A configurational investigation of smartphone use disorder among adolescents in three educational levels

Journal Pre-proofs A configurational investigation of smartphone use disorder among adolescents in three educational levels Qiufeng Gao, Ge Jia, En Fu, Yunusa Olufadi, Yanlin Huang PII: DOI: Reference:

S0306-4603(19)30351-X https://doi.org/10.1016/j.addbeh.2019.106231 AB 106231

To appear in:

Addictive Behaviors Addictive Behaviors

Received Date: Revised Date: Accepted Date:

25 March 2019 18 November 2019 18 November 2019

Please cite this article as: Q. Gao, G. Jia, E. Fu, Y. Olufadi, Y. Huang, A configurational investigation of smartphone use disorder among adolescents in three educational levels, Addictive Behaviors Addictive Behaviors (2019), doi: https://doi.org/10.1016/j.addbeh.2019.106231

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A configurational investigation of smartphone use disorder among adolescents in three educational levels

Qiufeng Gaoa Ge Jiaa En Fua* Yunusa Olufadib Yanlin Huanga

Author affiliations: a Shenzhen

University, Shenzhen, China

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b

University of Memphis, Memphis, USA

*

Corresponding author: En Fu. Address: Science and Technology Building, Room L3-1311,

Shenzhen University, Shenzhen, China, 518060. Email: [email protected].

A configurational investigation of smartphone use disorder among adolescents in three educational levels

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Abstract Introduction: Smartphone use disorder in youth was associated with severe physical, psychological, and financial problems. Based on the ecological system theory of child development, this study examined a wide range of psychosocial characteristics in elementary-, middle-, and high-school adolescents with high scores on smartphone use disorder. Methodology: Existing research, which mainly adopted regression-based analytical techniques, found that gender, self-control, sensation seeking, loneliness, anxiety, perceived parent-adolescent relationship, and perceived parental monitoring are associated with smartphone use disorder. To complement traditional variablecentered approaches, the current study adopted a person-centered approach, fuzzy-set Qualitative Comparative Analysis (fsQCA) procedures, to examine adolescents with smartphone use disorder. Results: The fsQCA procedure revealed four, nine, and thirteen distinct configurations that contributed to smartphone use disorder in adolescents for elementary school students, middle school students, and high school students respectively. A comparison across the three educational levels revealed four differences and two similarities. The results suggest that different groups of adolescents might be at risk for smartphone use disorder across the three educational levels. Conclusions: The fsQCA procedures generated solutions with satisfactory coverage and consistency. This demonstrates the promising value of fsQCA for researching smartphone use disorder and other behavior problems in adolescents. The results suggest that educators and mental health practitioners should consider educational level when helping adolescents with smartphone use disorder.

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Keywords: smartphone use disorder, adolescence, fuzzy-set qualitative comparative analysis, educational levels

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1. Introduction As smartphone became increasingly popular among adolescents (Long et al., 2016; Lopez-Fernandez, Honrubia-Serrano, Freixa-Blanxart, & Gibson, 2014), many educators and parents are concerned with smartphone-related problems such as addictive use. Psychologists defined addiction as “any activity, substance, object, or behavior that has become the major focus of a person’s life to the exclusion of other productive activities” (Dlodlo, 2015, p.209) and the person compulsively engage in the activity despite of the “devastating consequences on the individual’s physical, social, spiritual, mental and financial well-being” (Young, Yue, & Ying, 2011, p.3). Addictive use of smartphones (or smartphone addiction or problematic smartphone use) in children and adolescents has become a public health issue because it was found associated with severe physical problems (Kamibeppu & Sugiura, 2005; Sandström, Wilén, Oftedal, & Hansson Mild, 2001; Söderqvist, Carlberg, & Hardell, 2008), psychological problems (Chen et al., 2016; Elhai, Dvorak, Levine, & Hall, 2017), and financial problems (Billieux, van der Linden, & Rochat, 2008; James & Drennan, 2005). In accordance with the I-PACE model or specific internet-use disorders (Brand, Young, Laier, Wölfling, & Potenza, 2016), the current study uses the term smartphone use disorder to conceptualize smartphone-related behaviors that featured loss of control, withdrawal, escape negative emotions, and low productivity (Leung, 2008; Young, 1996). While existing research suggests that various environmental and psychological factors are associated with smartphone use disorder in adolescents, we

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do not know the profiles (as an integrated whole of relevant factors) of adolescents with different characteristics display smartphone use disorder behaviors. The analysis in the current study adopted variable-based as well as person-based analytic techniques. 1.1. The Ecological System Influence on Smartphone Use Disorder Based on the ecological system theory of child development, the developmental outcome of children and adolescents is influenced by five nested environmental systems (Bronfenbrenner, 1977, 1989). First, the adolescent is at the center of the microsystem. Individual characteristics such as gender, self-control, sensation seeking, loneliness, and anxiety are determinants of developmental outcomes. Gender is often examined in smartphone use disorder research because males and females often engage in very different smartphone activities (Roberts, Yaya, & Manolis, 2014) and different smartphone activities led to different consequences of smartphone use (Griffiths & Szabo, 2014). Similar to other addictive behaviors (Sayette, 2004), smartphone use disorder is often found in people with low levels of self-control (Yun, Kim, & Kwon, 2016). As a mechanism of seeking enjoyment and avoiding boredom (Elhai, Vasquez, Lustgarten, Levine, & Hall, 2018; Zhang, Chen, & Lee, 2014), sensation seeking was consistently found associated with smartphone use disorder in late adolescents and adults (De-Sola, Talledo, Rubio, & de Fonseca, 2017; Divband, 2013; P. Wang et al., 2018) (De-Sola, Talledo, Rubio, & de Fonseca, 2017; Divband, 2013; Wang et al., 2018). As a typical pathological trait, anxiety and related symptoms are found as reliable predictors for smartphone use disorder (Elhai, Levine, & Hall, 2019). Finally, loneliness is a personal characteristic associated with three of the six subscale scores

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and the total scores of Smartphone Addiction Scale (Darcin et al., 2016). Second, the microsystem includes the immediate environments that the child has direct interaction with (Johnson, 2010). For adolescents who have not gained financial independency, family environment is crucial for their survival and development. Problematic behaviors are lower in minors who received adequate parental monitoring (Dishion & McMahon, 1998). However, research also showed that adolescents with low effortful control are more likely to engage in addictive internet use when perceived parental monitoring was high (Ding, Li, Zhou, Dong, & Luo, 2017). This indicates that the effect of perceived parental monitoring on adolescent smartphone use disorder might be moderated by self-control. The family could also influence adolescent behavior through parent-adolescent interactions (Trivette, Dunst, & Hamby, 2010). Relationship with parents is important for meeting the psychological need for relatedness in adolescents (Leong, Lee, & Chow, 2018), lack of which could lead to problematic behaviors such as internet addiction (Elhai, Levine, et al., 2018). Adolescents with a high risk of smartphone use disorder reported more issues in parentadolescent communication (Lee et al., 2016). The parent-child relationship also interacts with parental monitoring to differentially influence addictive technology use in adolescents (Ding et al., 2017). 1.2. A configurational approach to the ecological system framework Configurational approach focusses on the configuration of a set of causal conditions instead of independent causal conditions thus can better capture complex interaction effects. Based on the ecological systems theory, Ecological Techno-

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Microsystem suggests interactions among factors among and across systems (Johnson, 2010). For instance, the effect of sensation seeking on smartphone use disorder depends on the levels of social support the child perceived (P. Wang et al., 2018). Also, depending on the factors controlled, parental monitoring could have positive (Lee & Ogbolu, 2018), negative (Jang & Ryu, 2016), or no relationships (Lee, Lee, & Lee, 2016) with internet-related addiction in adolescents. Both theoretical and empirical evidence suggests that factors in the ecological techno-microsystems do not contribute independently to smartphone use disorder. Therefore, the current study adopted a configurational approach to examine the combined effect of several conditions on adolescent smartphone use disorder. The complexity theory asserts that, in complex human behaviors, multiple paths (i.e., configuration or combinations of factors) could lead to the same outcome and the same condition of a factor could lead to different outcomes when combining with different conditions of other factors (Woodside, 2014). Equifinality of ecological system configuration implies the possibility that different configurations of ecological system factors are associated with the same outcome (i.e., the same level of smartphone use disorder). Existing research has adopted latent class analysis to characterize subgroups of problematic smartphone users (Elhai & Contractor, 2018; Kim, Nam, Oh, & Kang, 2016; Mok et al., 2014), which suggests that different configurations of individual characteristics might lead to the same levels of smartphone use disorder. Hence, the association between ecological system conditions and measure of smartphone use disorder is more likely to be equifinal rather than unique.

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1.3. Similarities and Differences Across Educational level Existing research that explored factors underlying smartphone use disorder mainly adopted correlational approaches and found that three factors consistently predicted addictive smartphone use across adolescents of all ages. First, adolescents with relatively low self-control reported relatively high smartphone use disorder (Chun, 2018; Jang & Ryu, 2016; Jeong, Kim, Yum, & Hwang, 2016; Liu et al., 2018; Yun et al., 2016). The bivariate correlation coefficient between self-control and smartphone use disorder was -0.46 in elementary school students (Jeong et al., 2016) and -0.39 in middle- and high- school students (Liu et al., 2018). Second, anxiety is positively associated with smartphone use disorder in adolescents of all ages except for certain populations (Lu et al., 2011). Bivariate association between anxiety and mobile phone use was 0.30 in 6-graders (Kim, Lee, & Choi, 2015) and 0.40 in middle-schoolers (Lee et al., 2018). High school students with excessive mobile phone use behaviors reported higher interpersonal anxiety than peers (Ha, Chin, Park, Ryu, & Yu, 2008). Third, in late childhood and early adolescence, a good parent-adolescent relationship could help prevent smartphone overuse (Kim et al., 2015). In mid- and late- adolescence, attachment to parents is the only type of attachment that significantly predicted smartphone use disorder (Lim & Lee, 2017). The stage theory of adolescent development asserts that optimal adolescent experience in schools and at home is achieved when there is a good stage-environment fit (Eccles et al., 1993). Therefore, the same factors should have different contribution to maladaptive development outcomes in adolescents of different developmental stages.

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Existing research found different characteristics of students with smartphone use disorder in elementary-, middle-, and high-schools. Students with high scores on smartphone use disorder in elementary schools reported relatively low self-control or parental-control (Bae, 2015; Jeong et al., 2016) whereas in middle-schools (Lee et al., 2016; Wang et al., 2017) and high-schools (Andreassen, Pallesen, & Griffiths, 2017; Mei, Yau, Chai, Guo, & Potenza, 2016) students with high scores on smartphone use disorder differed from other students on psychological and interpersonal factors. 1.4. fsQCA Versus Correlation-based Techniques Correlation-based techniques and fsQCA complement each other in data analysis because of the differences in these two approaches (Schneider & Wagemann, 2010). Correlation-based analyses are advantageous in detecting the net effect of independent variables and concepts such as degrees of freedom are closely related to the problem of collinearity. In regression analysis, for example, combined effects of variables are dealt with through interaction terms in the regression model. However, regression is limited in cases with multiple interaction terms with multiple variables because there are not enough degrees of freedom left. fsQCA, on the other hand, allows all possible combinations of all causal conditions because the complexity theory does not assume independence among conditions (Eng & Woodside, 2012). While latent profile analysis and cluster analysis search for groups of individuals with similar profiles, fsQCA searches for configurations that could explain the same outcome (Eng & Woodside, 2012). Traditionally, fsQCA procedures have been used in business research fields such

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as management, hospitality, customer behavior, and marketing. Recently, fsQCA has been adopted by more and more behavior researchers outside of the business world to examine groups such as heavy drinkers (Eng & Woodside, 2012), students with good academic performance (Olufadi, 2015), students with high digital skills (Sergis, Sampson, & Giannakos, 2018), students who intend to adopt mobile devices for learning (Pappas, Giannakos, & Sampson, 2017), students who reported positive attitudes towards educational video games (Martí-Parreño, Galbis-Córdova, & MiquelRomero, 2018), marketers who reported high quality of work life (Tho, 2018), students who selected certain courses (Jain & Jain, 2018), and adolescents with high perceived stress (Villanueva, Montoya-Castilla, & Prado-Gascó, 2017). Specifically, the perceived stress study (Villanueva et al., 2017) adopted rigorous fsQCA procedures (Charles C. Ragin, 2008) to examine the role of emotional intelligence in predicting biological and perceived stress in adolescents. Using the direct method of calibration, Villanueva and colleagues first list all combinations of causal conditions using a truth table. While editing the truth table, the frequency cutoff was set as 1 and the consistency cutoff was set as 0.80. Second, they adopted the fsQCA technique to generate three possible solutions (i.e., complex solution, parsimonious solution, and intermediate solution) and they reported the intermediate solution as recommended by Ragin (Ragin, 2008). Hierarchical regression revealed that relatively low emotional clarity and low hedonic balance were related to higher levels of perceived stress. The fsQCA solution revealed five configurations that contribute to high perceived stress. The authors then described and discussed the two most important configurations.

