Associations Between Coping Strategies and Depressive Symptoms in Adolescence: A Longitudinal Perspective

Abstract

Adolescent depression is a major concern for public health and is associated with negative consequences and outcomes. Identifying adolescent characteristics that might relate to the risk for developing depression is crucial. This study investigated bidirectional associations between coping strategies and depressive symptoms over time. The participants were 1,341 secondary school students from the Netherlands ( $\bar{X}$ = 13.91, SD = 0.55, 47.5% girls) who completed self-report questionnaires at six waves over 2.5 years. Cross-lagged models were used to investigate the associations between coping strategies and depressive symptoms. Coping strategies did not predict depressive symptoms over time. However, there was an indication of the reverse relationship. When adolescents experienced elevated depressive symptoms, they used fewer adaptive techniques to target stress. Gender differences were found in the use of avoidance. The inconsistencies of these findings with earlier studies are discussed and provide new directions for future research on working mechanisms underlying depression prevention programs.

Keywords

depression coping adolescents cross-lagged study

Adolescent depression is a major concern for public health, as clinical and subclinical depression rates show a sharp increase in this critical developmental period (Kessler, Petukhova, Sampson, Zaslavsky, & Wittchen, 2012; Reef, Diamantopoulou, Van Meurs, Verhulst, & Van Der Ende, 2009). Most depressed adults experienced their first depressive disorder in adolescence, with the period between 13 and 18 years of age being the most critical time for the onset of depression (Kessler et al., 2005; Kim-Cohen et al., 2003). International epidemiological studies have shown that 2.7% of the 8- to 15-year-old adolescents and 7.5% of the 13- to18-year-old adolescents experience depression (Avenevoli, Swendsen, He, Burstein, & Merikangas, 2015; Merikangas et al., 2010). Although the depression rates are already concerning, a depressed mood and elevated symptoms of depression are not included in these rates and even more common in adolescence, with prevalence rates of 20% in the adolescent population (Meijer, Smit, Schoemaker, & Cuijpers, 2006).

There are several risk factors that are known to be associated with depression in adolescence, such as individual characteristics (e.g., perfectionism), social and family processes (e.g., abuse or bullying), or the presence of a psychiatric disorder. Gender has also proved to be an evident risk factor for the development of depression. Depressive symptoms among boys are relatively stable across adolescence, whereas girls report a significant increase in depressive symptoms from the age of 13 (Avenevoli et al., 2015; D. M. Costello, Swendsen, Rose, & Dierker, 2008).

Besides these, mostly stable risk factors, another factor that is related to the development of depressive symptoms is coping. Substantial individual differences exist in the ways in which adolescents handle events and stressors, and it has been suggested that the use of certain coping strategies is related to greater vulnerability of developing depressive symptoms in response to stressors (Garnefski, Legerstee, Kraaij, van Den Kommer, & Teerds, 2002). Therefore, most depression prevention programs include techniques to enhance coping skills and help adolescents cope with negative thoughts and feelings (Gillham et al., 2007). However, these programs show small effect sizes (Merry et al., 2012; Stice, Shaw, Bohon, Marti, & Rohde, 2009), and there is limited evidence as to whether the techniques included are effective for the prevention of depressive symptoms. To further our understanding of the association between coping and depressive symptoms, the present study investigated whether specific coping strategies are associated with levels of depressive symptoms over time among adolescents.

Coping Strategies

Coping can be defined as “conscious volitional efforts to regulate emotion, cognition, behavior, physiology, and the environment in response to stressful events or circumstances” (Compas, Connor-Smith, Saltzman, Thomsen, & Wadsworth, 2001). Several theorists have studied coping strategies of adolescents, arguing that adolescents who have difficulties with their emotional response to daily stressors experience more severe stress, which may ultimately lead to depression (Aldao, Nolen-Hoeksema, & Schweizer, 2010; Compas, Orosan, & Grant, 1993; Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008). Active coping strategies (e.g., problem-solving) seem to provide a buffering effect, while strategies that involve disengagement from the source of stress put adolescents at risk for developing depressive symptoms (Aldao et al., 2010; Compas et al., 2001; Garnefski, Boon, & Kraaij, 2003; Wadsworth & Compas, 2002).

