To some extent, impulsivity can be conceptualised as a loss of control. According to Baumeister’s self-control theory (Baumeister, Vohs, & Tice, 2007), self-control refers to individuals’ ability to transform, inhibit and override their instinctual reaction in order to comply with an ideal, value or standard. The resource of self-control is limited and becomes exhausted if repeatedly used. A habitual Internet user might use most of their strength to control their behaviour in alignment with social rules and standards. Sometimes, individuals can feel exhausted by pressures from various sources. For an individual with strong motor impulsivity, additional self-control resources are consumed, resulting in the depletion of self-control and leading to PIU. Gender difference Regarding the gender difference in the predictive relationship between motor impulsivity and PIU, we found that motor impulsivity levels increase subsequent PIU levels for females; however, no cross-lagged relationship was found for males. A possible explanation is that the influence of previous PIU levels on subsequent PIU is greater for males than for females. Therefore, the relative strength of the predictive effect of previous motor impulsivity on PIU after 2 years is trivial for males. In other words, because male college students have fairly stable PIU levels across time, the influence of previous impulsivity levels on subsequent PIU was not obvious. The findings support our hypothesis of gender difference and are consistent with the view that the relationship between motor impulsivity and PIU is not straightforward. There is still much to learn about why the predictive power of the direct effect of motor impulsivity on subsequent PIU is not significant for males. Limitations and future studies Several limitations should be considered when interpreting these findings. With regard to the measurement, a weakness in the PIU scale lies in the use of only five items to measure the core symptoms, as this scale neglects behavioural consequences (e.g., lifestyle changes or functional impairments). Because all the information was obtained from self-reported measurement tools, a more thorough understanding of impulsivity and PIU may require other methods, such as behavioural laboratory measures (Dougherty, Mathias, Marsh, & Jagar, 2005)
for impulsive behaviour, a psychological diary or observations of the underlying process of PIU. Moreover, it should be noted that only a non-clinical sample was adopted in this study; therefore, the results may not apply to other samples, particularly samples of individuals who are diagnosed with Internet-related disorders. This study included a time interval of four semesters between the two data collection time points. Future studies could include data from more than two time points to conduct more elaborate analyses of time-related effects (e.g., an accelerating effect, a growth curve effect or a longitudinal mediation effect). Future studies should also consider other proximal of distal factors that may associated with PIU (e.g., other personality traits, trauma, media exposure) as mediators or moderators of the relationship between motor impulsivity and PIU. CONCLUSION This study provides researchers and practitioners with valuable insights into impulsivity, including its structure, its temporal relationship with PIU and associated gender differences. Researchers might benefit from this new knowledge and the suggested directions for future research. Practitioners might implement a programme for general college students to approach the issue of impulse control. To develop impulse control, techniques for managing impulses, such as enhancing self-awareness and delaying action on impulses, could be introduced. A training programme that aims to improve individuals’ willpower for self-control could also be an effective intervention for PIU. Our findings aim to not only highlight the issue of impulsivity and PIU in colleges but also initiate societal reflection on the future use of electronic media (Aboujaoude, 2010). Manuscript received March 2015 Revised manuscript accepted September 2015
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Collado, A., Felton, J. W., Macpherson, L., & Lejuez, C. W. (2014). Longitudinal trajectories of sensation seeking, risk taking propensity, and impulsivity across early to middle adolescence. Addictive Behaviors, 39(11), 1580–1588. Dickman, S. J. (1990). Functional and dysfunctional impulsivity: Personality and cognitive correlates. Journal of Personality and Social Psychology, 58(1), 95–102. Dougherty, D. M., Mathias, C. W., Marsh, D. M., & Jagar, A. A. (2005). Laboratory behavioral measures of impulsivity. Behavior Research Methods, 37(1), 82–90. Engelberg, E., & Sjöberg, L. (2004). Internet use, social skills, and adjustment. CyberPsychology & Behavior, 7, 41–47. Eppinger, B., Nystrom, L. E., & Cohen, J. D. (2012). Reduced sensitivity to immediate reward during decision-making in older than younger adults. PLoS One, 7(5), e36953. Eysenck, S. B. G., & Eysenck, H. J. (1977). The place of Impulsiveness in a dimensional system of personality description. British Journal of Social and Clinical Psychology, 16, 57–68. Huang, Y.