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The current study aimed to investigate conditions that contributed to high scores on smartphone use disorder in adolescents. The adoption of fsQCA is suitable in the current study because different psychosocial factors in adolescents have, instead of an added sum of individual net effects, complex integrated effects on developmental outcomes (Johnson, 2010). For example, as described in Section 1.1, parental monitoring could interact with effortful control and parent-child relationship to influence addictive technology use behavior in adolescents (Ding et al., 2017; Dishion & McMahon, 1998). Moreover, like other qualitative methods, fsQCA could present the exact situation of almost all cases instead of an estimation of the overall trend as could by correlation-based techniques. The current study was exploratory thus no hypothesis was formed or tested. We used both correlation and fsQCA procedures in the hope to explore relatively comprehensive information from the data. The fsQCA procedure is not common in psychology research and it has limitations mainly due to it not being an inferential statistic. The current study followed rigorous fsQCA procedures described in the literature (Fiss, 2011; Ragin, 2008; Rubinson, Universit, & Greckhamer, 2019). 2. Methods

2.1. Participants and Ethics Statement The predictive power of fsQCA can be inferenced from the consistency and coverage scores, which are related to the sample size, the number of causal conditions, and the type of research questions in concern. Since the fsQCA approach does not

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provide a simple algorithm or software to conduct power analysis, the current study estimated required sample size by referencing to existing research with similar outcome condition and similar number of causal conditions (Duarte & Pinho, 2019; Roy, Balaji, Quazi, & Quaddus, 2018; Su, Chiang, James Lee, & Chang, 2016). In the fall of 2017, 1769 students from grade five, six, seven, eight, ten, and eleven from six schools in a major China city were invited to participate in the current study. Three students reported not having or using a smartphone thus were excluded from further analysis, resulting in 1766 completed questionnaires. Sample characteristics are shown in Table 1. The present study was approved by the Ethics Committee of Shenzhen University. The study is a pencil-and-paper survey. Trained graduate students gave a detailed description of the study and standardized instruction orally through which the participants were informed of the purpose and procedures of the study. All the procedures guaranteed the generation of anonymized datasets. 2.2. Measures Smartphone use disorder was measured using the Mobile Phone Addiction Index (MPAI, Leung, 2008). The current study took a social development approach thus the Smartphone Use Disorder scale was used to measure developmental outcome of the participants instead of clinical diagnosis. High scores on the Smartphone Use Disorder measure indicates a higher tendency of addictive smartphone use. One item was used to measure gender with “1” indicating female and “0” indicating male. Selfcontrol was measured using a six-item scale (Kendall & Wilcox, 1979). Sensation

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seeking was measured using the BSSS-4 scale (Stephenson, Hoyle, Palmgreen, & Slater, 2003). Loneliness was measured using a loneliness scale (Jeong et al., 2016) that was based on the UCLA loneliness scale (Russell, Peplau, & Ferguson, 1978). Anxiety was measured using the anxiety domain subscale from the Inventory of Subjective Life Quality scale (Cheng, Gao, Peng, & Lifang, 1998). The Perceived parent-adolescent relationship was measured with a 16-item Chinese parent-adolescent relationship scale (Zhu, Zhang, Yu, & Bao, 2015) adapted from a widely-used English scale (Stattin & Kerr, 2014). Eight items were negatively poled items and were reversely coded before calculating the mean of all items. SPSS syntax of this reverse-coding procedure is included in Appendix G. One item was adopted to measure the extent of parental monitoring over adolescents’ smartphone use (Li, Feigelman, & Stanton, 2000). All raw scores were converted to membership scores as the firsts step of fsQCA (described in detail in section 2.3 below). The current study is part of a larger survey that surveyed adolescents’ smartphone use behaviors, psychological characters, parent-adolescent relationship, and subjective quality of life. Therefore, not all variables measured in the survey were included in the current study. 2.3. Theories and Calculations First, smartphone use disorder scores were correlated with gender, self-control, sensation seeking, loneliness, anxiety, parent-adolescent relationship, and perceived parental monitoring. The correlation tests were conducted in SPSS24.0 with all its default settings.

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Theoretically, the fsQCA procedures are built on set-theoretic relationships (Ragin, 2008). Two general analytic strategies in searching for commonalities are (1) examining cases with a given outcome to see whether they have shared causal conditions and (2) examining whether cases with shared causal conditions also exhibit the same outcome. Mathematically, the fsQCA procedures are built on Boolean algebra and include four main steps (Fiss, 2011). The first step in fsQCA procedures is calibration (Ragin, 2000). Similar to the calibration practice in chemistry, astronomy, and physics, the calibration procedure in fsQCA adjust measurements so that they conform to dependable known standards. A temperature of 20 degrees Celsius is easily interpretable because the measurement of temperature is agreed to be ranged between 0 degrees and 100 degrees (Byrne, 2013). Similarly, a calibrated anxiety measurement of 0.1 is easily interpretable as not quite anxious because in fsQCA all measurements are situated in between 0 (completely not in the set) and 1 (completely in the set). In correlational studies, “high anxiety is associated with high smartphone use disorder” means, in a representative sample, “participants whose anxiety scores were relatively high compared to other participants in this sample also had relatively high smartphone use disorder scores compared to other participants in this sample”. In fsQCA, “participants with high smartphone use disorder also reported high anxiety” means, in the current sample, “those who reported absolute high levels of smartphone use disorder also reported absolute high levels of anxiety”. In the current study, the cutoff for complete non-membership was the minimum possible score on each Likert scale; the

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cutoff for complete membership was the maximum possible score on each Likert scale; the cross-over point was the mid-point on each Likert scale. To make sure the results would not be inconsistent due to different calibration algorithms, the current study utilized the same calibration procedure (Ragin, 2008, pp. 89-94) for the fsQCA software (Appendix E) and the R package QCA (Appendix H). After calibration, a truth table was produced that list the frequency of all possible configurations of causal conditions (see Appendix B for an example of the Truth table). A frequency cutoff score was set so that configurations with a small number or no cases could be excluded from further analysis. For comparison purposes, the current study applied a frequency cutoff that suits our sample size (Fiss, 2011; Ragin, 2008). Therefore, configurations in the truth table with less than two cases were excluded from further analysis. Like frequency cutoff, a consistency cutoff is required to decide the criteria for “in” and “out” of the outcome set. Although the minimum recommended cutoff for consistency is 0.75 (Rihoux & Ragin, 2008), a relatively high consistency cutoff can reduce type I error (Dul, 2016). Similar to the way most statistical consistency tests determined its cutoff score for alpha level (i.e., 0.05) based on convention, the current study used 0.85 as the cutoff score for consistency so that results from the current study could be compared to those in existing research on similar behavior outcome with similar sample size (Pappas, Kourouthanassis, Giannakos, & Chrissikopoulos, 2016). The third step in fsQCA is to generate solutions based on the edited truth table. Further information about the procedures and guidelines for using the fsQCA software can be found on www.compasss.org. Two concepts are crucial for understanding fsQCA results and the

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procedures. Consistency of a configuration gauges the degree to which members in a supposed subset is also in the superset. Coverage of a configuration, by contrast, indicates the percentage of cases in the superset that is also in the supposed subset. As described by Woodside, consistency is analogous to the correlation coefficient and coverage is analogous to the coefficient of determination in correlation-based techniques such as regression (Woodside, 2013). Researchers suggested that the predictive validity of the models generated from one or a few samples should be tested in additional samples (Gigerenzer & Brighton, 2009; Woodside, 2016). Therefore, we first divided the original sample into a modeling subsample and a holdout subsample, then conducted the fsQCA procedures in each subsample (see Appendix C). Although we only reported here data analysis results from SPSS and fsQCA software, we conducted the same data analysis with R packages QCA (Dusa, 2019) and QCAfalsePositive (Braumoeller, 2015). The results obtained from fsQCA software and QCA package were generally comparable with different details due to different algorithms and default settings embedded in the two software (Thiem & Duşa, 2013). For replication and comparison purposes, interested readers can refer to Appendix H for the R procedures and Appendix I for the R results. The dataset for this

study

is

available

from

OSF

https://osf.io/9wmnc/?view_only=c031654c4e794a14b5d2d18510c5c9c2.

through The

full

dataset for the survey and the relevant R files is available from Mendeley Data (DOI: 10.17632/bz68yd4cs5.2).

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3. Results

3.1. Measurement Evaluation, Descriptive Statistics, and Correlation Results Psychometric parameters (Table 2) and descriptive statistics (Table 3) were examined for each measure. Students from the three educational levels differed on the mean values of all causal constructs (Table 3) with high school students showing the highest extent of smartphone use disorder. Table 4 shows that many of the causal constructs were significantly correlated. Specifically, in elementary school adolescents, male gender was associated with higher scores on smartphone use disorder (t = 3.17, p < 0.01). Smartphone use disorder was negatively associated with self-control (r = -0.32, 95% CI of [-0.20, -0.03]), positively associated with sensation seeking (r = 0.21, 95% CI of [0.11, 0.27]), positively associated with loneliness (r = 0.22, 95% CI of [0.10, 0.29]), positively associated with anxiety (r = 0.41, 95% CI of [0.34, 0.48]), and negatively associated with perceived parent-adolescent relationship (r = -0.36, 95% CI of [-0.44, -0.28]). In middle school adolescents, smartphone use disorder was negatively associated with self-control (r = -0.24, 95% CI of [-0.33, -0.16]), positively associated with sensation seeking (r = 0.22, 95% CI of [0.15, 0.31]), positively associated with anxiety (r = 0.34, 95% CI of [0.27, 0.41]), and negatively associated with perceived parent-adolescent relationship (r = -0.31, 95% CI of [-0.38, -0.23]). In high school adolescents, smartphone use disorder was negatively associated with selfcontrol (r = -0.32, 95% CI of [-0.41, -0.22]), positively associated with sensation seeking (r = 0.20, 95% CI of [0.12, 0.28]), positively associated with loneliness (r =