This was also confirmed in a recent meta-analysis among adolescents, conducted by Schäfer, Naumann, Holmes, Tuschen-Caffier, and Samson (2017), which indicated that avoidance and distraction were related to more depressive symptoms, whereas problem focusing and positive cognitive reframing were related to less depressive symptoms. Nevertheless, there are coping strategies that show mixed findings in relation to psychopathology. For example, social support was found to be unrelated to psychopathology according to a meta-analysis by Compas et al. (2017) but was associated with depressive symptoms in a meta-analysis by Rueger, Malecki, Pyun, Aycock, and Coyle (2016). This might be an example of a strategy that can be effective depending on an individual’s age. Social support might be adaptive for an early adolescent in the regulation of emotions, but it might be maladaptive for the mid-adolescent when it leads to co-rumination (Compas et al., 2017; Stone, Hankin, Gibb, & Abela, 2011). Therefore, researchers should allow for developmental effects, for example, by using longitudinal designs.

Longitudinal designs are especially important in this age group as strategies to cope with stress are not yet stable, and change might occur. Earlier research in the coping area showed that coping strategies increase with age in both number and variety. For example, older adolescents (14- to 18-year-olds) tend to use more emotion regulation strategies, like relaxation or distraction compared with younger adolescents (Donaldson, Prinstein, Danovsky, & Spirito, 2000). Moreover, developmental shifts ensure that adolescents start to use social partners to cope with stress, experience an increased ability to use cognitively complex processes, and change from more overt behavior (e.g., crying or yelling) to less overt strategies like distraction (Thompson & Goodman, 2010; Zimmer-Gembeck & Skinner, 2010).

Nonetheless, according to recent meta-analytic reviews most of these findings have come from cross-sectional studies (Aldao et al., 2010; Compas et al., 2017; Schäfer et al., 2017) and the existing literature has several limitations. First, most of the research between coping and depressive symptoms included adults and had small sample sizes. Second, there is limited research about the reversed effect of depressive symptoms on coping. This might be interesting as it is also possible that the level of symptoms is associated with an increased or decreased use of coping strategies, as this may interfere with the ability to use strategies when facing stress (Compas et al., 2017).

Furthermore, gender differences are often not included in analyses. The risk of depression differs when girls and boys reach adolescence (Avenevoli et al., 2015), and therefore the associations of coping strategies with depressive symptoms may also differ, depending on the moment in development at which coping strategies and depressive symptoms are measured (Carlson & Grant, 2008). Preliminary empirical evidence also suggests that boys and girls differ in coping strategies. According to some studies, girls are more likely to seek support and use more active approach coping strategies compared with boys (Eschenbeck, Kohlmann, & Lohaus, 2007; Frydenberg & Lewis, 1993). However, the findings are inconsistent and there are also studies that have shown opposite effects (Griffith, Dubow, & Ippolito, 2000; Hampel & Petermann, 2005). Allowing for gender differences is important as prevention and intervention programs can adapt to these differences and increase the effectivity.

To address these limitations, the present study provided a longitudinal and bidirectional perspective on the association between coping strategies and depressive symptoms using a sample of 1,341 adolescents who participated in a randomized controlled trial (RCT) examining the effectiveness of a universal depression prevention program. In addition, we allowed for gender differences in the associations between coping strategies and depressive symptoms. We also controlled for condition (intervention versus control), educational level, and ethnicity as they may interfere with depressive symptoms (E. J. Costello & Maughan, 2015). Although coping strategies can be classified in domains, such as adaptive and maladaptive, or primary control (acting directly on the source of stress), secondary control (strategies to adapt to the source of stress), and disengagement coping (strategies to take distance to the source of stress; Compas et al., 2017), the present study will examine single coping strategies in relation to depressive symptoms. This might yield a more nuanced picture of this relationship, which can be translated into practical guidelines that can be incorporated into prevention programs. Therefore, the present study investigates the following strategies: problem focusing, cognitive restructuring, avoidance, distraction, and seeking support in relation to depressive symptoms over time. Although the list of coping strategies is extensive, these are the most used strategies in literature and in coping questionnaires (Skinner, Edge, Altman, & Sherwood, 2003).