-R. (2006). Identity and intimacy crises and their relationship to Internet dependence among college students. CyberPsychology & Behavior, 9, 571–576. Kim, J. S., & Chun, B. C. (2005). Association of Internet addiction with health promotion lifestyle profile and perceived health status in adolescents. Journal of Preventive Medicine and Public Health, 38, 53–60. Kline, R. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford. Lee, H. W., Choi, J. S., Shin, Y. C., Lee, J. Y., Jung, H. Y., & Kwon, J. S. (2012). Impulsivity in Internet addiction: A comparison with pathological gambling. Cyberpsychology, Behavior and Social Networking, 15(7), 373–377. Li, C. H., Ko, H. C., Weng, L. J., Liau, L. C., & Lu, R. B. (2002). The development of an impulsiveness scale: Psychometric properties and relation to antisocial personality disorder. Chinese Journal of Psychology, 44(1), 109–119. Lin, M. P., Ko, H. C., & Wu, J. Y. W. (2011). Prevalence and psychosocial risk factors associated with Internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychology, Behavior and Social Networking, 14(12), 741–746. Lo, M-T (2013). Impulsivity and problematic Internet use in Taiwan college students: A cross-lagged panel analysis. Unpublished thesis, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
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Luijten, M., Meerkerk, G.-J., Franken, I. H., van de Wetering, B. J., & Schoenmakers, T. M. (2015). An fMRI study of cognitive control in problem gamers. Psychiatry Research: Neuroimaging, 231(3), 262–268. Martin, A. J., & Liem, G. A. D. (2010). Academic personal bests (PBs), engagement, and achievement: A cross-lagged panel analysis. Learning and Individual Differences, 20(3), 265–270. Meerkerk, G.-J., van den Eijnden, R. J. J. M., Franken, I. H. A., & Garretsen, H. F. L. (2010). Is compulsive Internet use related to sensitivity to reward and punishment, and impulsivity? Computers in Human Behavior, 26(4), 729–735. Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén. Park, H. S., Kim, S. H., Bang, S. A., Yoon, E. J., Cho, S. S., & Kim, S. E. (2010). Altered regional cerebral glucose metabolism in Internet game overusers: A F-18-fluorodeoxyglucose positron emission tomography study. CNS Spectrums, 15(3), 159–166. Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51(6), 768–774. Schimmenti, A., Passanisi, A., Alessia, M., Manzella, S., & Famà, F. (2014). Insecure attachment attitudes in the onset of problematic Internet use among late adolescents. Child Psychiatry and Human Development, 45(5), 588–595. Shapira, N. A., Goldsmith, T. D., Keck, P. E., Khosla, U. M., & McElroy, S. L. (2000). Psychiatric features of individuals with problematic Internet use. Journal of Affective Disorders, 57(1–3), 267–272. Steinberg, L. (2010). A dual systems model of adolescent risk-taking. Developmental Psychobiology, 52(3), 216–224. Stoltenberg, S., Batien, B., & Birgenheir, D. (2008). Does gender moderate associations among impulsivity and health-risk behaviors? Addictive Behaviors, 33(2), 252–265. Yen, C.-F., Hsiao, R. C., Ko, C.-H., Yen, J.-Y., Huang, C.-F., Liu, S.-C., & Wang, S.-Y. (2010). The relationships between body mass index and television viewing, Internet use and cellular phone use: The moderating effects of socio-demographic characteristics and exercise. International Journal of Eating Disorders, 43(6), 565–571. Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & Behavior, 1(3), 237–244.
Impulsivity as a precedent factor for problematic Internet use: How can we be sure? Ssu-Kuang Chen, Meng-Ting Lo, and Sunny S. J. Lin Institute of Education, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
P
revious research has suggested that problematic Internet use (PIU) is associated with impulse control disorder. Although researchers have suggested that impulsivity is a risk factor for PIU, the literature lacks longitudinal evidence on the relationship between impulsivity and PIU. We aimed to use a cross-lagged analytic framework to identify temporal order effects and hypothesised that impulsivity was the precedent factor for PIU. In a panel sample of college students (N = 367), trait impulsivity and PIU were measured in the spring of freshman year and in their junior year. The measures included a self-developed PIU Scale and the revised Impulsiveness Scale based on Barratt’s concept. We found that “non-planning impulsivity” was not associated with PIU. The “motor impulsivity” subfactor was thus adopted in the cross-lagged model. The results suggest that motor impulsivity and PIU were stable across time. Motor impulsivity at Time 1 positively predicted PIU at Time 2, but PIU at Time 1 did not predict motor impulsivity at Time 2. A further investigation using gender as a moderator found a gender difference in the temporal relationship. Because motor impulsivity is a risk factor for PIU, potential prevention strategies based on this result are suggested. Keywords: Impulsivity; Problematic Internet use; Cross-lagged analysis; Gender difference.