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0.09, 95% CI of [0.01, 0.17]), positively associated with anxiety (r = 0.26, 95% CI of [0.18, 0.35]), negatively associated with perceived parent-adolescent relationship (r = -0.17, 95% CI of [-0.26, -0.09]), and positively associated with perceived parental monitoring (r = 0.14, 95% CI of [0.05, 0.23]). One important procedure in fsQCA is to analyze contrarian cases, which is defined as exceptions to a statistically significant main effect. In real life, contrarian cases almost always occur and examining them might bring fruitful findings in qualitative research (Woodside, 2014). Contrarian cases are easily verifiable by creating quintiles of each variable and cross-tabulating the outcome variable with each causal condition (see Appendix F for a step-by-step description of the contrarian analysis procedures). Based on the contrarian analysis results (Appendix A), for each pair of constructs, many cases are not represented by the main effect. For example, the main effect shows that self-control is negatively correlated with smartphone use disorder while the contrarian analysis shows that 43.7% of the cases (the bolded numbers) were an exception to that main effect. Therefore, a qualitative approach such as fsQCA would complement the results obtained through correlation-based techniques. 3.2. fsQCA Results Configurations of cases with the presence of the outcome condition (i.e. high scores on smartphone use disorder) were examined. High levels of consistency suggest that the generated solution represents sufficient antecedents for the outcome condition (Mendel & Korjani, 2017). Solutions with high levels of coverage suggest that adolescents with smartphone use disorder could be inferred rather adequately based on

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the seven constructs examined in the current study. The combination of conditions (Table 5-7) displayed higher consistencies than those of single conditions (Appendix C). Core conditions (large black circle) are causal constructs that emerge in both parsimonious and intermediate solutions whereas peripheral conditions (small black circles) are those only emerge in the intermediate solutions (Fiss, 2011). It is worth noticing before we examine the fsQCA results in detail, the statistical robustness of the solutions was satisfactory as shown by high consistency and coverage of the overall solutions except for moderate coverage in the elementary school sample. Causal configurations of smartphone use disorder differed across three educational levels. Overall, the fsQCA procedure generated more configurations for high school students with smartphone use disorder than those in middle school or elementary school students (Table 5-7). The overall solution consistency and coverage from the subsamples were similar to those from the original sample (Appendix D) thus validated the solution (Pappas et al., 2017; Ragin, 2008). In accordance to existing studies, the current study grouped configurations with the same core conditions together as one solution thus generating two solutions for the elementary school sample (Table 5), eight solutions for the middle school sample (Table 6), and seven solutions for the high school sample (Table 7). However, to show configurations that are most representative of our sample, configurations with very low unique coverage will not be described separately (Rubinson et al., 2019). 3.2.1. fsQCA results in the elementary school sample. The fsQCA procedures generated four configurations for elementary school

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students with smartphone use disorder (Table 5). Coverage of each configuration and overall solution were relatively low, suggesting that there are other constructs (not considered in the current study) contributing to smartphone use disorder in elementary school adolescents. When we decreased the consistency cutoff score or the frequency cutoff score, the resulting solution coverage increased and included configurations with low self-control. The robustness of the results was also tested using the QCAfalsePositive permutation test, which showed that only the CDf path (i.e. configuration 1b) was robustly significant regarding both counterexamples and consistency. This path represents students with high sensation seeking, high loneliness, and low perceived parent-adolescent relationship. Overall, the core conditions for elementary school adolescents to score high on smartphone use disorder were high sensation seeking and high loneliness. 3.2.2. fsQCA results in the middle school sample. Nine configurations emerged from the middle school sample, with one configuration passed the permutation robustness tests regarding both counterexamples and consistency: Cdefg (i.e. configuration 2), with raw coverage of 0.346 and raw consistency of 0.880, suggesting that middle school students with high sensation seeking, low loneliness, low anxiety, low perceived parent-adolescent relationship, and low perceived parental monitoring reported high extent of smartphone use disorder. Overall, the core conditions for middle school adolescents to score high on smartphone use disorder were female gender, high sensation seeking, high loneliness, and high anxiety.

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3.2.3. fsQCA results in the high school sample. Thirteen configurations emerged from the high school sample. However, none of the 13 configurations passed the permutation robustness tests. One configuration passed the permutation test for consistency and achieved almost-significant result for the permutation test for counterexamples: Abcd (configuration 5), with raw coverage of 0.199 and raw consistency of 0.930. However, configuration 5 has an extremely low unique coverage (less than 0.005) which means this configuration is more of a logical conclusion than a representation of actual cases in our data. Overall, the core conditions for high school adolescents to score high on smartphone use disorder were female gender, high loneliness, and high anxiety. Interestingly, the two configurations that showed the highest coverage and consistency were not featured any of the three core conditions, meaning the presence of ecological factors examined in this study is not necessary nor sufficient for the presence of smartphone use disorder in high school students. Overall, the seven factors examined in this study do not combine to influence smartphone use disorder in high school students. 3.3. Robustness tests Three steps were taken to test for the robustness of our results and to guard against Type I error. First, in accordance to the practice in existing fsQCA studies in human behaviors (Martí-Parreño et al., 2018; Pappas et al., 2017; Sergis et al., 2018), we divided the original sample into two subsamples through random assignments. The consistency and coverage values were similar in the two subsamples and the original sample (see Table 5-7, Appendix D). Second, fsQCA procedures were conducted in the

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R package QCA. The results obtained from fsQCA software and QCA package showed similar consistency values, similar coverage values, and similar presence and absence causal conditions (see Table 5-7, Appendix H). Third, the R package QCAfalsePositive was used to test the chance that the resulting configurations emerged by chance (Braumoeller, 2015). The test results showed that most configurations in the parsimonious solutions did not occur by chance (as shown by the bolded p-values in Appendix J). 3.4. Regression analysis To compare the results from fsQCA analysis with that from regression analysis, smartphone use disorder was regressed on the seven ecological factors (Table 8). Bonferroni holm method was used to guard against inflated Type I resulting from multiple hypotheses tests (Holm, 1979). Results showed that smartphone use disorder in elementary school students was negatively associated with self-control (β = -0.19, p < 0.001) and perceived parent-adolescent relationship (β = 0.17, p < 0.001), and was positively associated with sensation seeking (β = 0.13, p < 0.01), and anxiety (β = 0.25, p < 0.001). Smartphone use disorder in middle school students was negatively associated with self-control (β = 0.15, p < 0.001), loneliness (β = -0.09, p < 0.05), perceived parent-adolescent relationship (β = -0.18, p < 0.001), and positively associated with sensation seeking (β = 0.18, p < 0.001) and anxiety (β = 0.31, p < 0.001). Smartphone use disorder in high school students was negatively associated with selfcontrol (β = -0.26, p < 0.001), and positively associated with sensation seeking (β = 0.24, p < 0.001) and anxiety (β = 0.23, p < 0.001).

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4. Discussion

The current study is the first one that investigated smartphone use disorder from the complexity theory perspective thus complemented the knowledge gained from correlation-based studies. Compared to configurations of single causal condition, the configurations of multiple causal conditions better represented sufficient conditions for the outcome to occur. Overall, the results support the ecological systems theory by showing that factors in the microsystem formed sufficient conditions for high sores on smartphone use disorder in adolescence. The following paragraphs discuss the differences and similarities regarding adolescents with smartphone use disorder across elementary schools, middle schools, and high schools. Four differences emerged across educational levels. First, the mean scores of smartphone use disorder increased significantly from elementary school to middle school to high school. This is consistent with findings from many existing studies (Bener & Bhugra, 2013; Gong et al., 2009; Wang et al., 2014). A recent study, for example, revealed an increasing trend of smartphone dependency from elementary school students to high school students but no difference across educational level regarding teacher-report fondness of using smartphones in adolescents (Gao, Yan, Zhao, Pan, & Mo, 2014). Researchers have long suspected that social smartphone usages are especially vulnerable to smartphone use disorder (Salehan & Negahban, 2013; Sha, Sariyska, Riedl, Lachmann, & Montag, 2019). However, social smartphone use,

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compared to other smartphone usages such as gaming, requires certain levels of literacy. Therefore, it is possible that high school adolescents are more prone to social smartphone usages than elementary school adolescents due to literacy level. Second, fsQCA results generated more configurations for students with smartphone use disorder as the educational level goes up. This result suggests that as the educational level goes up there are more identifiable groups of students with smartphone use disorder. It is also possible that the seven constructs investigated in the current study contribute to more cases in middle and high school adolescent smartphone use disorder than to cases in elementary school adolescents. Future studies could examine constructs that contribute to smartphone use disorder in elementary school adolescents but not to smartphone use disorder in middle and high school adolescents. Third, compared to middle and high school students, elementary school adolescents who scored high on smartphone use disorder reported higher levels of self-control. This result might be associated with higher perceived self-control in elementary school adolescents than that in middle school and high school adolescents (Table 3). Fourth, compared to that in middle school and elementary school, there are more cases in high school adolescents that featured female gender and high anxiety. This result supports the hypothesis that late adolescents and young adults are mainly addicted to social smartphone usages and that girls were more prone to this type of smartphone use disorder than boys (Griffiths & Szabo, 2014; Roberts et al., 2014). Early adolescents with smartphone use disorder were mainly addicted to smartphone gaming (Jeong et al., 2016) whereas late adolescents with smartphone use disorder were mainly addicted to smartphone social

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(Salehan & Negahban, 2013). Research on Facebook and WhatsApp revealed that communicating with friends on the internet is a crucial risk factor for smartphone use disorder (Sha et al., 2019) but smartphone social involves relatively high literacy skills thus would be more prominent in late than in early adolescence. The current study suggests that the different characteristics of students with smartphone use disorder in different educational levels might be a manifestation of the different smartphone usages that students engaged in. There are also some similarities in the configurations of adolescents with smartphone use disorder from three educational levels. First, adolescents who scored high on smartphone use disorder from the three educational levels all included males as well as females. This result is consistent with existing research showing that both male and female were prone to problematic smartphone use (Roberts et al., 2014). Future research could investigate the mechanisms through which user characteristics, such as gender, influence smartphone use disorder through specific smartphone activities such as gaming and social networking (Lee et al., 2018). Second, the fsQCA results from all three educational levels included configurations with high loneliness. This result is important considering that most participants reported low levels of loneliness in our sample. A longitudinal study showed that loneliness in children and adolescents predicted maladaptive behavior and mental health outcomes, controlling for initial levels of loneliness (Schinka, Van Dulmen, Mata, Bossarte, & Swahn, 2013). Future research could investigate how loneliness influence smartphone use disorder across adolescence.

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A comparison between the fsQCA results and the regression results showed that the regression results revealed the main trends between each predictor variable and the outcome variable whereas the fsQCA results revealed configurations of the causal constructs regardless of their influence on the overall trend. For example, configuration 1b consists of participants with high anxiety and high smartphone use disorder and showed the highest unique coverage. Therefore, as shown in the regression results, there was an overall trend of positive relationship between anxiety and smartphone use disorder. That is, fsQCA shows configurations of minority groups whose characteristics might be inconsistent with the main trend. 4.1. Perspectives and Implications First, the stage theory asserted that a good match between the environment and the adolescents’ need at a specific developmental stage would predict the adolescents’ experiences and motivations (Eccles et al., 1993). From early adolescence to late adolescence, the individual developed more need for autonomy. The correlation results showed that relatively high perceived parental monitoring was associated with relatively high scores on smartphone use disorder in high school adolescents, but this association was not significant in middle school adolescents and elementary school adolescents. The fsQCA results showed that, in more than half of the cases with high scores on smartphone use disorder, perceived parental monitoring did not matter in terms of contributing to the outcome when combined with certain individual characteristics. The results revealed in the current study support the notion that we need to pay attention to different factors in the microsystem when attempting to prevent

