Classes of trajectory in mobile phone dependency and the effects of negative parenting on them during early adolescence

Abstract

The aim of this study was to identify the classes of trajectory in mobile phone dependency using growth mixture modeling among Korean early adolescents from elementary school to the middle school transition. The effects of negative parenting on determining the classes were also examined. The participants were 2,378 early adolescents in the Korean Children and Youth Panel Survey. Three classes of trajectory in mobile phone dependency were found. Adolescents who were highly dependent on mobile phones were very few and most Korean adolescents were not serious with regard to mobile phone dependency. Although there was a large difference in mobile phone dependency among three classes at the very early period of adolescence, it decreased over time and all adolescents showed moderate level of mobile phone dependency. In addition, a multinomial logistic regression analysis showed that negative parenting was a significant predictor of determining the classes. The findings provided important implications for intervention and prevention of mobile phone dependency during early adolescence.

Keywords

early adolescence growth mixture modeling Korean adolescents mobile phone dependency parenting

As the age of those who get their first mobile phone gets younger and adolescents become more dependent on their phones, researchers have examined various factors that affect mobile phone dependency (MPD) and the deleterious effects of MPD on developmental outcomes during adolescence (Augner & Hacker, 2012; Bianchi & Phillips, 2005; Jun, 2016; Lepp, Barkley, & Karpinski, 2014; Seo, Park, Kim, & Park, 2016). However, little research has been done regarding whether there are different developmental trajectories of MPD during adolescence, as well as the factors determining the different trajectories. Given that the detrimental effects of MPD are especially serious when adolescents experience MPD during early adolescence (Jun, 2015; Wiart et al., 2005) and early adolescence is a period of change and developmental vulnerability, when many emotional and behavioral problems begin and increase (Wang, Brinkworth, & Eccles, 2013), it is crucial to identify the different pathways of MPD in early adolescence and to find important factors that influence MPD to prevent future adverse effects.

MPD refers to the phenomenon of using a mobile phone too much and losing control of mobile phone use despite the knowledge of negative outcomes (Han & Hur, 2004; Lu et al., 2011). MPD includes four symptoms such as dependence, tolerance, anxiety, and withdrawal of mobile phone. Thus, MPD and mobile phone addiction often are used interchangeably in the literature. However, the term mobile phone dependency is used in this study due to the reasons below. The term mobile phone addiction implies pathology in a medical model. The measure used for MPD in this study reflects MPD, rather than mobile phone addiction. In addition, the term MPD has been used more frequently in the literature than mobile phone addiction (Lee et al., 2016).

Due to the advances of statistical techniques and the increase in longitudinal data, some researchers have examined the developmental changes of MPD (Bae, 2014; Jun, 2016; Kim & Hong, 2014; Kim & Yang, 2014). However, all of them examined middle school students who already actively used mobile phones. Given that the starting age of mobile phone use is getting lower, these studies had limitations in regard to revealing the early period of MPD and what factors influence this period. In addition, most of them used a growth curve model, which examines individual differences based on an average growth curve (Bae, 2014; Kim & Hong, 2014; Kim & Yang, 2014). However, latent growth models are not appropriate for identifying the various types of trajectories in MPD because the heterogeneity of trajectories is not taken into account. Some adolescents may rarely rely on their mobile phones, while some may have increased or decreased MPD. Particularly, the differences among classes may be further differentiated at the early period of mobile phone use. Thus, a more advanced statistical method is needed to divide adolescents into several classes according to their trajectories in MPD.