Background Problematic Internet use (PIU) among adolescents is a growing phenomenon. Substantial evidence has shown that although the Internet is a convenient tool for work, entertainment and communication, spending excessive amounts of time on the Internet may result in “PIU” and may have negative impacts on an individual’s social functions and psychological or physical health (Aboujaoude, 2010). Many studies have suggested that PIU has become a serious problem that is similar to pathological gambling, which is conceptualised as an impulse control disorder (Shapira, Goldsmith, Keck, Khosla, & McElroy, 2000; Young, 1998). Researchers have also found that individuals’ impulsivity levels are positively associated with PIU (Cao, Su, Liu, & Gao, 2007; Park et al., 2010). A study that involved small samples of clinical patients reached the conclusion that impulsivity is a marker of PIU development (Lee et al., 2012). However, the research suggesting that impulsivity is a risk factor for PIU has primarily relied on cross-sectional designs (Cao et al., 2007; Lee et al., 2012), a method that cannot provide strong evidence to determine the temporal ordering effect between
two variables (e.g., A is precedent to B). Information on the temporal order of the relationship between PIU and its risk factors is needed before PIU prevention strategies can be planned and studied, but researchers have thus far neglected the importance of temporal order. To fill this gap in the literature, the primary objective of this study was to examine the abovementioned temporal order effects. Longitudinal data across 24-month intervals were collected and examined using cross-lagged analyses. Although impulsivity is a stable characteristic, the relationship between impulsivity and PIU is not necessarily constant across time. Therefore, the stability of impulsivity and PIU, their temporal order and their synchronous relationship were analysed simultaneously. This study also considered gender1 as a critical factor in the relationship between impulsivity and PIU. Hence, after the temporal order effects were confirmed, the moderating effect of gender was investigated. Problematic Internet use According to Shapira et al., (2000), the concept of PIU describes an individual’s inability to control the impulse
Correspondence should be addressed to: Sunny S. J. Lin, Institute of Education National Chiao Tung University, Taiwan: 1001 Ta-Hsueh Rd, Hsinchu 300, Taiwan, R.O.C. (E-mail: [email protected]). This research was funded by Ministry of Science and Technology (NSC 97-2631-S-009-001, NSC-100-2631-S-009-001), Taiwan (R. O. C.). 1 We use gender, instead of sex, to prevent confusion between sex and sexual behaviour.
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and growing tension to use the Internet, which ultimately leads to feelings of distress or discomfort and negative life outcomes. In a study of undergraduates, students who used the Internet at excessively high levels reported high levels of loneliness (Engelberg & Sjöberg, 2004). A large-scale survey in Taiwan found that college students who spent more time on the Internet tended to have problems with identity and intimacy (Huang, 2006). Additionally, a study that explored the relationship between PIU and attachment found that insecure attachment attitudes are related to the development of PIU among older adolescents (Schimmenti, Passanisi, Alessia, Manzella, & Famà, 2014). With regard to the harmful results of PIU, a study of adolescents reported that high levels of PIU might result in adverse consequences and changes in lifestyle, such as physical inactivity (Yen et al., 2010). Moreover, compared with non-addicts, the Korean Internet addicts in Kim and Chun’s (2005) study reported less sleep, poorer diet and less regular exercise. Although many studies have provided evidence of the psychological harm that can result from excessive Internet use, this evidence was based on different PIU assessments that lack consensus on the definition of PIU. Thus, further research is needed (Aboujaoude, 2010). Impulsivity Psychologists have conceptualised impulsivity as a personal trait with various definitions and dimensions (Eysenck & Eysenck, 1977; Dickman, 1990). In particular, Barratt (1985) defined impulsivity as a three-dimensional concept that includes motor, cognitive and non-planning dimensions (Patton, Stanford, & Barratt, 1995). Motor impulsivity was defined as lacking thorough consideration before acting; cognitive impulsivity involved rapid decision making; and non-planning was defined as behaviours that show concern only for the present and a lacking of planning for the future. Although impulsivity is considered to be stable, longitudinal evidence has suggested a possible developmental shift. For example, Collado, Felton, Macpherson, and Lejuez (2014) found a curvilinear trend of impulsivity among middle adolescents. Other evidence has shown a linear decrease in impulsivity from childhood into adulthood (Steinberg, 2010). Moreover, neuroimaging findings have revealed that decreases in impulsivity during adolescence may be associated with the maturation of brain regions that are associated with cognitive control (Eppinger, Nystrom, & Cohen, 2012). PIU and impulsivity In a study that examined the relationship between Internet addiction and trait impulsivity, Lee et al. (2012) found that Internet addiction and pathological gambling patients
had higher scores on an impulsivity scale than healthy controls. Some research has shown that Internet addicts are more impulsive than non-addicts and that impulsivity is positively correlated with Internet addiction (Cao et al., 2007; Park et al., 2010). In a study of Dutch Internet users with an average age of 40 years, Meerkerk, van den Eijnden, Franken, and Garretsen (2010) found that rash, spontaneous impulsiveness (performing actions without considering the consequences) predicted PIU. Moreover, in a neuroscience study, researchers (Park et al., 2010) found that the 11 excessive Internet users in a sample of 20 males had abnormal glucose metabolism in their orbitofrontal cortex and other brain regions, most of which were associated with impulse control. All these results support the hypothesis that PIU is associated with impulsivity. With respect to online game addiction, Billieux et al. (2015) found that among different types of online gamers classified based on game motivation, trait impulsivity and self-esteem indicators, the gamers with problematic use had relatively higher levels of impulsivity. Gender difference To the best of our knowledge, no previous research has attempted to investigate the moderating effect of gender on the relationship between impulsivity and PIU. However, the literature on gender differences in the relationship between impulsivity and health-risk behaviours (Stoltenberg, Batien, & Birgenheir, 2008) may provide insights for this study. According to the literature, the question of whether there are gender differences