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smartphone use disorder in students from elementary schools, middle schools, and high schools. Combining the framework of ecological system theory and the stage theory, different individual and family factors contribute to child development outcomes in different educational level. For elementary school adolescents, efforts of preventing smartphone use disorder should pay special attention to high sensation seeking (i.e. a physical developmental factor) and high loneliness (i.e. a social developmental factor). For example, in the microsystem, parents and teachers could spend more time interacting with children through enrichment activities that both fulfill their need for sensation seeking and reduce their sense of loneliness through parents’ and teachers’ accompany. In the mesosystem, parents could learn through school internet portals the student organizations their children might participate in thus combine adolescents’ school engagement through parental participation. In the macrosystem, an atmosphere could be promoted in families, schools, and communities that encourage real-life sensation-seeking activities and social activities that involve smartphone as a tool instead of an atmosphere that encourage sensation-seeking activities and social activities that are exclusively online. For middle school students, efforts to preventing smartphone use disorder should focus on high sensation seeking (i.e. a physical developmental factor), high loneliness (i.e. a social developmental factor), high anxiety (i.e. an emotional developmental factor), especially in girls. For example, prevention effort for middle school students should emphasize more on teaching adaptive emotion regulation strategies to reduce anxiety. According to the Ecological Techno-

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Microsystem theory, efforts to fulfill sensation seeking and reduce loneliness and anxiety should be made at school, at home, and in the community. For high school students, efforts to prevent smartphone use disorder should focus on teaching adaptive emotion regulation strategy at home, at school, and in the community so that high school adolescents could successfully deal with anxiety through adaptive methods. Second, compared to correlation, fsQCA revealed a comprehensive depiction of conditions that contributed to smartphone use disorder in adolescents. Whereas the correlation results only showed the general trend association between two variables (see Table 4), the fsQCA results showed the presence or absence of a causal condition that could contribute to an outcome condition when combining with other causal conditions. For example, according to the correlation results, relatively high sensation seeking predicted relatively high smartphone use disorder in adolescents across all three educational levels (Table 4). The fsQCA results, although also show high sensation seeking as a core condition for the presence of smartphone use disorder in elementary school adolescents (Table 5) and middle school adolescents (Table 6), revealed a more detailed situation that covered the cases that contradict to the main effects. Specifically, in elementary school adolescents, those who reported male gender, high self-control, low anxiety, high perceived parent-adolescent relationship, and high perceived parental monitoring also reported high scores on smartphone use disorder regardless their level of sensation seeking (configuration 4 in Table 5). In middle school adolescents, low sensation-seeking contributed to high scores on smartphone use disorder when combining with the following conditions: male gender, low self-control, high loneliness,

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high anxiety, high perceived parent-adolescent relationship, high perceived parental monitoring (configuration 7 in Table 6). The current study demonstrated fsQCA as a powerful tool in depicting an outcome condition when there are complex interaction effects among the causal conditions. 4.2. Limitations, outlooks, and future research As the fsQCA is an analytical method between qualitative study and quantitative study, it carries some characteristics of both approaches. For example, like all qualitative studies, the results would differ from sample to sample. The sample in the current study represents adolescents from Shenzhen, which is a rich metropolitan city in China (Güneralp & Seto, 2008; Liu, Heilig, Chen, & Heino, 2007). Therefore, the results of the current study could be generalized to adolescents living in metropolitan cities in other parts of the world with similar rates of smartphone ownership. Second, it is possible that Type I error is inflated in the fsQCA procedures because it required performing more than one test at the same time (Krogslund, Choi, & Poertner, 2015). This issue is of special concern since fsQCA is originally proposed to analyze cases with small and medium sample size. The current study took precautions on Type I error by adopting a relatively large sample size. We also used the R package QCAfalse-Positive to guard against Type I error (Appendix J) (Braumoeller, 2015). Test results with the QCAfalse-Positive package showed that most configurations we obtained were unlikely to have emerged by chance. Finally, all the constructs were measured by self-report surveys thus the interpretation of the results should always consider the subjective nature of the measurement. For example, when

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we say “sensation seeking and loneliness are core conditions for smartphone use disorder for elementary school students” we must remember it means “perceived sensation seeking and perceived loneliness in fifth- and sixth- graders are core conditions for perceived smartphone use disorder in fifth- and sixth- graders” and does not mean “fifth- and sixth- graders’ sensation seeking and loneliness perceived by their parents are core conditions for smartphone use disorder in fifth- and sixth- graders perceived by their parents”. Future research could adopt other measurements of the constructs. For example, the configurations contributing to smartphone use disorder in adolescents might differ from those revealed in the current study had we surveyed parents instead of adolescents. Due to the limitations of the fsQCA method and the exploratory nature of the current study, results should be explained carefully and should not be generalized to other population without cross-validation in additional samples.

5. Conclusions

The current study applied the ecological systems theory and a configurational analytic approach to examining the characteristics of adolescents with smartphone use disorder. Results showed that characteristics of adolescents who scored high on smartphone use disorder shared some similarities across elementary-, middle-, and high school but also revealed major differences. Therefore, educators and mental health practitioners should consider educational level when helping adolescents with or at risk

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of smartphone use disorder. Also, compared to correlation-based techniques, fsQCA revealed differential associations between each causal condition and the outcome condition when combining with different values of other causal conditions. Therefore, future research should consider adopting both correlation-based techniques and fsQCA when investigating complex behavior problems in adolescents such as smartphone use disorder.

Acknowledgments The authors would like to thank Wangshan Wen, an outstanding undergraduate student at Shenzhen University who volunteered his time to help with data collection, data entry, and data mining.

Funding source: This work was supported by the National Social Science Fund of China [Grant Number 16BSH089].

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A CONFIGURATIONAL INVESTIGATION

Table 1 Gender and age information of participants with missing values Elementary school students

Middle school students

High school students

Total

N

%

N

%

N

%

Male

287

51.6

340

53.0

306

53.0

933

Female

267

48.0

298

46.4

261

46.0

826

Missing

2

0.4

4

0.6

1

0.2

7

10-12

328

59.0

11

1.7

0

0

339

12-15

179

32.2

583

90.8

9

1.6

771

15-18

0

0

11

1.7

539

94.9

550

Missing

49

8.8

37

5.8

20

3.5

106

Total

556

100

642

100

568

100

1766

Gender

Age

A CONFIGURATIONAL INVESTIGATION

Table 2 Descriptive statistics and measurement reliability of all constructs Constructs

M

SD

Skewness

Kurtosis

Cronbach's

AVE

Factor loadings

alpha L. Smartphone use disorder

2.44

0.84

0.32

-0.41

0.90

0.32 - 0.64

0.50 - 0.74

B. Self-control

3.66

0.68

-0.45

0.72

0.78

0.36

0.60 - 0.75

C. Sensation seeking

2.91

1.02

0.06

-0.63

0.78

0.60

0.67 - 0.85

D. Loneliness

1.62

0.82

1.54

2.11

0.89

0.54

0.75 - 0.85

E. Anxiety

2.17

0.63

0.46

-0.04

0.74

0.72

0.60 - 0.75

3.03

0.51

-0.43

-0.05

0.87

0.37, 0.50

0.40 - 0.68

5.01

1.86

-0.50

-0.79

-

F.

Perceived

parent-adolescent

relationship G. Perceived parental monitoring Note: M = mean, SD = standard deviation.

-

-

A CONFIGURATIONAL INVESTIGATION

Table 3 Descriptive statistics and one-way ANOVA results across the three educational levels Elementary school

Middle school

High school

Measurement

ANOVA

M

SD

M

SD

M

SD

scale

L. Smartphone use disorder

2.05

0.73

2.42

0.84

2.85

0.74

5-point

151.04***

B. Self-control

3.81

0.64

3.66

0.71

3.53

0.66

5-point

25.36***

C. Sensation seeking

2.81

0.97

2.75

1.05

3.19

0.96

5-point

33.41***

D. Loneliness

1.46

0.77

1.66

0.86

1.76

0.79

5-point

19.85***

E. Anxiety

2.95

0.59

2.69

0.60

2.53

0.51

4-point

77.48***

F. Perceived parent-adolescent 3.17

0.53

2.93

0.52

3.00

0.51

4-point

33.99***

1.77

5.34

1.69

4.28

1.91

7-point

70.62***

relationship G.

Perceived

parental 5.39

monitoring Note:

M

=

mean,

SD

=

standard

deviation.

***p

<

.001,

**p

<

.01,

*p

<

.05.

A CONFIGURATIONAL INVESTIGATION

Table 4 Inter-construct correlations with raw (uncalibrated) scores across the three educational levels (95% confidence intervals were indicated in parenthesis using the Bootstrap method with sample size = 1000 for each sample). Constructs

L

A

B

C

D

E

Elementary school L.

Smartphone

use

1

disorder A. Gender

-0.12

1

(-0.20, -0.04) B. Self-control

C. Sensation seeking

-0.34

0.01

(-0.20, -0.03)

(-0.07, 0.09)

0.19 (0.11, 0.27)

D. Loneliness

0.20 (0.10, 0.29)

E. Anxiety

0.41 (0.34, 0.48)

1

-0.08

-0.04

(-0.16, -0.001)

(-0.13, 0.05)

0.06 (-0.03, 0.14) -0.05 (-0.13, 0.03)

-0.19 (-0.29, -0.11) -0.34 (-0.42, -0.26)

1

0.07

1

(-0.02, 0.14) 0.13 (0.05, 0.21)

0.27 (0.18, 0.36)

1

F

G

A CONFIGURATIONAL INVESTIGATION

F.

Perceived

parent-

adolescent relationship G. Perceived parental monitoring

-0.36

0.07

(-0.44, -0.28)

(-0.01, 0.15)

0.04 (-0.05, 0.12)

0.34

-0.10

-0.32

-0.39

1

(0.27, 0.43)

(-0.18, -0.02)

(-0.40, -0.24)

(-0.46, -0.31)

-0.11

-0.02

0.07

0.02

0.04

-0.05

(-0.20, -0.03)

(-0.11, 0.07)

(-0.01, 0.16)

(-0.07, 0.09)

(-0.05, 0.12)

(-0.13, 0.04)

Middle school students L.

Smartphone

use

1

disorder A. Gender

0.02

1

(-0.06, 0.10) B. Self-control

C. Sensation seeking

-0.25

-0.05

(-0.33, -0.16)

(-0.12, 0.04)

0.23 (0.15, 0.31)

D. Loneliness

E. Anxiety

Perceived

parent-

(-0.09, 0.08)

-0.003

1

(-0.10, 0.09)

0.05

0.01

-0.14

-0.02

(-0.03, 0.14)

(-0.07, 0.09)

(-0.22, -0.06)

(-0.10, 0.06)

0.34

0.01

-0.18

0.02

(-0.07, 0.09)

(-0.26, -0.10)

(-0.06, 0.10)

0.002

0.29

-0.22

(0.27, 0.41) F.

-0.01

1

-0.30

1

0.31

1

(0.24, 0.38) -0.18

-0.20

1

1

A CONFIGURATIONAL INVESTIGATION

adolescent relationship G. Perceived parental monitoring

(-0.38, -0.23) -0.01 (-0.10, 0.07)

(-0.08, 0.08) -0.13 (-0.21, -0.06)

(0.21, 0.36)

(-0.29, -0.14)

(-0.25, -0.11)

(-0.28, -0.12)

0.16

0.01

-0.03

0.05

-0.01

(0.08, 0.24)

(-0.07, 0.09)

(-0.11, 0.06)

(-0.03, 0.13)

(-0.09, 0.07)

High school students L.

Smartphone

use

1

disorder A. Gender

0.06

1

(-0.02, 0.15) B. Self-control

C. Sensation seeking

D. Loneliness

E. Anxiety

F.