Present Study

We analyzed the associations between five single coping strategies and depressive symptoms using bidirectional cross-lagged models. Based on the findings of Garnefski et al. (2002; Garnefski et al., 2003) and Schäfer et al. (2017), we expected to find a negative association over time between problem focusing and positive cognitive reframing and depressive symptoms and we proposed positive associations over time between distraction and avoidance and depressive symptoms. While we expected to find negative associations over time between seeking support and depressive symptoms in this early adolescent phase, we were aware that this could change as adolescents reach the mid-adolescent phase (Compas et al., 2017; Rueger et al., 2016). In addition, we explored whether the reverse direction, depressive symptoms predicting an increased or greater use of specific coping strategies, was true. Given the limited literature on the association between depressive symptoms and coping, these analyses were explorative. Finally, due to inconsistencies regarding the role of gender in coping strategies, we performed additional analyses to explore the role of gender in the relationship between coping strategies and depressive symptoms.

Method

Participants and Procedure

The sample comprised 1,341 participants from 54 classes of nine secondary schools in the southern and middle part of the Netherlands who participated in the RCT of the universal depression prevention program “Op Volle Kracht” (OVK), which translates to “On Full Power” (see Tak, Lichtwarck-Aschoff, Gillham, Van Zundert, & Engels, 2016). This study was approved by the ethical committee of the research institute. There were no differences between the intervention and control group in levels of depressive symptoms and coping strategies on baseline and follow-ups. In addition, there was no effect of OVK on the levels of depressive symptoms or coping strategies at any time points. The participants’ mean age was 13.91 (SD = .55), and 47.5% were girls. Most participants were of Dutch origin (83.1%), and 7% were involved in prevocational education, 51.2% in higher general education, and 41.8% in pre university education.

Schools decided whether to participate in the study. All adolescents in the eighth grade of participating schools were eligible to participate (age range of 11–14). However, the pupils and their parents were informed about the study and were allowed to withdraw from the study at any point. Schools were randomly assigned to the intervention or control condition. Schools in the intervention condition integrated the OVK intervention in the school curriculum during mentor lessons. Schools in the control condition followed the schedule as usual.

Assessments were conducted by administering questionnaires at six time points, baseline (T1), and at 6-month (T2), 12-month (T3), 18-month (T4), 24-month (T5), and 30-month (T6) after baseline. Coping strategies were measured at five time points, and the questionnaire was not administered at T6 due to the length of the questionnaire and for practical reasons. Adolescents received incentives in the form of a gift voucher to complete each assessment. Retention rates were high across all assessments: 96.5% of the participants completed pre-intervention, 89.4% completed post-intervention, 89.3% completed the 6-month follow-up, 83.7% completed the 12-month follow-up, 77.4% completed the 18-month follow-up, and 84.5% completed the 24-month follow-up.

Measures

Coping strategies

We measured coping strategies with the Dutch version of the 54-itemed Children Coping Strategies Checklist–Revised (CCSC-R; de Boo & Wicherts, 2009). Adolescents had to rate whether they use a specific coping style when they face a problem on a 4-point scale ranging from 1 (almost never) to 4 (almost always). The questionnaire comprised five scales and 13 subscales. The five subscales were problem focusing that assessed cognitive decision-making, direct problem-solving, and seeking understanding (12 items; e.g., “Do something to make things better”); positive cognitive reframing that assessed positive and optimistic thinking and control (12 items; e.g., “Tell yourself that you can handle the problem”); distraction strategies that assessed the physical release of emotions and distracting actions (9 items; e.g., “Listen to music”); avoidance strategies that assessed avoidant actions such as repressing and wishful thinking (12 items; e.g., “Just forget about it”); and seeking support, which assessed seeking support to cope with actions and feelings (9 items; e.g., “Tell others how you feel about the problem”). Cronbach’s alpha ranged from .88 to .91 for problem focusing, from .87 to .91 for positive cognitive reframing, from .74 to .79 for distraction, from .73 to .85 for avoidance, and from .91 to .93 for seeking support across the different time points.