A few studies have explored the classes of trajectories in MPD using statistical methods to identify several different groups. Ha (2014a) used a latent class growth analysis to identify the subtypes of changes in MPD among Korean adolescents aged 14- to 17-years-old participating in the Korean Children and Youth Panel Survey. Ha (2014a) divided the adolescents into three subtypes of MPD trajectory: A middle-level group (43.4%), a low-level increasing group (45.4%), and a high-level decreasing group (11.2%). The most important findings of Ha’s study were that a lot of adolescents were involved in a low-level increasing and middle-level groups and three classes were returning to convergence at the middle level at the final time point. These results suggested that the MPD of adolescents in transition from junior high school to high school gradually increased. In addition, Lee and Chung (2016) classified Korean high school students into four subgroups –– high-level dependency (61.2%), middle-level dependency (26.8%), low-level dependency (5.4%), and low-dependency efficiency (6.6%) –– with respect to MPD. These results showed that many high school students in Korea depended heavily on mobile phones. Although these findings contributed to show different pathways of MPD among adolescents, they had limitations in revealing the development of MPD in the early period of using mobile phones.

A lot of studies have examined the factors that influence MPD during adolescence. Psychological factors such as depressive symptoms, self-esteem, and social withdrawal are the most studied predictors of MPD (Augner & Hacker, 2012; Bickhan, Hswen, & Rich, 2015; Ha, 2014a; Jun, 2015, 2016). Compared to psychological factors, relational factors have been overlooked relative to their importance. However, given that most addictive symptoms come from interpersonal relationships (Suler, 2004) as well as the fact that a common motivation for mobile phone use is to help people communicate and to strengthen relationships (Lepp et al., 2014), relational factors should be investigated when examining MPD. Among various relational factors, parenting styles are focused on in this study because parenting is a fundamental environment in adolescents’ socialization and has critical influence on various kinds of problem behavior such as internalizing and externalizing behavior (Hart, Newell, & Olson, 2003), and internet addiction (Floros & Siomos, 2013; Szwedo, Mikami, & Allen., 2011).

Positive parenting has been referred as parental warmth, acceptance, consistency, and emotional support (Galambos, Barker, & Almeida, 2003; Perris et al., 1980) whereas negative parenting has been considered as parental control (e.g., over intrusiveness, over-expectations, inverse being parental support of autonomy) (Grolnick & Pomerantz, 2009), parental rejection, inconsistency, and over protection (Vera, Granero, & Ezpeleta, 2012). Recently, researchers have examined the effects of parenting styles on MPD (Bae, 2014; Lee et al., 2016). For example, parental support, good communication with parents, and helping adolescents pursue autonomy were negatively associated with MPD (Yoo & Kwon, 2011). Some studies showed that higher parental supervision and affection were closely related to lower MPD (Chang, Song, & Cho, 2011; Fletcher, Steinberg, & Williams-Wheeler, 2004). However, negative parenting, such as inconsistency, over-expectation, and over-intrusiveness in parenting was positively related to MPD (Bae, 2014). In addition, higher levels of parental restriction and psychological control were a risk factor for MPD (Kim & Lim, 2014; Lee et al., 2016). Lower parental knowledge was also an important predictor of MPD (Stattin & Kerr, 2000). Although these findings showed the associations between negative parenting and MPD, little research is provided concerning how negative parenting affects the different pathways of MPD among adolescence longitudinally.

The aim of this study was to examine the classes of trajectory in MPD among early adolescents and the effects of negative parenting on determining the classes using five-year longitudinal data. For a more sophisticated analysis, gender and purposes for mobile phone use were used as covariates– two factors known to be most influential in MPD in the literature (Jun, 2015; Kim, 2012; Kim & Hong, 2014; Kim & Seo, 2012; Oksman & Turtiainen, 2004; Rautiainen & Kasesniemi, 2000; Seo, 2016). Based on the literature, two research questions have been established: (a) What are the classes of trajectory in MPD during early adolescence? (b) Does negative parenting influence the classes of trajectory in MPD during early adolescence, even when gender and purposes for mobile phone use are controlled?

Methods

Data and participants

In this study, longitudinal data from the Korean Children and Youth Panel Survey (KCYPS) were analysed. The KCYPS was conducted by the National Youth Policy Institute collected data on family, school, community, mass media, and cultural context, as well as developmental behaviors in Korean children and adolescents. This study used the five-year data (from 2010–2014) from the KCYPS.