Perceived

-0.32

-0.08

(-0.41, -0.22)

(-0.16, 0.003)

0.20

-0.06

0.07

(0.12, 0.28)

(-0.14, 0.02)

(-0.04, 0.16)

0.09

0.03

-0.11

-0.03

(0.01, 0.17)

(-0.05, 0.11)

(-0.20, -0.02)

(-0.12, 0.06)

0.08

-0.20

(0.18, 0.35)

(-0.003, 0.15)

(-0.29, -0.11)

-0.18

-0.06

0.26

parent-

1

0.23

1

-0.14

1

0.20

(-0.24, -0.06)

(0.11, 0.28)

-0.09

-0.19

1

-0.05

1

1

A CONFIGURATIONAL INVESTIGATION

adolescent relationship G. Perceived parental monitoring

(-0.26, -0.09)

(-0.14, 0.03)

(0.14, 0.32)

(-0.18, -0.002)

(-0.28, -0.11)

0.14

-0.06

-0.08

0.04

0.05

(0.05, 0.23)

(-0.15, 0.02)

(-0.18, 0.02)

(-0.05, 0.13)

(-0.04, 0.13)

Gender: male = 1, female = 0. ***p < .001, **p < .01, *p < .05. Table 5 Configurations that lead to high scores on smartphone use disorder in elementary school adolescents 1a

1b

1c

2

⊕

•

•

⊕

B. Self-control

•

•

•

•

C. Sensation seeking

●

●

●

D. Loneliness

●

●

●

●

E. Anxiety

⊕

•

⊕

⊕

⊕

•

•

•

⊕

•

A. Gender

F. Perceived parent-adolescent relationship G. Perceived parental monitoring

•

(-0.13, 0.04) 0.10 (0.01, 0.17)

-0.15*** (-0.23, -0.06)

1

A CONFIGURATIONAL INVESTIGATION

Raw coverage

0.180

0.149

0.145

0.187

Unique coverage

0.003

0.023

0.019

0.010

Consistency

0.854

0.868

0.887

0.824

Overall solution coverage

0.358

Overall solution consistency

0.820

Note: Female = 1, Male = 0. ●Presence of a core condition, •Presence of a peripheral condition, ⊕Absence of a condition

A CONFIGURATIONAL INVESTIGATION

Configurations

with

the

same

core

causal

conditions

were

grouped

together

(Fiss,

2011).

A CONFIGURATIONAL INVESTIGATION

Table 6 Configurations that lead to high scores on smartphone use disorder in middle school adolescents 1a A. Gender

1b

2

⊕

3

4

5

6

7

8

●

●

●

●

⊕

●

⊕

⊕

•

B. Self-control

•

⊕

•

⊕

⊕

C. Sensation seeking

●

●

●

●

⊕

●

⊕

⊕

●

⊕

⊕

⊕

⊕

⊕

⊕

●

●

D. Loneliness E. Anxiety

●

●

⊕

⊕

⊕

●

●

●

F. Perceived parent-adolescent relationship

•

⊕

⊕

•

⊕

⊕

•

•

•

G. Perceived parental monitoring

•

•

•

•

•

•

⊕

Raw coverage

0.451

0.211

0.346

0.238

0.222

0.200

0.199

0.148

0.138

Unique coverage

0.045

0.011

0.029

0.019

0.017

0.007

0.009

0.019

0.002

Consistency

0.854

0.926

0.880

0.826

0.866

0.910

0.895

0.925

0.852

Overall solution coverage

0.627

Overall solution consistency

0.776

A CONFIGURATIONAL INVESTIGATION

Note: Female = 1, Male = 0. ●Presence of a core condition, •Presence of a peripheral condition, ⊕Absence of a condition Configurations with the same core causal conditions were grouped together (Fiss, 2011). Table 7 Configurations that lead to high scores on smartphone use disorder in high school adolescents 1

2

3a

A. Gender

3b

4a

4b

4c

4d

5a

5b

5c

6

7

⊕

●

●

●

●

●

●

●

⊕

⊕

•

•

•

⊕

⊕

•

⊕

•

•

•

⊕

•

•

•

⊕

⊕

⊕

●

⊕

⊕

B. Self-control

•

⊕

•

⊕

C. Sensation seeking

•

⊕

⊕

•

D. Loneliness

⊕

⊕

⊕

⊕

⊕

⊕

E. Anxiety

⊕

⊕

●

●

●

●

●

⊕

•

•

•

•

•

•

•

•

⊕

0.194

0.188

0.174

F.

Perceived

⊕

⊕

⊕

●

⊕

parent-adolescent •

•

⊕

•

⊕

•

0.199

0.176

0.243

•

relationship G. Perceived parental monitoring Raw coverage

• 0.472

0.470

0.388

0.222

0.247

0.277

0.155

A CONFIGURATIONAL INVESTIGATION

Unique coverage

0.037

0.018

0.011

0.010

0.004

0.000

0.002

0.000

0.016

0.000

0.000

0.020

0.011

Consistency

0.900

0.901

0.925

0.952

0.913

0.932

0.939

0.950

0.892

0.930

0.931

0.926

0.952

Overall solution coverage

0.717

Overall solution consistency

0.839

Note: Female = 1, Male = 0. ●Presence of a core condition, •Presence of a peripheral condition, ⊕Absence of a condition. Configurations with the same core causal conditions were grouped together (Fiss, 2011).

A CONFIGURATIONAL INVESTIGATION

Table 8 Smartphone use disorder regressed on ecological factors for elementary, middle, and high school participants (significance level criteria were corrected using the Bonferroni Holm method). Elementary school

Middle school

High school

β (p-value)

β (p-value)

β (p-value)

A. Gender

-0.08 (0.03)

0.01 (0.759)

0.04 (0.348)

B. Self-control

-0.19 (< .001)

-0.15 (< .001)

-0.26 (< .001)

C. Sensation seeking

0.13 (0 .001)

0.18 (< .001)

0.24 (< .001)

D. Loneliness

0.04 (0.285)

-0.09 (0.014)

0.003 (0.936)

E. Anxiety

0.25 (< .001)

0.31 (< .001)

0.23 (< .001)

F. Perceived parent-adolescent relationship

-0.17 (< .001)

-0.18 (< .001)

-0.07 (0.105)

G. Perceived parental monitoring

0.001 (0.986)

-0.01 (0.857)

0.08 (0.031)

R2

0.27

0.23

0.22

Note: P-values that survived the Bonferroni-Holm correction were bolded. *p < 0.05, **p < 0.01, ***p < 0.001

A CONFIGURATIONAL INVESTIGATION

C

Q1 Q2 Q3 Q4 Q5

14 [2.5%] 21 [3.8%] 27 [4.9%] 18 [3.3%] 34 [6.1%]

16 [2.9%] 30 [5.4%] 21 [3.8%] 19 [3.4%] 24 [4.3%]

18 [3.3%] 32 [5.8%] 27 [4.9%] 22 [4.0%] 17 [3.1%]

19 [3.4%] 29 [5.2%] 20 [3.6%] 17 [3.1%] 28 [5.1%]

30 [5.4%] 32 [5.8%] 16 [2.9%] 9 [1.6%] 13 [2.4%]

D

Q1 Q2

46 [8.3%] 68 [12.3%]

63 [11.4%] 47 [8.5%]

66 [11.9%] 50 [9.0%]

74 [13.4%] 39 [7.1%]

65 [11.8%] 35 [6.3%]

E

Q1 Q2 Q3 Q4 Q5

5 [0.9%] 18[3.3%] 19 [3.4%] 23 [4.2%] 49 [8.9%]

11 [2.0%] 26[4.7%] 28 [5.1%] 15 [2.7%] 30 [5.4%]

18 [3.3%] 19 [3.4%] 32 [5.8%] 26 [4.7%] 21 [3.8%]

31 [5.6%] 27 [4.9%] 29 [5.2%] 13 [2.4%] 13 [2.4%]

41 [7.4%] 26 [4.7%] 15 [2.7%] 7 [1.3%] 11 [2.0%]

F

Q1 Q2 Q3 Q4 Q5

49[8.9%] 19[3.4%] 26 [4.7%] 16 [2.9%] 4 [0.7%]

20 [3.6%] 27[4.9%] 26 [4.7%] 30[5.4%] 7 [1.3%]

14[2.5%] 26 [4.7%] 23 [4.2%] 32 [5.8%] 21 [3.8%]

16 [2.9%] 20 [3.6%] 23 [4.2%] 26 [4.7%] 28 [5.1%]

10[1.8%] 20 [3.6%] 16 [2.9%] 16 [2.9%] 38 [6.9%]

G

Q1 Q2 Q3

30 [5.4%] 47 [8.5%] 37 [6.7%]

33 [6.0%] 48[8.7%] 29[5.2%]

30 [5.4%] 41 [7.4%] 45[8.1%]

32 [5.8%] 24 [4.3%] 57 [10.3%]

38 [6.9%] 16 [2.9%] 46 [8.3%]

B

Q1 Q2 Q3 Q4 Q5

44 [7.0%] 31 [4.9%] 20 [3.2%] 14 [2.2%] 14 [2.2%]

29 [4.6%] 37 [5.9%] 30 [4.8%] 21 [3.3%] 19 [3.0%]

17 [2.7%] 29 [4.6%] 31 [4.9%] 19 [3.0%] 18 [2.9%]

22[3.5%] 27 [4.3%] 29 [4.6%] 31 [4.9%] 30 [4.8%]

17 [2.7%] 21 [3.3%] 18 [2.9%] 18 [2.9%] 45 [7.1%]

Middle school adolescents

Elementary school adolescents

Appendix A Contrarian analysis: scores on each construct were divided into several quarters and cross-tabulated with scores on smartphone use disorder. Bolded cells demonstrated cases that contradicted the main effect. Smartphone use disorder Q1 Q2 Q3 Q4 Q5 B Q1 38 [6.9%] 24 [4.3%] 17 [3.1%] 19 [3.4%] 5 [1.3%] Q2 34 [6.1%] 34 [6.1%] 35 [6.3%] 20 [3.6%] 13 [2.4%] Q3 26 [4.7%] 24 [4.3%] 30 [5.4%] 22 [4.0%] 20 [36%] Q4 13 [2.4%] 12 [2.2%] 22 [4.0%] 25 [4.5%] 22 [4.0%] Q5 3 [0.5%] 16 [2.9%] 12 [2.2%] 27 [4.9%] 38 [6.9%]

High school adolescents

A CONFIGURATIONAL INVESTIGATION

C

Q1 Q2 Q3 Q4 Q5

22 [3.5%] 9 [1.4%] 15 [2.4%] 28 [4.4%] 49 [7.8%]

29 [4.6%] 22 [3.5%] 28 [4.4%] 27 [4.3%] 30[4.8%]

20 [3.2%] 22 [3.5%] 20 [3.2%] 37 [5.9%] 15 [2.4%]

33 [5.2%] 34 [5.4%] 21 [3.3%] 28 [4.4%] 23 [3.6%]

32 [5.1%] 25 [4.0%] 21 [3.3%] 20 [3.2%] 21 [3.3%]

D

Q1 Q2

62 [9.8%] 61 [9.7%]

62 [9.8%] 74 [11.7%]

51 [8.1%] 63 [10.0%]

67 [10.6%] 72 [11.4%]

71[11.3%] 48 [7.6%]

E

Q1 Q2 Q3 Q4 Q5

14 [2.2%] 14 [2.2%] 25 [4.0%] 30[4.8%] 40 [6.3%]

20 [3.2%] 20 [3.2%] 27 [4.3%] 37 [5.9%] 32[5.1%]

19 [3.0%] 17 [2.7%] 24 [3.8%] 28 [4.4%] 26 [4.1%]

23 [3.6%] 35 [5.5%] 32 [5.1%] 32 [5.1%] 17 [2.7%]

44 [7.0%] 24 [3.8%] 23[3.6%] 20 [3.2%] 8 [1.3%]

F

Q1 Q2 Q3 Q4 Q5

46 [7.3%] 26 [4.1%] 20 [3.2%] 16 [2.5%] 15 [2.4%]

36 [5.7%] 23 [3.6%] 35 [5.5%] 23 [3.6%] 19 [3.0%]

22 [3.5%] 24 [3.8%] 25 [4.0%] 21 [3.3%] 22 [3.5%]

12 [1.9%] 33 [5.2%] 33 [5.2%] 30 [4.8%] 31 [4.9%]