Depressive symptoms

We measured depressive symptoms with the Children’s Depression Inventory (CDI; Kovacs, 1985; Timbremont, Braet, & Roelofs, 2008). The CDI is a self-report questionnaire comprising 27 items, each consisting of three statements rated in severity from 0 to 2 (e.g., I don’t feel alone = 0, I often feel alone = 1, I always feel alone = 2). The sum of the scores of depressive symptoms ranged from 0 to 54. Cronbach’s alpha ranged from .84 to .91, indicating a high reliability of all assessments. Item 9 measured suicidal ideation; it was excluded from the questionnaire as this was beyond the scope of this research project.

Demographic variables

This study included the following demographic variables: age, gender, condition, educational level, and ethnicity. We included these variables as covariates as these factors are known to be associated with depressive symptoms and therefore might influence the results (E. J. Costello & Maughan, 2015). Ethnicity was measured by asking adolescents in which country they and their parents were born. When the adolescent or one of the parents was not born in the Netherlands, the adolescent was labeled as having a migration background. Although Tak et al. (2016) found no differences in results between adolescents in the experimental condition versus the control condition, we included this variable to control for possible effects.

Strategy of Analyses

Means, standard deviations, and correlations were computed for all outcome variables included in this study. The associations between coping strategies and depressive symptoms over time (five time points) were tested with five cross-lagged models for each of the five coping strategies. Because the number of parameters to be estimated in the model would increase rapidly by using items as indicators of the latent variables, with the consequence that power to detect important parameters will decrease (Yang & Dunson, 2010) and estimation problems will increase (Sass & Smith, 2006), we decided to use four parcels as indicators of the latent variable depressive symptoms and three parcels for each of the coping strategies. The items of each construct at T1 were allocated to three or four equivalent parts (parcels) according to the item-to-construct balance method (T. D. Little, Cunningham, Shahar, & Widaman, 2002). Parcels for the latent variables at T2 to T5 had identical indicators as the parcels at T1.

Adolescents were nested within 54 classes and classes were nested within nine schools. Correcting for clustering effects with three-level multilevel analysis was not possible because the number of schools was too low. To correct for school effects, eight dummy variables representing nine schools (see Cohen et al., 2003, pp. 303-307) were regressed on intercept and slope as the first step in our analyses. To correct for clustering effects within classes, the TYPE=COMPLEX procedure in Mplus version 7.2 was used (Muthén & Muthén, 1998-2015). The full information robust maximum likelihood (FIML) estimator was used to account for missing values. This estimator requires that missing values are missing at random (MAR), but there are no statistical MAR tests (Nakagawa, 2015). Therefore, we tested for the data mechanism missing completely at random (MCAR) with all outcome variables. Little’s MCAR test was significant, χ²(791) = 865.22, p = .034, indicating that missing values were not MCAR (R. J. Little, 1995). This result does not provide a definite answer for MAR so we used the FIML estimator under the assumption of MAR. Besides χ²(df) and p, two model fit measures were used: (a) the root mean square error of approximation (RMSEA; Byrne, 2005; Kaplan, 2000) and (b) the Bentler comparative fit index (CFI; Kaplan, 2000; Kline, 1998). RMSEA values lower than or equal to .05 are preferred, but values under .08 are acceptable while CFI values above .95 (.90) are indicative of a fair (acceptable) fit. Occasionally, unacceptable or untrusted parameter estimates are found during structural equation modeling (SEM) analyses. In that case, causes and consequences are examined and solutions are found. No unacceptable or untrusted parameter estimates were found in this study.

Cross-lagged analyses were performed for each coping strategy in combination with depressive symptoms. An overall model including depressive symptoms and the five coping strategies would have created a very complex model with too many parameters to be estimated (1,054) in relation to the sample size (1,341). For this reason, we decided to analyze five cross-lagged models separately. All latent variables of the cross-lagged model were regressed on the eight dummy variables for school effects and on the control variables age, condition, education level, and ethnicity. The parameters of the cross-lagged model were estimated freely (in the baseline model). To test equality of regression weights of cross paths, we first tested a model by constraining the cross paths between every consecutive time points to be equal. If this constrained model showed a significant difference with the baseline model (a significant increase of chi-square), post hoc χ² difference tests were used to test which pair(s) of cross paths were significantly different. Because a robust ML estimator was used (due to the COMPLEX procedure), the χ² values were robust and had to be rescaled to unbiased χ² values before using the χ² difference tests (Satorra & Bentler, 2010). This test statistic is known as the Sattora-Bentler (SB)-scaled χ² difference test. The robust ML estimator is also robust against non-normality of the variables (see depressive symptoms in Table A1 in the Appendix); parameters in the cross-lagged models are estimated with robust standard errors.