in the associations between impulsivity and certain health-risk behaviours is inconclusive (Stoltenberg et al., 2008). For example, impulsivity is associated with alcohol use in both men and women, but impulsivity is associated with nicotine use only in women. Additionally, impulsivity is related to smoking in women but not in men. The present study The aforementioned literature does not address or explain whether impulsivity influences PIU. Lin, Ko, and Wu (2011) noted that further studies should investigate whether impulsivity is a risk factor for or a result of PIU. Luijten, Meerkerk, Franken, van de Wetering, and Schoenmakers (2015) suggested that researchers should not exclude the possibility that any prolonged excessive and compulsive behaviour may change neural circuits; thus, longitudinal studies on temporal relationships are valuable. Hence, this study aims to provide evidence for the claim that impulsivity is a precedent factor for PIU using a proper study design and data analysis techniques. Because gender is an important factor in understanding the nature of the impulsivity–PIU relationship, we also © 2015 International Union of Psychological Science
IMPULSIVITY AS A PRECEDENT FACTOR Time 1
Time 2 e1
IMP1
c
IMP2 f b
a e d PIU 2
PIU 1
e2
Figure 1. The cross-lagged model of PIU.
tested the moderating effect of gender and assumed that the impulsivity–PIU relationship is different between females and males. This study adopted a cross-lagged model of impulsivity and PIU to investigate the temporal order effect. Cross-lagged analysis is a method that examines the covariation between variables that are measured at two time points and identifies the temporal order of the variables (Martin & Liem, 2010). In this study, we assumed that impulsivity would predict subsequent PIU across time. Therefore, data on impulsivity and PIU were repeatedly collected at two time points in a sample of university students. As shown in Figure 1, IMP1 is the measure of impulsivity at Time 1, PIU1 represents the PIU level at Time 1, and IMP2 and PIU2 are the measures at Time 2. The effects of “a” and “b” represent the synchronous correlations in which “a” examines the relationships between impulsivity and PIU at Time 1 and “b” is the correlation of measurement error for the two latent variables at Time 2. The effects of “c” and “d” represent auto-regressive path coefficients that examine the variables’ test-retest reliability over time. The path coefficients “e” and “f” represent the cross-lagged relationships between the latent variables that examine the relative salience of impulsivity in predicting subsequent PIU compared with the relative salience of PIU in predicting subsequent impulsivity. A significant coefficient estimate of “e” and “f” suggests that the cross-lagged effect is sufficiently strong to claim the existence of a temporal order after controlling for the synchronous correlations and auto-regressive relationships that usually primarily account for the variance in Time 2 latent variables. METHOD Participants and procedure The research was part of a longitudinal study of the well-being of college students in Taiwan that is financially supported by the Ministry of Science and Technology of Taiwan. A total of 430 freshmen were recruited © 2015 International Union of Psychological Science
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through instructors from several different departments in a college. The students voluntarily participated in the first survey in April 2012 (Time 1) and in the second survey after 24 months (Time 2). The final sample comprised 367 college students (59% males) who completed both surveys (85% retention rate). The average age of the participants was 19 years (SD = .55) at Time 1. The survey instrument collected data on demographics (age, gender and major), impulsivity, PIU and the other variables that were not used in this study (e.g., Internet use habits and PIU-related issues such as lifestyle change and severe consequences). The questionnaires were distributed to all the participants in a class and were returned immediately after the group survey. All questionnaires were valid with only a few missing responses. Informed consent was obtained for all participants prior to their participation in the study. After each survey, all participants received a small gift as compensation (e.g., stationery valued at $6). Measures Revised impulsivity scale We used the two-factor version of an impulsivity scale (Lo, 2013) that was revised from the 31-item scale of Li, Ko, Weng, Liau, and Lu (2002), which was designed based on Barratt’s impulsivity concept. The items are rated using a 4-point scale, with higher scores indicating greater impulsivity. In study by Lo (2013), items were revised to correspond to the characteristics of college students. A principal axis factor (PAF) analysis with varimax rotation of the 31-revised items from Li et al.’s impulsivity scale was performed for 526 college students. The number of extracted factors was determined by the standard of eigenvalue with values greater than or equal to one; items were retained if their factor loadings were greater than or equal to .40. The two-factor version measures motor impulsivity (10 items) and non-planning (8 items). Motor impulsiveness concerns the extent to which individuals do not thoroughly consider an action, and an example item is “I say things without thinking.” Non-planning impulsiveness concerns the extent to which individuals care about the present and do not plan for the future, and an example item is “I am more interested in the present than the future.” In this study, confirmatory factor analysis (CFA) results supported single-factor models of motor impulsiveness (eight items) and non-planning (seven items). We examined the single-factor model because it would be used in the subsequent cross-lagged analysis. Detailed CFA results are presented in the results section. PIU scale In the study, we adopted the self-developed PIU scale for college students that includes five self-reported items
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regarding five core behavioural symptoms (i.e., tolerance, withdrawal, impulsivity, preoccupation and craving). The item regarding tolerance states as follows: “I need to use the Internet for increasing amounts of time to achieve satisfaction.” Withdrawal is measured by the following item: “When I tried to stop using the Internet, I was anxious.” The item involving impulsivity states as follows: “When I used the Internet, I felt excited and satisfied.” The item measuring preoccupation states as follows: “I would spend a tremendous amount of time arranging to use the Internet.” Craving is rated based on the following statement: “I rush to the Internet whenever I have a chance.” Responses to each question were provided on a 5-point Likert scale. In the PAF analysis, one factor was extracted, and 57.44% of the total variance was explained. All factor loadings were above .69.