15 [2.4%] 15 [2.4%] 22 [3.5%] 23 [3.6%] 44 [7.0%]

G

Q1 Q2 Q3

35 [5.5%] 47 [7.4%] 41[6.5%]

42 [6.7%] 56 [8.9%] 38 [6.0%]

30 [4.8%] 49 [7.8%] 35 [5.5%]

45 [7.1%] 48 [7.6%] 46 [7.3%]

38 [6.0%] 32 [5.1%] 49 [7.8%]

B

Q1 Q2 Q3 Q4 Q5

48 [8.5%] 26 [4.6%] 18 [3.2%] 10 [1.8%] 13 [2.3%]

24 [4.2%] 22 [3.9%] 29 [5.1%] 28 [4.9%] 9 [1.6%]

19 [3.4%] 27 [4.8%] 31 [5.5%] 24 [4.2%] 18 [3.2%]

15 [2.6%] 18 [3.2%] 24 [4.2%] 28 [4.9%] 18 [3.2%]

15 [2.6%] 14 [2.5%] 19 [3.4%] 31 [5.5%] 39 [6.9%]

C

Q1 Q2 Q3 Q4 Q5

18 [3.2%] 12 [2.1%] 22 [3.9%] 21 [3.7%] 42[7.4%]

18 [3.2%] 13 [2.3%] 25 [4.4%] 27 [4.8%] 29 [5.1%]

21 [3.7%] 21 [3.7%] 28 [4.9%] 29 [5.1%] 20 [3.5%]

26 [4.6%] 19 [3.4%] 19 [3.4%] 21 [3.7%] 18 [3.2%]

36 [6.3%] 16 [2.8%] 20 [3.5%] 23 [4.1%] 23 [4.1%]

D

Q1 Q2

48 [8.5%] 67 [11.8%]

58 [10.2%] 54 [9.5%]

59 [10.4%] 60 [10.6%]

56 [9.9%] 47 [8.3%]

72 [12.7%] 46 [8.1%]

E

Q1 Q2 Q3

11 [1.9%] 13 [2.3%] 24[4.2%]

20 [3.5%] 16 [2.8%] 31 [5.5%]

28 [4.9%] 16 [2.8%] 30 [5.3%]

30 [5.3%] 15 [2.6%] 24 [4.2%]

41 [7.2%] 15 [2.6%] 31 [5.5%]

A CONFIGURATIONAL INVESTIGATION

Q4 Q5

23 [4.1%] 44 [7.8%]

23 [4.1%] 22 [3.9%]

18 [3.2%] 27[4.8%]

18 [3.2%] 16 [2.8%]

15 [2.6%] 16 [2.8%]

F

Q1 Q2 Q3 Q4 Q5

35 [6.2%] 23 [4.1%] 28 [4.9%] 15 [2.6%] 14 [2.5%]

15 [2.6%] 24 [4.2%] 28 [4.9%] 24 [4.2%] 21 [3.7%]

22 [3.9%] 30 [5.3%] 19 [3.4%] 27 [4.8%] 21 [3.7%]

16 [2.8%] 23 [4.1%] 17 [3.0%] 26 [4.6%] 21 [3.7%]

16 [2.8%] 24 [4.2%] 16 [2.8%] 26 [4.6%] 36 [6.3%]

G

Q1 Q2 Q3

34 [6.0%] 33 [5.8%] 48 [8.5%]

34 [6.0%] 50 [8.8%] 28 [4.9%]

43 [7.6%] 43 [7.6%] 33 [5.8%]

41 [7.2%] 35 [6.2%] 27 [4.8%]

48 [8.5%] 36 [6.3%] 34 [6.0%]

A CONFIGURATIONAL INVESTIGATION

Appendix B Truth table for the 10 most frequent configurations of causal conditions associated with smartphone use disorder A B C D E F G Frequency L Consistency Elementary school 0 1 0 0 0 1 1 78 (22%) 0 0.475 1 1 0 0 0 1 1 69 (20%) 0 0.402 0 1 1 0 0 1 1 59 (17%) 0 0.535 1 1 1 0 0 1 1 34 (10%) 0 0.503 1 1 0 0 0 1 0 26 (7%) 0 0.502 0 1 0 0 0 1 0 15 (5%) 0 0.614 1 1 1 0 0 1 0 14 (4%) 0 0.616 0 1 1 0 0 1 0 9 (2%) 0 0.681 0 1 0 0 0 0 1 6 (2%) 0 0.758 1 1 0 0 0 0 1 5 (1%) 0 0.744 Middle school 0 1 0 0 0 1 1 85 (23%) 0 0.562 1 1 0 0 0 1 1 58 (17%) 0 0.565 0 1 1 0 0 1 1 45 (12%) 0 0.656 1 1 1 0 0 1 1 27 (8%) 0 0.700 1 1 0 0 0 1 0 16 (4%) 0 0.682 0 1 0 0 0 1 0 14 (4%) 0 0.732 1 1 1 0 0 1 0 12 (3%) 0 0.754 0 1 1 0 0 0 1 10 (3%) 0 0.796 1 1 1 0 0 0 1 8 (2%) 0 0.825 0 1 0 0 0 0 1 8 (3%) 0 0.772 High school 0 1 1 0 0 1 1 50 (17%) 0 0.805 0 1 1 0 0 1 0 33 (12%) 0 0.799 1 1 1 0 0 1 0 28 (9%) 0 0.801 1 1 1 0 0 1 1 27 (10%) 0 0.847 0 1 0 0 0 1 1 26 (9%) 0 0.829 0 1 0 0 0 1 0 24 (9%) 0 0.810 1 1 0 0 0 1 1 24 (8%) 0 0.778 1 1 0 0 0 1 0 19 (7%) 0 0.807 0 1 1 0 0 0 1 9 (3%) 1 0.897 1 0 1 0 0 1 1 7 (2%) 1 0.945 Note: A = Gender, B = Self-control, C = Sensation seeking, D = Loneliness, E = Anxiety, F = Perceived parent-adolescent relationship, G = Perceived parental monitoring. L = smartphone use disorder.

A CONFIGURATIONAL INVESTIGATION

Appendix C Subset/Superset analysis results for the set relationships between each causal condition and smartphone use disorder. In elementary school In middle school In high school students students students Consistency Coverag Consistency Coverag Consistency Coverage e e A 0.217 0.443 0.339 0.475 0.467 0.472 ~A 0.254 0.557 0.329 0.525 0.445 0.528 B 0.308 0.962 0.431 0.897 0.597 0.870 ~B 0.651 0.725 0.726 0.660 0.867 0.640 C 0.440 0.834 0.583 0.750 0.673 0.818 ~C 0.349 0.818 0.442 0.752 0.666 0.653 D 0.719 0.396 0.727 0.369 0.846 0.341 ~D 0.261 0.960 0.384 0.954 0.529 0.947 E 0.733 0.655 0.772 0.615 0.871 0.561 ~E 0.284 0.956 0.413 0.908 0.582 0.903 F 0.291 0.928 0.434 0.869 0.588 0.909 ~F 0.666 0.699 0.706 0.694 0.854 0.556 G 0.304 0.934 0.412 0.888 0.634 0.757 ~G 0.460 0.534 0.593 0.495 0.622 0.623 Note: A = Gender, B = Self-control, C = Sensation seeking, D = Loneliness, E = Anxiety, F = Perceived parent-adolescent relationship, G = Perceived parental monitoring.

A CONFIGURATIONAL INVESTIGATION

Appendix D Results for testing predictive validity using subsamples Configurations Training set Raw Consistency coverage 0.152 0.863 Elementary ABCDeFg school solution 0.152 0.863

Test set Raw coverage 0.137

Consistency 0.920

Middle school

BCEFG bcdefG ABCdEG AbCdeFG solution

0.464 0.471 0.224 0.242 0.616

0.846 0.863 0.867 0.858 0.794

0.435 0.448 0.260 0.281

0.864 0.837 0.824 0.768

High school

BCdef bCdeFG BcdEFG AcdEFG ABCdEg AbcdeFg aBCDeFG ABCDEFG ACdeFG ABCdeG solution

0.460 0.434 0.364 0.172 0.165 0.157 0.141 0.103 0.245 0.244 0.678

0.901 0.947 0.931 0.950 0.972 0.922 0.927 0.965 0.863 0.862 0.832

0.483 0.458 0.409 0.214 0.183 0.202 0.139 0.130 0.268 0.265

0.900 0.926 0.920 0.920 0.932 0.918 0.913 0.959 0.822 0.818

A CONFIGURATIONAL INVESTIGATION

Appendix E. Data analysis procedures with SPSS and fsQCA software 1. Original data was processed in SPSS to calibrate the variables using the following syntax: * Encoding: UTF-8. *step1: score1 - cross-over point. DATASET ACTIVATE 数据集 1. COMPUTE L_2=L - 3. COMPUTE B_2=B - 3. COMPUTE C_2=C - 3. COMPUTE D_2=D - 3. COMPUTE E_2=E - 2.5. COMPUTE F_2=F - 2.5. COMPUTE G_2=G - 4. EXECUTE. *step 2: recode each variable into its scalar scores. RECODE L_2 (0 thru Highest=1.5) (Lowest thru 0=1.5) INTO L_3. RECODE B_2 (0 thru Highest=1.5) (Lowest thru 0=1.5) INTO B_3. RECODE C_2 (0 thru Highest=1.5) (Lowest thru 0=1.5) INTO C_3. RECODE D_2 (0 thru Highest=1.5) (Lowest thru 0=1.5) INTO D_3. RECODE E_2 (0 thru Highest=2) (Lowest thru 0=2) INTO E_3. RECODE F_2 (0 thru Highest=2) (Lowest thru 0=2) INTO F_3. RECODE G_2 (0 thru Highest=1) (Lowest thru 0=1) INTO G_3. EXECUTE.

A CONFIGURATIONAL INVESTIGATION

*step 3: calculate score4 = score2 * score3. COMPUTE L_4=L_2 * L_3. COMPUTE B_4=B_2 * B_3. COMPUTE C_4=C_2 * C_3. COMPUTE D_4=D_2 * D_3. COMPUTE E_4=E_2 * E_3. COMPUTE F_4=F_2 * F_3. COMPUTE G_4=G_2 * G_3. EXECUTE. *step 4: calculate set membership score = exp(score4)/(1+exp(score4)). COMPUTE L_5=exp(L_4)/(1+exp(L_4)). COMPUTE B_5=exp(B_4)/(1+exp(B_4)). COMPUTE C_5=exp(C_4)/(1+exp(C_4)). COMPUTE D_5=exp(D_4)/(1+exp(D_4)). COMPUTE E_5=exp(E_4)/(1+exp(E_4)). COMPUTE F_5=exp(F_4)/(1+exp(F_4)). COMPUTE G_5=exp(G_4)/(1+exp(G_4)). EXECUTE. *Recode the original binary variable "gender" into the calibrated causal condition "gender" that ranges from 0 to 1. RECODE Gender (1=0) (2=1) INTO A_5. VARIABLE LABELS

A_5 'gender'.

A CONFIGURATIONAL INVESTIGATION

EXECUTE.

2. The original SPSS data file was split into three SPSS data files to store data collected from elementary schools, junior middle schools, and high schools. DATASET ACTIVATE 数据集 1. DATASET COPY

ele.

DATASET ACTIVATE

ele.

FILTER OFF. USE ALL. SELECT IF (EducationalLevel = 1). EXECUTE. DATASET ACTIVATE

数据集 1.