The participants completed annual surveys administered by trained staff at the 4th grade (wave 1) through the 8th grade (wave 5). The samples for this study consisted of 2,378 adolescents (1,242 boys and 1,136 girls) in the first year and 2,264; 2,219; 2,092; and 2,070 adolescents in the second, third, fourth, and fifth years, respectively.

Measures

Mobile phone dependency (MPD)

MPD was measured using the seven items established in the KCYPS. For each item, participants reported how much they depended on their mobile phones (e.g., ‘I feel anxious when I don’t have a mobile phone with me’, ‘I get easily bored and irritable when I am alone without a mobile phone’, and ‘I cannot live without a mobile phone because I feel uncomfortable without a mobile phone’). Each item was rated on a scale from 1 (strongly yes) to 4 (strongly no). Response scales were reversed to maintain a consistent scale from unfavorable to favorable. Cronbach’s alpha coefficients were 0.83 at wave 1, 0.88 at wave 2, 0.89 at wave 3, 0.90 at wave 4, and 0.88 at wave 5.

Negative parenting

The 21 items from Huh’s Parenting Attitude Inventory (Huh, 2004) were asked to measure perceived parenting behavior in KCYPS. The parenting behavior scale consists of affection, monitoring, rational explanation, inconsistency, over-expectation, and over-intrusiveness. Among these subscales, inconsistency, over-expectation, and over-intrusiveness were considered as negative parenting.

To measure inconsistent parenting, participants responded to three questions asking whether their parents reared them in inconsistent ways (e.g., ‘My parents sometimes scold and sometimes don’t, even though it is same situation’). Over-expectation was measured by four items (e.g., ‘I feel uncomfortable because my parents’ expectations are beyond my ability all the time’). Over-intrusiveness was also measured using four items (e.g., ‘My parents become involved, even over trifles’). The negative parenting scale was measured at the 4th grade and had a total of 11 items. All response scales varied from 1 (strongly yes) to 4 (strongly no). All items were scored in reverse so that higher numbers indicate greater perceived negative parenting. Cronbach’s alpha coefficients were 0.83 at wave 1.

Covariates

Gender was recorded as 0 or 1 for female and male, respectively. The purposes for mobile phone use included three components: Use for contacting family members, use for contacting friends, and use for entertainment. These constructs were further divided into sub-items based on the results from empirical studies (Ha, 2014b; Lee et al., 2012; Seo, 2016). Use for contacting family members was measured by adding a set of two items (‘Calling family members’ and ‘Text messaging family members’; α = 0.58). Use for contacting friends was measured by summing up a set of two items (‘Calling friends’ and ‘Text messaging friends’; α = 0.82), and use for entertainment was measured by one item (‘Games and entertainment’). These items were measured at grade 4 (wave 1) and rated on a scale from 1 to 4 (1 = often use, 2 = sometimes use, 3 = rarely use, 4 = never use). Response scales were reversed to maintain a consistent scale on the less frequent to frequent continuum.

Analysis

A person-centered approach was adopted to identify the trajectories of MPD. Growth mixture modeling (GMM) was used to classify individuals into distinct groups based on similar trajectory patterns of MPD from the 4th grade to the 8th grade. GMM is accomplished by using latent trajectory classes, which allow different groups of individuals to vary around different mean growth curves. The results of GMM yield individual growth models for each class and estimates of each individual’s most likely class membership. GMM is estimated by maximum likelihood using an expectation-maximization algorithm. This method allows for the use of data from all participants, even those with data at only one point, to determine parameter estimates and their standard errors.