Model 1 IMP 1
IMP 2
PIU 1
PIU 2
Model 2 IMP 1
IMP 2
PIU 1
PIU 2
Statistical analysis The descriptive statistics (mean and SD), Cronbach’s α coefficients and inter-correlations of the Time 1 and Time 2 subtotal and total scores were examined. Because a cross-lagged model consists of measurement models across time, a preliminary examination of the measurement model is required. First, CFAs were conducted to ensure the model-data fit of the impulsivity and PIU measurement models. Second, measurement invariance across time was also evaluated (Kline, 2010). A series of nested models were compared and tested to confirm that the measurement model is invariant across time. The initial analysis was a test of configural invariance that assumed that the numbers of factors and the pattern of factor loadings were the same between two time points. The second was a test of metric invariance that constrained the unstandardised factor loadings of each indicator to be equal across time. Third, in a test of scalar invariance, the intercept of each indicator was set to be equal across time. The last analysis was a test of uniqueness invariance in which the error variances of each indicator were constrained to be equal across time. The chi-square difference was used to test H0 , stating that the constrained model is not worse than the less constrained model, versus H1 , positing that the constrained model is worse than the less constrained model. For the cross-lagged model, we not only tested whether the hypothesised models showed a good fit to our data but also evaluated nested models (Models 1–3). As shown in Figure 2, Model 1 hypothesises significant synchronous correlations and auto-regressive relationships between impulsivity and PIU as well as reciprocal relationships (the baseline model). However, Models 2 and 3 hypothesise a single-lagged relationship between impulsivity and PIU. Model 2 hypothesises that impulsivity at Time 1 influences PIU across time (freely estimated), and the path coefficient from PIU1 to IMP2 was fixed to zero. Model
Model 3 IMP 1
IMP 2
PIU 1
PIU 2
Figure 2. Cross-lagged models of impulsivity and PIU.
3 hypothesises that PIU at Time 1 influences impulsivity across time (free estimated); the path coefficient from IMP1 to PIU2 was fixed to zero. Model 1 was compared with Models 2 and 3 to determine the model that best fits the data. A possible moderating effect of gender was also investigated. The interaction between IMP1 and gender, the interaction between PIU1 and gender and the gender effect on each latent variable (IMP1, IMP2, PIU1 and PIU2) would be added to the best fitting model among the three models. We used Mplus (Muthén & Muthén, 2010) to conduct the analyses of model fit. All parameters were estimated using the full information maximum likelihood method. Because the chi-square test of fit is sensitive to sample size, the criteria for assessing the model fit included the following (Kline, 2010): (a) a comparative fit index (CFI) greater than .90, which suggests the relative improvement © 2015 International Union of Psychological Science
IMPULSIVITY AS A PRECEDENT FACTOR
in the fit of the researcher’s model over that of a baseline model; (b) a root mean squared error of approximation (RMSEA) of less than .08, which indicates a moderate fit, with an RMSEA of .05 or less suggesting a good fit and (c) a standardised root mean squared residuals (SRMR) of less than .08, with smaller values suggesting a better model fit. RESULTS Descriptive statistics Table 1 shows the descriptive statistics, internal consistencies and inter-correlations of motor impulsivity, non-planning and PIU scale/subscale scores at Time 1 and Time 2. The results indicated adequate reliabilities for each scale/subscale (Cronbach’s 𝛼s ranged from .73 to .87). Additionally, positive correlations were observed among most of the scales/subscales. In general, the PIU1 and PIU2 scores were not correlated with the non-planning1 and non-planning2 scores.