3. Then the calibrated variables were saved as csv files. Here we take the elementary school data as an example. SAVE

TRANSLATE

OUTFILE='C:\Users\DELL\Documents\research\projects\fsQCArevised\analysis\element ary.csv' /TYPE=CSV /ENCODING='UTF8' /MAP /REPLACE

A CONFIGURATIONAL INVESTIGATION

/FIELDNAMES /CELLS=VALUES /DROP=SocialMediaInstantMessagingPhoneCalls Posting Music Videos Information Gaming Novels StudySearchStudyChat Shopping ID Age SchoolNameEducationalLevel Class

TestScoresTotalTestScores

Grade

EstimatedRank

Gender

SingleChildParentalMonitoringSchoolMonitoringSelfFrequencyOtherFrequency SelfControl1

SelfControl2

SensationSeeking1 Loneliness1

SelfControl3

SensationSeeking2

Loneliness2

Loneliness3

SelfControl4

SelfControl5

SensationSeeking3 Loneliness4

SelfControl6

SensationSeeking4

Loneliness5

Loneliness6

Addiction_level PhoneAddiction1 PhoneAddiction2 PhoneAddiction3 PhoneAddiction4 PhoneAddiction5 PhoneAddiction6 PhoneAddiction7 PhoneAddiction8 PhoneAddiction9 PhoneAddiction10

PhoneAddiction11

PhoneAddiction12

PhoneAddiction13

PhoneAddiction14

PhoneAddiction15

PhoneAddiction16

PhoneAddiction17

ParentChildRelationship1

ParentChildRelationship2

ParentChildRelationship3

ParentChildRelationship4

ParentChildRelationship5

ParentChildRelationship6

ParentChildRelationship7

ParentChildRelationship8

ParentChildRelationship9

ParentChildRelationship10

ParentChildRelationship11

ParentChildRelationship12

ParentChildRelationship13

ParentChildRelationship14

ParentChildRelationship15

ParentChildRelationship16 PCR_R1 PCR_R3 PCR_R4 PCR_R7 PCR_R9 PCR_R11 PCR_R12 PCR_R15 L A B C D E F G B_2 C_2 D_2 E_2 F_2 G_2 L_3 B_3 C_3 D_3 E_3 F_3 G_3 L_4 B_4 C_4 D_4 E_4 F_4 G_4.

A CONFIGURATIONAL INVESTIGATION

4. Open the filed produced in step 3 (i.e. “elementary.csv”) in fsQCA software. Click “Analyze – Truth Table Algorithm”. Then selecting the outcome condition "L_5" into the "outcome" box by clicking "set" button and selecting the remaining conditions "A_5", "B_5", "C_5", "D_5", "E_5", "F_5", "G_5" into the "causal conditions" box by clicking the "add" button. Here's the screenshot of this step:

5. After clicking the "OK" button at step 4, the following "Edit Truth Table" dialogue appears.

A CONFIGURATIONAL INVESTIGATION

6. Click "Edit - Delete and code". Enter "2" in the "Delete rows with number less than" box and enter "0.85" in the "and set L_5 to 1 for rows with consist >=" box. Here's the screenshot of this step:

7. After clicking the "OK" button at step 6, the Edit Truth Table window filled the "L_5" column. Then click "standard analysis" button as shown below:

A CONFIGURATIONAL INVESTIGATION

8. After clicking the "standard analysis" button in step 7, the "intermediate solution" dialogue appear as shown below:

9. Click "ok" button and obtain the solutions as shown below:

A CONFIGURATIONAL INVESTIGATION

Appendix F. Syntax and procedure for contrarian analysis 1. Create quintiles using the mean of each construct. RANK VARIABLES=L A B C D E F G (A) /RANK /NTILES(5) /PRINT=YES /TIES=MEAN.

2. Create a 5 X 5 table to investigate the cross-tabulation between the outcome condition and each of the causal construct using SPSS. The following syntax demonstrate the crosstabulation between smartphone use disorder and gender. CROSSTABS /TABLES=NL BY NA /FORMAT=AVALUE TABLES /CELLS=COUNT TOTAL /COUNT ROUND CELL.

3. We discovered the existence of contrarian cases by building a contingency table as shown below: in some cases boys obtained high scores on smartphone use disorder and in other cases girls obtained high scores on smartphone use disorder. Percentile Group of A (Smartphone Use Disorder) * Percentile Group of B (self-control) Percentile Group of Self-control

Total

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1 Percentile Group 1

Count

of

Percentage

Smartphone

Use Disorder

2

Count Percentage

3

Count Percentage

4

Count Percentage

5

Count Percentage

Total

Count Percentage

2

3

4

5

8

13

20

22

38

101

1.4%

2.3%

3.6%

4.0%

6.8%

18.2%

19

20

22

25

27

113

3.4%

3.6%

4.0%

4.5%

4.9%

20.3%

17

35

30

22

12

116

3.1%

6.3%

5.4%

4.0%

2.2%

20.9%

24

34

24

12

16

110

4.3%

6.1%

4.3%

2.2%

2.9%

19.8%

39

34

26

13

4

116

7.0%

6.1%

4.7%

2.3%

0.7%

20.9%

107

136

122

94

97

556

19.2%

24.5%

21.9%

16.9%

17.4%

100.0%

Appendix G. SPSS syntax to reversely code the negatively poled items

DATASET ACTIVATE dataset6. RECODE ParentChildRelationship1 ParentChildRelationship3 ParentChildRelationship4 ParentChildRelationship7 ParentChildRelationship9 ParentChildRelationship11 ParentChildRelationship12 ParentChildRelationship15 (1=4) (2=3) (3=2) (4=1) INTO PRC_R1 PRC_R3 PRC_R4 PRC_R7 PRC_R9 PRC_R11 PRC_R12 PRC_R15.

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EXECUTE.

Appendix H: procedures to conduct fsQCA using package “QCA” in RStudio 1. R syntax to read elementary school data into R: setwd("C:/Users/DELL/Documents/research/projects/fsQCArevised/analysis") data

<-

read.csv("C:/Users/DELL/Documents/research/projects/fsQCArevised/analysis/ele.csv") names(data) <- c("L","A","B","C","D","E","F","G") data <- data[-c(134,233,548),]

2. fsQCA procedures with the QCA package: install.packages("QCA") library(QCA)

fsCalibrate<-function(x,thresholds) { #x = a vector of values to be fuzzified #thresholds = a vector of length 3 containing the threshold values in the following order: (fully out,crossover,fully in)

if( length(thresholds) != 3 ) stop("You must provide three threshold values") dev<-x-thresholds[2] if ( thresholds[1] > thresholds[3] ) dev<-dev*(-1)

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us<-3/abs(thresholds[3]-thresholds[2]) ls<-3/abs(thresholds[1]-thresholds[2]) scal<-rep(0,length.out=length(x)) scal[which(dev>=0)]<-us scal[which(dev<0)]<-ls prod<-dev*scal scores<-exp(prod)/(1+exp(prod)) return(scores) }

data$L <- fsCalibrate(data$L,c(1,3,5)) data$B <- fsCalibrate(data$B,c(1,3,5)) data$C <- fsCalibrate(data$C,c(1,3,5)) data$D <- fsCalibrate(data$D,c(1,3,5)) data$E <- fsCalibrate(data$E,c(1,2.5,4)) data$F <- fsCalibrate(data$F,c(1,2.5,4)) data$G <- fsCalibrate(data$G,c(1,4,7))

t

=

truthTable(data,outcome="L",conditions="A,B,C,D,E,F,G",incl.cut=0.85,n.cut=2,complete =TRUE,sort.by="incl") solution1 <- minimize(t,details = TRUE,method = "QMC",dir.exp="")

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solution1

n OUT = 1/0/C: 9/334/0 Total

: 343

M1: a*B*C*d*E*F*g + A*B*C*D*E*f*G + A*b*C*d*E*f*G + a*B*c*D*e*F*G => L inclS

PRI

covS

covU

-------------------------------------------1

a*B*C*d*E*F*g

0.869

0.228

0.213

0.082

2

A*B*C*D*E*f*G

0.861

0.201

0.154

0.009

3

A*b*C*d*E*f*G

0.859

0.122

0.225

0.079

4

a*B*c*D*e*F*G

0.863

0.034

0.177

0.046

-------------------------------------------M1

0.827

0.161

0.493

3. R syntax for permutation test (with QCAfalsePositive package) to guard against Type I error: setwd("C:/Users/DELL/Documents/research/projects/fsQCArevised/analysis") data

<-

read.csv("C:/Users/DELL/Documents/research/projects/fsQCArevised/analysis/ele.csv") names(data) <- c("L","A","B","C","D","E","F","G") data <- data[-c(134,233,548),]

install.packages("QCAfalsePositive") library(QCAfalsePositive)

A CONFIGURATIONAL INVESTIGATION

aCDe <- pmin((1-data$A),data$C,data$D,(1-data$E)) CDf <- pmin(data$C,data$D,(1-data$F)) CDeg <- pmin(data$C,data$D,(1-data$E),(1-data$G)) aDe <- pmin((1-data$A),data$D,(1-data$E))

test<-fsQCApermTest(y

=

data$L,

configs

=

list(aCDe=aCDe,CDf=CDf,CDeg=CDeg,aDe=aDe), total.configs=26, num.iter = 10000, my.seed = 123, adj.method = "holm") summary(test) plot(test)

A CONFIGURATIONAL INVESTIGATION

Appendix I: fsQCA results obtained in RStudio 1. Elementary school students: 1.1 fsQCA results n OUT = 1/0/C: 9/334/0 Total : 343 M1: a*B*C*d*E*F*g + A*B*C*D*E*f*G + A*b*C*d*E*f*G + a*B*c*D*e*F*G => L inclS PRI covS covU -------------------------------------------1 a*B*C*d*E*F*g 0.869 0.228 0.213 0.082 2 A*B*C*D*E*f*G 0.861 0.201 0.154 0.009 3 A*b*C*d*E*f*G 0.859 0.122 0.225 0.079 4 a*B*c*D*e*F*G 0.863 0.034 0.177 0.046 -------------------------------------------M1 0.827 0.161 0.493 2. Middle school students n OUT = 1/0/C: 52/319/0 Total : 371 M1: B*D*E*F*G + a*B*c*E*f*G + a*c*D*E*F*G + a*C*d*E*f*g + A*b*d*E*F*G + A*B*C*D*F*G + A*C*d*E*f*G + a*B*c*d*E*F*g + A*b*c*d*e*f*G + A*b*C*d*e*F*g + A*B*C*d*E*F*g + (a*b*C*d*E*f) => L M2: B*D*E*F*G + a*B*c*E*f*G + a*c*D*E*F*G + a*C*d*E*f*g + A*b*d*E*F*G + A*B*C*D*F*G + A*C*d*E*f*G + a*B*c*d*E*F*g + A*b*c*d*e*f*G + A*b*C*d*e*F*g + A*B*C*d*E*F*g + (b*C*d*E*f*G) => L ------------------inclS PRI covS covU (M1) (M2) ----------------------------------------------------------1 B*D*E*F*G 0.849 0.164 0.316 0.009 0.009 0.009 2 a*B*c*E*f*G 0.832 0.183 0.253 0.025 0.025 0.025 3 a*c*D*E*F*G 0.855 0.151 0.162 0.003 0.003 0.003 4 a*C*d*E*f*g 0.917 0.433 0.169 0.005 0.005 0.013 5 A*b*d*E*F*G 0.856 0.362 0.240 0.022 0.022 0.022 6 A*B*C*D*F*G 0.852 0.113 0.138 0.001 0.001 0.001 7 A*C*d*E*f*G 0.878 0.425 0.218 0.014 0.020 0.014 8 a*B*c*d*E*F*g 0.857 0.213 0.182 0.017 0.017 0.017