The number of latent classes was determined by fit indices such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Lo-Mendel-Rubin Adjusted LRT statistics (LMR-LRT). The model with a low AIC and BIC value and a significant LMR P-value comparing the k and the k-1 class model would be more appropriate (Nylund, Asparouhov, & Muthén, 2007). In addition to fit indices, parsimony, theoretical justification, and interpretability were considered to determine the number of classes (Bauer & Curran, 2003; Muthén, 2003). After determining the most appropriate number of MPD trajectory classes, we examined the predictors through multinomial logistic regression analysis. In Figure 1, a growth mixture model depicting prediction of latent class membership was presented.

Figure 1.

Representation of a growth mixture model showing prediction of latent class membership.

Analyses for descriptive statistics and logistic regression were conducted using IBM SPSS 18.0. GMM was implemented in Mplus 5.2.

Results

Estimation of classes of MPD trajectories

GMM was used to test from one-class to five-class models of MPD trajectories. Participants were classified according to initial value and the change patterns of MPD. As shown in Table 1, the AIC for the unconditional model decreased from one-class model to five-class model and the BIC decreased until we added a fifth class, at which point it increased. And the three-class model has a significant LMR-LRT P-value when compared to the 1 less class model. Taking these together, with the criteria of goodness of fit, parsimony, and interpretability, the three-class solution provided the best fit model. In addition to these considerations, it had high average posterior probabilities for class membership, ranging from 0.82 to 0.87, and consisted of more than 3% of the total count in each class (70.6, 26.1, and 3.3%).

Table 1.

Determining the number of classes.

Models	LMR- LRT	AIC	BIC	Class counts and proportions
Models	LMR- LRT	AIC	BIC	Class 1	Class 2	Class 3	Class 4	Class 5
One-class		17785.59	17842.98	2,296(100%)
Two-class	178.02***	17605.90	17680.51	188(8.2%)	2,108(91.8%)
Three-class	120.32***	17486.40	17578.23	1,621(70.6%)	599(26.1%)	76(3.3%)
Four-class	40.57	17450.09	17559.13	1,511(65.8%)	647(28.2%)	103(4.5%)	35(1.5%)
Five-class	40.24	17441.01	17567.27	107(4.7%)	1,471(64.1%)	46(2.0%)	635(27.7%)	37(1.6%)

As Figure 2 indicated, there were differences in the initial value and the change pattern of MPD among the three class groups. Each trajectory class was named according to the initial value and the change type of MPD. Among the three trajectory classes, the Low Dependency-Increasing class is the largest (70.6% of total sample), followed by the Middle Dependency-Stability class (26.1% of total sample), and the High Dependency-Decreasing class (3.3% of total sample). Those adolescents in the Low Dependency-Increasing trajectory exhibited little MPD at the 4th grade but gradually increased until the 8th grade over time. Those in the Middle Dependency-Stability trajectory started out with a medium probability of MPD at the 4th grade and remained in a stable pattern until the 8th grade. Finally, the High Dependency-Decreasing trajectory showed that the initial value of MPD started out with at a high level but steadily decreased over time. And this trajectory still exhibited a higher level of MPD than those of any other trajectories throughout early adolescence.

Figure 2.

Growth trajectories of MPD for the three-class latent variable growth mixture model.

Evaluating predictors of MPD trajectory class membership

A multinomial logistic analysis was conducted to identify the effects of negative parenting on the latent class membership according to trajectories of MPD. In this analysis, gender and purposes for mobile phone use were included as covariates. Table 2 showed the model estimates for the variables predicting the classes of trajectory in MPD.

Table 2.

Estimates for predictors of MPD trajectory class membership (N = 2,296).

Variables measured at Grade 4	Versus Low Dependency-Increasing		Versus Middle Dependency-Stability
Variables measured at Grade 4	Middle Dependency- Stability	High Dependency- Decreasing	High Dependency- Decreasing
Gender	−0.26*	−0.64*	−0.36
Use for contacting family members	0.10	0.58	0.64
Use for contacting Friends	0.42***	0.49*	0.20
Use for entertainment	0.45***	0.91***	0.42**
Negative parenting	0.79***	1.68***	1.33***

Note: N = 1,621, 599, and 76 for the Low Dependency-Increasing, the Middle Dependency-Stability, and the High Dependency-Decreasing classes, respectively.

p <0.05; **p <0.01; ***p <0.001.