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TABLE 1 Descriptive statistics, internal consistencies and correlations among three factors Time 1 variables
Time 2 variables
Motor1 Nonplan1 PIU1 Motor2 Nonplan2 PIU2 Time 1 Motor1 Nonplan1 PIU1 Time 2 Motor2 Nonplan2 PIU2 Mean SD N Item Alpha
— .28** .22**
— −.05
—
.55** .27** .24** 9.01 3.59 425 8 .73
.13* .53** .05 10.34 3.25 423 7 .78
.21** .01 .57** 4.88 4.30 421 5 .87
Motor = motor impulsivity, PIU = problematic Internet use. *p < .05. **p < .01.
— .24** .36** 9.31 3.40 360 8 .75
— .13* 9.45 3.06 364 7 .77
— 4.67 3.91 364 5 .87
Nonplan = non-planning,
CFA at Time 2 Confirmatory factor analysis CFA at Time 1 For motor impulsivity at Time 1, a CFA based on the 10 items in Lo’s (2013) scale was conducted. However, the modification index suggested that there were residual correlations between items, and some items needed to be deleted to improve the model-data fit. After deleting two items (e.g., “I often have extraneous thoughts when thinking”), the model fit was improved (χ2 [20] = 57.90, p < .001; RMSEA = .066; CFI = .926; SRMR = .041). The factor loadings ranged from .41 to .72, ps < .001. Therefore, only eight items for motor impulsivity were adopted in the subsequent study. For non-planning at Time 1, after one item was deleted (i.e., “I plan trips well ahead of time”), the model fit statistics showed moderate goodness of fit (χ2 [14] = 37.10, p < .001; RMSEA = .062; CFI = .965; SRMR = .035). The factor loadings ranged from .45 to .78, ps < .001. There were seven items in the non-planning subscale. For PIU at Time 1, the CFA also supported a one-factor model (χ2 [5] = 7.03, p = .22; RMSEA = .031; CFI = .998; SRMR = .012). All factor loadings were between .68 and .80, ps < .001. In the model that included the motor impulsivity, non-planning and PIU factors and their indicators as mentioned above, we found that the factor correlation between non-planning and PIU was not statistically significant (r = −.09, p = .13). Because of the lack of significant correlations between the PIU and non-planning score (Table 1) and between the PIU and non-planning factor scores, non-planning was excluded from further analysis. © 2015 International Union of Psychological Science
After the measurement models were finalised for the data at Time 1, CFA was also performed for the data at Time 2. The motor impulsivity results revealed that the model fit was moderate, χ2 (20) = 58.10, p < .001, RMSEA = .072; CFI = .926; SRMR = .045. All indicators had significant factor loadings ranging from .41 to .70, ps < .001. For PIU, the CFA results for the Time 2 sample also revealed a moderate model-data fit (χ2 [5] = 15.27, p < .01; RMSEA = .075; CFI = .987; SRMR = .021). In addition, all indicators had significant factor loadings ranging from .71 to .81, ps < .001. Measurement invariance across time Motor impulsivity measurement invariance First, the configural invariance model fitted the data reasonably well (Table 2). Second, the chi-square difference test between the metric invariance model and the configural invariance model was not significant (Δχ2 [7] = 2.07, n.s.), indicating that the factor loadings were equivalent across the two time points. Third, the chi-square difference of scalar invariance and metric invariance yielded a nonsignificant result (Δχ2 [7] = 2.46, n.s.), indicating that the intercept of each indicator was equivalent across time. Finally, we found a significant change in the chi-square difference test between the uniqueness invariance model and the scalar model (Δχ2 [8] = 38.33, p < .001). This result suggested that the uniqueness invariance model was statistically worse than the scalar invariance model; the error variance of each
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CHEN, LO, LIN TABLE 2 Longitudinal invariance of the motor impulsivity and PIU measurement model
Motor impulsivity Equal configural Equal factor loadings Equal indicator intercepts Equal indicator error variances PIU Equal configural Equal factor loadings Equal indicator intercepts Equal indicator error variances
χ2
df
Δχ2
Δdf
RMSEA
SRMR
CFI
115.99 118.06 120.52 158.85
40 47 54 62
2.07 2.46 38.33***
7 7 8
.069 .062 .056 .063
.043 .044 .044 .065
.926 .931 .935 .906
22.31 27.93 49.95 64.64
10 14 18 23
5.62 22.02*** 14.69**
4 4 5
.056 .050 .064 .067
.016 .027 .034 .042
.993 .992 .982 .976
Δχ2 = nested χ2 difference; RMSEA = root mean square error of approximation; SRMR = standardised root mean square residual; CFI = comparative fit index. **p < .01. ***p < .001. TABLE 3 The goodness-of-fit statistics for the nested models on the predictive relationships between motor impulsivity and PIU χ2
df
Model comparisons
Δχ2
Δdf
RMSEA
SRMR
CFI
505.46 507.06 512.51
280 281 281
Model 2 vs. Model 1 Model 3 vs. Model 1
1.61 7.05**
1 1
.043 .043 .044
.054 .055 .058
.937 .937 .935
Model Model 1 Model 2a Model 3 a The
best fit model. **p < .01.
indicator was not equivalent across time. In sum, our findings demonstrated strong measurement invariance for motor impulsivity, which suggested that the factor loadings and intercepts of the items remained invariant across time (Table 2).