To explore gender differences in regression weights of cross-lagged relations, we used multiple group analysis. First, a baseline model was estimated without constraints between boys and girls. The fit of this model was the baseline χ². In the second model, the regression weights of cross paths were constrained to be equal across gender, and the χ² of this model was compared with the baseline. A significant difference in χ² is an indication that one or more paths are different for boys and girls.

Results

Descriptive statistics (mean, SD, and correlations for each time point) of depressive symptoms and coping strategies are presented in Tables A1 and A2 in the Appendix. The mean for depressive symptoms was 7.55, which is far below the cutoff of 13 that is used to screen for depressive symptoms (Timbremont, Braet, & Dreessen, 2004), indicating that this is a relative healthy sample that is comparable with other universal prevention studies in non-high-risk population samples (e.g., Challen, Machin, & Gillham, 2014; Horowitz & Garber, 2006). Girls reported a greater use of seeking support on all time points compared with boys. In addition, they reported more problem focusing at T3, T4, and T5, positive cognitive reframing at T5, and avoidance at T1 and T5. Regarding depressive symptoms, girls reported higher levels of depressive symptoms at T1 but lower levels of symptoms at T4 compared with boys. No baseline differences in study variables were found for age, condition, educational level, or ethnicity.

Prior to the final cross-lagged analyses, we tested measurement equivalence for each of the six latent variables over time and across gender. To compare unstandardized regression coefficients of the cross-lagged models, configural equivalence and weak metric equivalence are required (Guenole & Brown, 2014; Keith, 2014). Configural equivalence means that the number of factors and pattern of loadings (but not the strengths of the loadings) are the same over time or across gender. Weak metric equivalence is an additional requirement, and means equal metrics (= equal scale intervals) of a latent variable over time or across gender. Factor loadings define the metric of measurement and represent the strength of the relationship between a factor (latent variable) and indicators (parcels in our case). When factor loadings of a latent variable are equal over time, or across gender, the unit of measurement is also equal and relationships with other (latent) variables can be studied (Wang, Chen, Dai, & Richardson, 2018). This allows the comparisons of unstandardized regression coefficients and covariances over time and across groups (Steenkamp & Baumgartner, 1998).

If the fit measures of the configural model are acceptable, configural equivalence is supported. For weak metric equivalence, the factor loadings of the identical parcels over time are constrained to be equal. The fit of this constrained model is compared with the baseline or configural model. Strong metric equivalence was tested by additionally constraining the corresponding intercepts to be equal over time. The fit of this model was compared with the factor loadings constrained model. A decrease of CFI < .01 and an increase of RMSEA < .015 supported the weak or strong metric equivalence of the longitudinal factor model (Chen, Curran, Bollen, Kirby, & Paxton, 2008; Cheung & Rensvold, 2002). The results are presented in Table A3 in the Appendix. For weak metric equivalence, the decrease of CFI for all models was < .01 and the increase of RMSEA was just a little above the threshold of .015, only for gender in relation to seeking support. Weak measurement invariance is supported for all six latent variables and (with one small exception) for gender. For strong measurement equivalence (allowing comparisons of latent means over time and across groups), a decrease of CFI by more than .01 was found for gender in relation to distraction, avoidance, and seeking support. An increase of RMSEA by more than .015 was found for positive cognitive reframing and gender of positive cognitive reframing, for gender of distraction, for avoidance and gender of avoidance, and for seeking support. This means that strong metric equivalence was not always met for some latent variables in combination with gender. As already noted, for testing paths of the cross-lagged models, weak measurement invariance is sufficient. In the last four columns of Table A3, the range of the factor loadings across five time points are presented, including their means and standard deviations. As can be seen in Table A3, all factor loadings are high indicating that the parcels represent their factor very well for each time point.