Girls were more likely to be involved in the Middle Dependency-Stability and High Dependency-Decreasing classes than boys. Adolescents using mobile phones to communicate with their friends were more likely to be in the Middle Dependency-Stability and High Dependency-Decreasing classes than the Low Dependency-Increasing class. Youths using mobile phones for entertainment were more likely to be in the High Dependency-Decreasing class than in the Middle Dependency-Stability class membership, as well as more likely to be in either of the Middle Dependency-Stability and High Dependency-Decreasing trajectories relative to the Low Dependency-Increasing trajectory.

As compared to the Low Dependency-Increasing class, the adolescents with higher negative parenting were more likely to be involved in the Middle Dependency-Stability and High Dependency-Decreasing classes and more likely to be in the High Dependency-Decreasing class than the Middle Dependency-Stability class.

Discussion

The purpose of this study was to identify the classes of trajectory in MPD during early adolescence, as well as to examine the effects of parenting on determining these class trajectories. KCYPS data, which were representative data for Korean adolescents, were used.

Three classes in the five-year trajectories of MPD were identified: The Low Dependency-Increasing class who had low level of MPD at the beginning and whose MPD showed an increase, the Middle Dependency-Stability class who had middle level of MPD and kept it stable for five years, and the High Dependency-Decreasing class who had high level of MPD at the beginning and whose dependency showed a decrease during five years. Most adolescents were involved in the Low Dependency-Increasing class and the Middle Dependency-Stability class. Very few adolescents were in the High Dependency-Decreasing class. The results suggested that most Korean adolescents did not have serious problems with regard to MPD during early adolescence. Although there was a big difference in MPD at the 4th grade among the three classes, all classes tended to converge to the moderate level of MPD over time. That means, there was a particular group of adolescents with severe MPD at the 4th grade, but there was no such group at the 8th grade and they all showed the medium level of MPD later.

The results of this study were very similar to Ha’s (2014a) study in the sense that they also found the same three types of changes of MPD and the convergence to the moderate level of MPD with age. However, the proportion of adolescents in the group was different between the two studies. In this study, the number of adolescents in the Low Dependency-Increasing class was much higher than that of adolescents in the Middle Dependency-Stability class and the High Dependency-Decreasing class. On the other hand, in Ha’s (2014a) study, the ratios of the low-level increasing group and middle-level group were similar and the ratio of the high-level decreasing group was higher than that of this study. In addition, while the Low Dependency-Increasing class was much more common in this study, Lee and Chung’s (2016) study that examined high school students showed the high dependency group to be much more common. Based on the previous findings and these results, despite of the variability of in their level of MPD at the very early period of adolescence, the variability may diminish as they grow up and all adolescents have the moderate level of MPD at some point.

The findings of this study suggest that even if there were few highly dependent phone users at the 4th grade, their MPD may decrease without any intervention at the 8th grade. However, this interpretation is made very cautiously. This is because even if students in the High Dependency-Decreasing class did not have serious problems in terms of MPD at the 8th grade, we cannot rule out the possibility that MPD at the 4th grade could change into various types of problem behaviors given that early MPD had adverse effects on various kinds of other developmental outcomes (Jun, 2015; Wiart et al., 2005). Thus, great attention and interventions should be given to the students in the High Dependency-Decreasing class. In addition, since most middle school students showed the moderate level of MPD, most students may be exposed to the risks of problems caused by MPD in Korea. Thus, MPD programs and education are needed for most middle school students at the mid adolescence.