TABLE 4 The estimated unstandardised path coefficients for the model with gender moderating effect Dependent variable
Independent variable
b
SE
Motor2
PIU measurement invariance The equal configural model was tested, and the results showed an acceptable model-data fit (Table 2). Concerning the test of metric invariance, the chi-square difference test was not significant (Δχ2 [4] = 5.62, n.s.), indicating that the factor loadings across two time points were equivalent. A significant result was obtained from the chi-square difference test of scalar invariance (Δχ2 [4] = 22.02, p < .001), suggesting that the intercept of each indicator was not equivalent across time. Therefore, for PIU, weak measurement invariance was found, and the factor loadings of the items remained invariant across time (Table 2). We then proceeded with the cross-lagged analysis. Cross-lagged analysis We conducted model comparisons to examine the cross-lagged relationships between impulsivity and PIU. The results suggested that Models 1 to 3 fit the data well (Table 3). As shown in Table 3, the differential chi-square test statistics between Models 3 and 1 were significant
Motor1 Gender × Motor1 Gender (male = 0, female = 1)
.67*** −.07 −.02
.11 .12 .03
PIU1 Gender × PIU1 Motor 1 Gender × Motor1 Gender
.66*** −.24* .15 .57* −.07
.08 .11 .16 .27 .07
PIU2
Motor 1 Gender
.01
.04
Gender
−.01
.08
PIU1 Motor = motor impulsivity, PIU = problematic Internet use. *p < .05. ***p < .001.
(Δχ2 [1] = 7.05, p < .01), which indicates that Model 3 was statistically a poorer fit than Model 1. The chi-square different test between Model 2 and Model 1 was statistically nonsignificant (Δχ2 [1] = 1.61, n.s.), which indicates that the fit of Model 2 was not worse than that of Model 1. Because Model 2 was more parsimonious than Model 1, Model 2 was the best fit for the data. © 2015 International Union of Psychological Science
IMPULSIVITY AS A PRECEDENT FACTOR
Model 2 .61(.05)
Motor 1
.24(.06)
Motor 2
.38(.06)
.16(.06)
PIU 1
PIU 2 .57(.04)
Figure 3. Standardised path coefficients of Model 2. Note. Parentheses represent the corresponding standard error.
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shown in Table 4. The auto-regressive path coefficients were significant (Path Motor1-Motor2: b = .67, p < .001; Path PIU1-PIU2: b = .66, p < .001). Gender did not moderate the path from Motor1 to Motor2, but it moderated the path from PIU1 to PIU2 (b = −0.24, p < .05). The negative coefficient suggests that the effect of PIU1 on PIU2 decreased for female participants (given that gender equals 0 for males and 1 for females). The results showed that the prediction from Motor1 to PIU 2 was not significant, but gender moderated the path of Motor1-PIU2 (b = 0.57, p < .05). Therefore, Motor1 predicted PIU2 for females but not for males. DISCUSSION
Time 1 Motor 1
Gender
Motor 2
c
a
b
e
PIU 1
Relationships between motor impulsivity, non-planning and PIU
Time 2
PIU 2 d
Figure 4. Extended Model 2 with gender as a moderator.