Cross-Lagged Models of Coping Strategies With Depressive Symptoms

Problem focusing

The fit of the cross-lagged model was good; χ²(769) = 1426.38, p < .001, CFI = .978, RMSEA = .025, 90% confidence interval (CI): [.023, .027], p(RMSEA) = 1.000. The model chi-square test was significant, indicating a poor model fit. However, in large samples chi-square is almost always significant and global fit measures can be used as an alternative. CFI was > .95 and RMSEA was < .05 and, according to the norms used for global fit measures, these values indicate a good model fit. Problem focusing did not predict depressive symptoms over time. Depressive symptoms had a significantly negative association with problem focusing over time, from T1 to T2, T2 to T3, and T3 to T4, indicating that elevated depressive symptoms were associated with lower levels of problem focusing 6 months later. Figure 1 presents the completely standardized parameter estimates. Using the SB-scaled χ² difference test, we first tested whether the four pairs of cross paths were significantly different by constraining the cross paths between T1 and T2 to be equal, as well as between T2 and T3, between T3 and T4, and between T4 and T5. This test was significant, χ²(4) = 26.08, p < .001. Post hoc difference tests showed that the cross paths were different between T1 and T2, χ²(1) = 8.03, p = .005, between T2 and T3, χ²(1) = 27.05, p < .001, and between T3 and T4, χ²(1) = 3.99, p = .046. This means that the negative associations between depressive symptoms and problem focusing were significantly greater compared with the associations between problem focusing and depressive symptoms at the same time points. The SB-scaled χ² difference test showed no significant difference in cross paths between boys and girls, χ²(8) = 24.26, p = .075.

Figure 1.

Cross-lagged model of depressive symptoms and problem focusing.

Positive cognitive reframing

The fit of the cross-lagged model was χ²(769) = 1521.13, p < .001, CFI = .975, RMSEA = .027, 90% CI: [.025, .029], p(RMSEA) = 1.000. The global fit measures CFI and RMSEA indicated a good fit. Positive cognitive reframing did not predict depressive symptoms over time. Depressive symptoms were significantly negatively associated with positive cognitive reframing over time, from T1 to T2 and from T2 to T3, indicating that higher depressive symptoms were associated with lower levels of positive cognitive reframing 6 months later (see Figure 2). With the SB-scaled χ² difference test, we found that one or more of the four pairs of cross paths were significantly different, χ²(4) = 11.01, p = .026. Post hoc difference tests showed that the cross paths were different between T1 and T2, χ²(1) = 3.95, p = .047, and between T2 and T3, χ²(1) = 4.41, p = .036. This indicates that the negative associations between depressive symptoms and positive cognitive reframing were significantly greater compared with the associations between positive cognitive reframing and depressive symptoms at the same time points. The SB-scaled χ² difference test showed no significant difference in cross paths between boys and girls, χ²(8) = 7.31, p = .504.

Figure 2.

Cross-lagged model of depressive symptoms and positive cognitive reframing.

Distraction

The fit of the cross-lagged model was χ²(769) = 1588.13, p < .001, CFI = .965, RMSEA = .028, 90% CI: [.026, .030], p(RMSEA) = 1.000. The global fit measures indicated a good model fit. Distraction did not predict depressive symptoms over time. Depressive symptoms had a significant negative association with distraction over time, from T3 to T4 (see Figure 3). Using the SB-scaled χ² difference test, we found that cross paths were not significantly different, χ²(4) = 5.26, p = .262. The SB-scaled χ² difference test showed a significant difference in cross paths between boys and girls, χ²(8) = 16.61, p = .034. Post hoc testing showed that cross path Dep T2 to DS T3 was significantly different for boys and girls, χ²(1) = 3.91, p = .048, with B = −.01 and p = .955 for boys and B = .19 and p = .004 for girls. Girls’ elevated depressive symptoms at T2 predicted an increase in distraction strategies at T3, but this association was not found for boys.

Figure 3.

Cross-lagged model of depressive symptoms and distraction.