A CONFIGURATIONAL INVESTIGATION

9 A*b*c*d*e*f*G 0.864 0.255 0.212 0.017 0.017 0.017 10 A*b*C*d*e*F*g 0.877 0.197 0.167 0.010 0.010 0.010 11 A*B*C*d*E*F*g 0.880 0.278 0.173 0.011 0.011 0.011 ----------------------------------------------------------12 a*b*C*d*E*f 0.902 0.411 0.227 0.000 0.013 13 b*C*d*E*f*G 0.920 0.446 0.411 0.000 0.013 ----------------------------------------------------------M1 0.762 0.282 0.662 M2 0.762 0.282 0.662 3. High school students 3.1. fsQCA results > solution3 n OUT = 1/0/C: 132/177/0 Total : 309 M1: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*b*c*d*F + a*B*C*d*E*G + A*B*C*d *E*g) => L M2: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*b*c*d*F + a*B*C*d*E*G + A*B*C*d *f*g) => L M3: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*b*c*d*F + a*B*C*d*f*G + A*B*C*d* E*g) => L M4: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*b*c*d*F + a*B*C*d*f*G + A*B*C*d* f*g) => L M5: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*c*d*E*F + a*B*C*d*E*G + A*B*C*d *E*g) => L M6: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*c*d*E*F + a*B*C*d*E*G + A*B*C*d *f*g) => L M7: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*c*d*E*F + a*B*C*d*f*G + A*B*C*d*

A CONFIGURATIONAL INVESTIGATION

E*g) => L M8: B*d*E*F + d*E*F*G + a*C*d*E*F + A*b*d*e*F + B*C*d*e*f + a*b*C*d *e*G + a*B*C*D*e*F + A*B*C*E*F*G + b*c*d*e*F*g + (A*c*d*E*F + a*B*C*d*f*G + A*B*C*d* f*g) => L

inclS PRI

------------------------------------------------------------covS covU (M1) (M2) (M3) (M4) (M5)

(M6) (M7) -------------------------------------------------------------------------------------------1 B*d*E*F 0.831 0.401 0.631 0.006 0.011 0.024 0.011 0.024 0.006 0.013 0.006 2 d*E*F*G 0.863 0.483 0.552 0.007 0.008 0.008 0.008 0.008 0.00 7 0.007 0.007 3 a*C*d*E*F 0.868 0.491 0.316 0.006 0.006 0.006 0.006 0.006 0.00 6 0.006 0.006 4 A*b*d*e*F 0.900 0.461 0.249 0.007 0.007 0.007 0.007 0.007 0.00 9 0.009 0.009 5 B*C*d*e*f 0.904 0.420 0.446 0.013 0.027 0.021 0.019 0.013 0.027 0.021 0.019 6 a*b*C*d*e*G 0.931 0.541 0.229 0.015 0.015 0.015 0.015 0.015 0.01 5 0.015 0.015 7 a*B*C*D*e*F 0.898 0.318 0.148 0.007 0.007 0.007 0.007 0.007 0.0 07 0.007 0.007 8 A*B*C*E*F*G 0.909 0.479 0.215 0.002 0.002 0.002 0.002 0.002 0.0 02 0.002 0.002 9 b*c*d*e*F*g 0.927 0.231 0.353 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 -------------------------------------------------------------------------------------------10 A*b*c*d*F 0.901 0.466 0.234 0.000 0.000 0.000 0.000 0.000 11 A*c*d*E*F 0.855 0.424 0.257 0.000 0.001 0.001 0.001 12 a*B*C*d*E*G 0.889 0.444 0.259 0.000 0.002 0.002 0.00 2 0.002 13 a*B*C*d*f*G 0.890 0.364 0.221 0.000 0.001 0.001 0.001 14 A*B*C*d*E*g 0.920 0.513 0.204 0.001 0.002 0.002 0.00 2 0.002 15 A*B*C*d*f*g 0.931 0.454 0.177 0.000 0.002 0.002 0.002 -------------------------------------------------------------------------------------------M1 0.791 0.426 0.783 M2 0.791 0.426 0.782

A CONFIGURATIONAL INVESTIGATION

M3 M4 M5 M6 M7 M8

0.791 0.791 0.791 0.791 0.792 0.792

0.427 0.427 0.427 0.427 0.427 0.428

---------------------(M8) ---------------------1 B*d*E*F 0.013 2 d*E*F*G 0.007 3 a*C*d*E*F 0.006 4 A*b*d*e*F 0.009 5 B*C*d*e*f 0.013 6 a*b*C*d*e*G 0.015 7 a*B*C*D*e*F 0.007 8 A*B*C*E*F*G 0.002 9 b*c*d*e*F*g 0.006 ---------------------10 A*b*c*d*F 11 A*c*d*E*F 0.001 12 a*B*C*d*E*G 13 a*B*C*d*f*G 0.001 14 A*B*C*d*E*g 15 A*B*C*d*f*g 0.002 ----------------------

0.783 0.782 0.783 0.783 0.783 0.782

A CONFIGURATIONAL INVESTIGATION

Appendix J Permutation test results to guard against Type I error using package “QCAfalsePositive” in RStudio 1. Results for the elementary school sample Call: fsQCApermTest(y = data$L, configs = list(aCDe = aCDe, CDf = CDf, CDeg = CDeg, aDe = aDe), total.configs = 26, num.iter = 10000, my.seed = 123, adj.method = "holm") Counterexamples Observed Upper Bound Lower c.i. aCDe 31.0000 61.0000 33.0000 CDf 62.0000 108.0000 71.0000 CDeg 39.0000 69.0000 39.0000 aDe 38.0000 65.0000 38.0000

p-adj se(p-adj) 0.0250 0.0066 0.0000 0.0000 0.1288 0.0142 0.0864 0.0119

Consistency Observed Lower Bound Upper c.i. p-adj se(p-adj) aCDe 0.85174 0.70117 0.85014 0.05060 0.0093 CDf 0.87264 0.75166 0.84910 0.00000 0.0000 CDeg 0.92251 0.82758 0.91548 0.01250 0.0047 aDe 0.79083 0.63350 0.78830 0.04320 0.0086 Total number of configurations: 26 Number of permutations: 10000 p-value adjustment method: holm

2. Results for the middle school sample Call: fsQCApermTest(y = dat$L, configs = list(CE = CE1, abCdEf = abCdEf2, Cdefg = Cdefg3, AbCde = AbCde4, Abcdef = Abcdef5, ACdEf = ACdEf 6, AbcdE = AbcdE7, abcDE = abcDE8, ACD = ACD9), total.configs = 38, num.iter = 10000, my.seed = 123, adj.method = "holm") Counterexamples Observed Upper Bound Lower c.i. CE 248.0000 279.0000 233.0000 abCdEf 68.0000 110.0000 69.0000 Cdefg 98.0000 152.0000 103.0000 AbCde 88.0000 123.0000 83.0000

p-adj se(p-adj) 1.0000 0.0000 0.0814 0.0109 0.0076 0.0035 1.0000 0.0000

A CONFIGURATIONAL INVESTIGATION

Abcdef ACdEf AbcdE abcDE ACD

95.0000 79.0000 96.0000 41.0000 43.0000

120.0000 114.0000 124.0000 64.0000 69.0000

78.0000 1.0000 75.0000 0.5652 83.0000 1.0000 35.0000 1.0000 38.0000 1.0000

0.0000 0.0197 0.0000 0.0000 0.0000

Consistency Observed Lower Bound Upper c.i. p-adj se(p-adj) CE 0.76634 0.63839 0.70954 0.00000 0.0000 abCdEf 0.90185 0.75148 0.85734 0.00000 0.0000 Cdefg 0.87789 0.79046 0.86768 0.00000 0.0000 AbCde 0.80759 0.70115 0.81759 0.39370 0.0194 Abcdef 0.84378 0.72567 0.83582 0.01980 0.0055 ACdEf 0.86931 0.71801 0.83287 0.00000 0.0000 AbcdE 0.84527 0.71059 0.82351 0.00000 0.0000 abcDE 0.90714 0.77797 0.89964 0.02560 0.0063 ACD 0.82461 0.71079 0.85311 1.00000 0.0000 Total number of configurations: 38 Number of permutations: 10000 p-value adjustment method: holm

3. Results for the high school sample Call: fsQCApermTest(y = da$L, configs = list(def = def1, bcde = bcde2, cdE = cdE3, abdE = abdE4, AdE = AdE5, AcdE = AcdE6, AE = AE7, AdEg = AdEg8, Abde = Abde9, Abcd = Abcd10, Adfg = Adfg11, abde = abde12, aDe = aDe13), total.configs = 32, num.iter = 10000, my.seed = 123, adj.method = "holm") Counterexamples Observed Upper Bound Lower c.i. p-adj se(p-adj) def 123.000 161.000 115.000 1.000 0.0000 bcde 100.000 128.000 92.000 1.000 0.0000 cdE 161.000 190.000 146.000 1.000 0.0000 abdE 58.000 98.000 56.000 0.310 0.0194 AdE 107.000 138.000 97.000 1.000 0.0000 AcdE 78.000 101.000 66.000 1.000 0.0000 AE 120.000 144.000 107.000 1.000 0.0000 AdEg 63.000 86.000 52.000 1.000 0.0000 Abde 57.000 91.000 55.000 0.310 0.0194 Abcd 50.000 84.000 50.000 0.112 0.0132

A CONFIGURATIONAL INVESTIGATION

Adfg abde aDe

42.000 75.000 37.000

70.000 100.000 57.000

35.000 61.000 27.000

1.000 1.000 1.000

0.0000 0.0000 0.0000

Consistency Observed Lower Bound Upper c.i. p-adj se(p-adj) def 0.87488 0.83984 0.89033 1.00000 0.0000 bcde 0.90898 0.86279 0.91313 0.28320 0.0189 cdE 0.83020 0.78804 0.84484 1.00000 0.0000 abdE 0.89726 0.80769 0.89888 0.09720 0.0124 AdE 0.82121 0.69819 0.79329 0.00000 0.0000 AcdE 0.84629 0.75341 0.85085 0.15080 0.0150 AE 0.79164 0.66169 0.75982 0.00000 0.0000 AdEg 0.86548 0.77095 0.87380 0.33810 0.0199 Abde 0.89543 0.79687 0.89368 0.04930 0.0091 Abcd 0.90344 0.79595 0.89311 0.00300 0.0023 Adfg 0.90586 0.80556 0.91296 0.26250 0.0185 abde 0.89521 0.81170 0.89609 0.08680 0.0118 aDe 0.86033 0.77314 0.92115 1.00000 0.0000 Total number of configurations: 32 Number of permutations: 10000 p-value adjustment method: holm

Author statement

Below are the contribution of each author: Qiufeng Gao: Conceptualization, Funding acquisition, Project administration, resources, supervision, writing – review & editing. Ge Jia: Data curation, Investigation, Visualization, writing – review & editing. En Fu (corresponding author): Conceptualization, Formal analysis, writing – original draft, writing – review & editing. Yunusa Olufadi: Methodology, Supervision, Validation, writing – review & editing.

A CONFIGURATIONAL INVESTIGATION

Yanlin Huang: Data curation, Formal analysis, writing – review & editing.

Role of Funding Sources This work was supported by the National Social Science Fund of China [Grant Number 16BSH089]. The National Social Science Fund of China was not involved in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Contributors Author Qiu-feng Gao and En Fu designed the study and wrote the protocol. Author Ge Jia and Yan-ling Huang searched literature and collected data. Author En Fu conducted data analysis and drafted the manuscript. Author Yunusa Olufadi and Qiu-feng Gao revised the manuscript. Conflict of interest All authors declare that they have no conflicts of interest. Acknowledgements The authors would like to thank Wangshan Wen, an outstanding undergraduate student at Shenzhen University who volunteered his time to help with data collection, data entry, and data mining.

Highlights  Used ecological system theory to investigate adolescent smartphone use disorder.  fsQCA is a rising person-centered approach for researching human behaviors.  Smartphone use disorder became more severe as educational level went up.  The core conditions for smartphone use disorder differed across educational levels.