In Model 2, the synchronous correlations between motor impulsivity and PIU were positive (Correlation Motor1-PIU1 = .24, p < .001; Correlation Motor2PIU2 = .38, p < .001). The standardised auto-regressive coefficients of motor impulsivity and PIU were significant (Path Motor1-Motor2 = .61, p < .001; Path PIU1-PIU2 = .57, p < .001), which suggests that the measures of motor impulsivity and PIU were stable over 24 months. For the cross-lagged relationships, Motor1 positively predicted PIU2 (Standardised Path Motor1-PIU2 = .16, p < .01), but PIU1 did not predict Motor2 (Figure 3). Gender as moderator After the cross-lagged model comparison suggested that motor impulsivity is a precedent factor of PIU, the subsequent analysis included gender as a moderator in the predicted relationship between Motor1 and Motor2 (“c” path in Figure 4), Motor1 and PIU2 (“e” path in Figure 4) and PIU1 and PIU2 (“d” path in Figure 4). We also assumed that gender predicts the four latent factors in this model (Figure 4). Mplus (Muthén & Muthén, 2010) provided the unstandardised path coefficients and the statistically significant test of H0 : b = 0. The results are © 2015 International Union of Psychological Science
The Pearson’s correlation showed that motor impulsivity at Times 1 and 2 was positively correlated with PIU at both time points, whereas non-planning at Times 1 and 2 was uncorrelated with PIU at both time points. This result supports the findings of Cao et al. (2007) that an Internet-addicted group had significantly higher motor impulsivity scores than did a control group, but the difference in the non-planning scores of the two groups was not significant. Therefore, PIU is strongly associated with motor impulsivity (defined as lacking thorough consideration before acting) but not non-planning (defined as behaviours that solely consider the present rather than the future). The reasons why non-planning is of little relevance to PIU have not been discussed in the literature. Future research may consider whether a mediating or moderating factor is involved in the relationship between non-planning and PIU. For example, the relationship between non-planning and PIU may be significant for students with attention deficit/hyperactivity disorder. Cross-lagged analysis The results suggested significant synchronous correlations between various latent variables; the auto-regressive path coefficients were also significant. Moreover, motor impulsivity was a prospective predictor of PIU; the reciprocal relationship was not supported by our data. Our synchronous correlation results are consistent with the findings of Cao et al. (2007) who reported that high school students with greater impulsivity are more likely to have Internet addiction problems. Our results also partially support Park et al.’s (2010) finding that those with excessive Internet game use display greater impulsivity than normal users and that impulsivity is positively correlated with the severity of excessive Internet gaming. The findings of this study add to the literature by confirming the unidirectional temporal relationship between motor
8
CHEN, LO, LIN
impulsivity and PIU in a sample of college students; PIU did not lead to the subsequent development of a change in motor impulsivity. Although not directly observed in this study, a possible reason for the abovementioned relationship must be discussed. To some extent, impulsivity can be conceptualised as a loss of control. According to Baumeister’s self-control theory (Baumeister, Vohs, & Tice, 2007), self-control refers to individuals’ ability to transform, inhibit and override their instinctual reaction in order to comply with an ideal, value or standard. The resource of self-control is limited and becomes exhausted if repeatedly used. A habitual Internet user might use most of their strength to control their behaviour in alignment with social rules and standards. Sometimes, individuals can feel exhausted by pressures from various sources. For an individual with strong motor impulsivity, additional self-control resources are consumed, resulting in the depletion of self-control and leading to PIU. Gender difference Regarding the gender difference in the predictive relationship between motor impulsivity and PIU, we found that motor impulsivity levels increase subsequent PIU levels for females; however, no cross-lagged relationship was found for males. A possible explanation is that the influence of previous PIU levels on subsequent PIU is greater for males than for females. Therefore, the relative strength of the predictive effect of previous motor impulsivity on PIU after 2 years is trivial for males. In other words, because male college students have fairly stable PIU levels across time, the influence of previous impulsivity levels on subsequent PIU was not obvious. The findings support our hypothesis of gender difference and are consistent with the view that the relationship between motor impulsivity and PIU is not straightforward. There is still much to learn about why the predictive power of the direct effect of motor impulsivity on subsequent PIU is not significant for males. Limitations and future studies Several limitations should be considered when interpreting these findings. With regard to the measurement, a weakness in the PIU scale lies in the use of only five items to measure the core symptoms, as this scale neglects behavioural consequences (e.g., lifestyle changes or functional impairments). Because all the information was obtained from self-reported measurement tools, a more thorough understanding of impulsivity and PIU may require other methods, such as behavioural laboratory measures (Dougherty, Mathias, Marsh, & Jagar, 2005)
for impulsive behaviour, a psychological diary or observations of the underlying process of PIU. Moreover, it should be noted that only a non-clinical sample was adopted in this study; therefore, the results may not apply to other samples, particularly samples of individuals who are diagnosed with Internet-related disorders. This study included a time interval of four semesters between the two data collection time points. Future studies could include data from more than two time points to conduct more elaborate analyses of time-related effects (e.g., an accelerating effect, a growth curve effect or a longitudinal mediation effect). Future studies should also consider other proximal of distal factors that may associated with PIU (e.g., other personality traits, trauma, media exposure) as mediators or moderators of the relationship between motor impulsivity and PIU. CONCLUSION This study provides researchers and practitioners with valuable insights into impulsivity, including its structure, its temporal relationship with PIU and associated gender differences. Researchers might benefit from this new knowledge and the suggested directions for future research. Practitioners might implement a programme for general college students to approach the issue of impulse control. To develop impulse control, techniques for managing impulses, such as enhancing self-awareness and delaying action on impulses, could be introduced. A training programme that aims to improve individuals’ willpower for self-control could also be an effective intervention for PIU. Our findings aim to not only highlight the issue of impulsivity and PIU in colleges but also initiate societal reflection on the future use of electronic media (Aboujaoude, 2010). Manuscript received March 2015 Revised manuscript accepted September 2015
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