The effects of negative parenting on the subtypes of MPD trajectories were also examined. Negative parenting, including inconsistency, over-expectation, and over-intrusiveness in parenting, was found to have significant influence on the classes of MPD trajectories during early adolescence. Specifically, adolescents who perceived higher negative parenting were more likely to be involved in the Middle Dependency-Stability class and the High Dependency-Decreasing class. The findings were partly consistent with the significant effects of inconsistent, over-intrusive, and over-expectant parenting on MPD (Bae, 2014; Lee et al., 2016) and the positive influence of parental supervision on lower MPD (Bae, 2014; Chang et al., 2011; Fletcher et al., 2004). However, these findings were the results when the time change was not considered. When the effects of early negative parenting on MPD from a longitudinal point of view were examined, the exposure to negative parenting did not make MPD worse until they were at the 8th grade. This was because the level of MPD in the High Dependency-Decreasing class decreased as students grew up and they had moderate level of MPD at the 8th grade. This finding of the current study suggested that there would be a limitation to the longitudinal effects of early negative parenting on MPD during adolescence. For these results, it is speculated that other environmental factors such as peer relationships, the rapid penetration of mobile phones, and a lot of class time in Korean middle school might affect MPD. However, given that MPD at the 4th grade could change into other problem behaviors at the 8th grade, the interpretation for the limitation of longitudinal effects of early negative parenting on MPD is made very cautiously. Therefore, particularly for adolescents with the High Dependency-Decreasing class, MPD and other problem behaviors should be examined together in future studies. Taken together, the findings suggest that negative parenting might be a risk factor of determining the high and moderate MPD groups at the very early adolescence, but early negative parenting would not have long-term adverse effects on later MPD during adolescence.

This study has several limitations. Although negative parenting may change over time, negative parenting was considered as time-independent variables in this study. Thus, future studies are needed to examine the associations between changes in negative parenting and changes in MPD. In addition, all scales in this study were self-reported measures to examine study variables. Thus, more sophisticated methods such as interviews or observations would be necessary for future studies. Third, the internal consistency of use for contacting family members was low. Therefore, more validated measure of use for contacting family members would be necessary in future studies. Lastly, although this study examined the unidirectional effects of negative parenting on MPD over time, bidirectional associations between negative parenting and MPD may be possible. Thus, it is necessary to reveal bidirectional associations between them in the future.

Despite these limitations, this study contributed to elucidate the developmental course of MPD and the effects of negative parenting on them. Based on the findings, the practical implications are as follows. The High Dependency-Decreasing students need to be selected in the upper grades of elementary school and MPD preventive education and programs should be necessary for them based on the effects of negative parenting on the classes of trajectory in MPD in this study. In particular, school psychologists could develop a parenting program and education for their parents. In addition, students in the Low Dependency-Increasing class and the Middle Dependency-Stability class should be educated on desirable use of their mobile phone, informing them of the risks of MPD. Because the participants of this study were Korean adolescents, it is hard to apply the results of this study to the international situation. However, given that Korean mobile phone penetration rate is high and related technology is advanced, the findings of the current study might provide insightful information across an international context. Internationally, as the age of mobile phone usage has got lower in recent years, it is important to announce the risk of MPD from early adolescence and to provide education on the proper use of mobile phones. Especially, it is important to screen high-MPD students, inform their parents of the adverse impact of negative parenting on MPD, and take appropriate interventions in early adolescence.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Mijung Seo is an Assistant Professor of the Faculty of Human Ecology & Welfare at Jeju National University in Jeju, South Korea. She worked as a Research Professor and a Counseling Psychologist at Pusan National University. Her research interests include parenting and behavior problems such as aggression, bullying, and mobile phone dependency.

Eunsil Choi is an Assistant Professor of the Department of Family and Housing Studies at Yeungnam University in Gyeongsan, South Korea. She completed her PhD degree at the University of Illinois at Urbana Champaign. Her current research focuses on attachment, parenting, family well-being, and problem behaviors.

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