Avoidance

The fit of the cross-lagged model was χ²(769) = 1491.348, p < .001, CFI = .971, RMSEA = .026, 90% CI: [.024, .028], p(RMSEA) = 1.000. The fit measures CFI and RMSEA indicated a good model fit. No significant cross-associations were found between avoidance and depressive symptoms (see Figure 4). The SB-scaled χ² difference test showed a significant difference in cross paths between boys and girls, χ²(8) = 25.55, p = .001. Post hoc testing revealed that cross path Dep T2 to AV T3 was significantly different for boys and girls, χ²(1) = 10.31, p = .001, with B = −.02 and p = .838 for boys and B = .28 and p = .011 for girls; cross path Dep T3 to AV T4 was significantly different for boys and girls, χ²(1) = 7.92, p = .005, with B = −.29 and p = .005 for boys and B = .09 and p = .394 for girls; and cross path Dep T4 to AV T5 was significantly different for boys and girls, χ²(1) = 6.17, p = .013, with B = .02 and p = .824 for boys and B = .24 and p = .001 for girls. The results suggest that elevated depressive symptoms in boys at T3 were associated with lower levels of avoidance strategies 6 months later. Regarding girls, elevated depressive symptoms at T2 and T4 were associated with an increase in avoidance strategies 6 months later.

Figure 4.

Cross-lagged model of depressive symptoms and avoidance.

Seeking support

The fit of the cross-lagged model was χ²(769) = 1566.92, p < .001, CFI = .975, RMSEA = .028, 90% CI: [.026, .030], p(RMSEA) = 1.000. The fit measures CFI and RMSEA indicated a good model fit. Seeking support at T2 showed significantly negative association with depressive symptoms at T3 and vice versa. Depressive symptoms showed a significantly negative associations with seeking support over time, from T1 to T2 and from T2 to T3, indicating that elevated depressive symptoms at T1 and T2 were associated with lower levels of seeking support 6 months later (see Figure 5). With the SB-scaled χ² difference test, we found that cross paths were significantly different, χ²(4) = 15.17, p = .004. Post hoc difference tests showed that the cross paths between T2 and T3, χ²(1) = 9.44, p = .002, were significantly different. This indicates that the negative associations between depressive symptoms and seeking support were significantly greater at 6 months compared with the associations between seeking support and depressive symptoms on the same time points. The SB-scaled χ² difference test showed no significant difference in cross paths between boys and girls, χ²(8) = 10.91, p = .207.

Figure 5.

Cross-lagged model of depressive symptoms and seeking support.

Discussion

The aim of the present study was to investigate the bidirectional associations between coping strategies and depressive symptoms over time. It was expected that adolescents high in problem focusing, positive cognitive reframing, and seeking support would experience fewer depressive symptoms. Likewise, we expected that adolescents high in distraction and avoidance would experience more depressive symptoms. The analyses regarding the reversed effect of depressive symptoms on coping strategies were exploratory. In addition, we explored the role of gender differences in these relations.

In contrast to the hypotheses, no robust significant associations between coping strategies and depressive symptoms emerged. Of the 40 associations tested, we found that coping affected depressive symptoms over time only in one association. However, there was an indication for the reverse relationship. When adolescents experienced elevated depressive symptoms, they used fewer adaptive techniques to target stress in practical ways, using, for example, problem-solving and cognitive decision-making. In addition, depressive symptoms had a negative association with positive cognitive reframing and seeking support but only at the first two time points. One cross path showed a significant negative association between depressive symptoms and distraction. Although this effect was not consistent over time, it was contrary to expectations and in contrast with research that classified distraction as maladaptive (Aldao et al., 2010).

Significant gender differences were found in depressive symptoms and avoidance across three time points. For boys, a higher score of depressive symptoms was related with lower levels of avoidance strategies. For girls, a higher score on depressive symptoms was related to an increase in avoidance and distraction strategies. However, significant findings among gender differences were neither consistent nor robust. Future studies should clarify these findings to draw further conclusions.

The prospective one-way association between depressive symptoms and the use of problem focusing and positive cognitive reframing was inconsistent with our hypotheses, previous studies (e.g., Aldao & Nolen-Hoeksema, 2010; Garnefski et al., 2003), and models in which bidirectional associations between cognitive variables and depressive symptoms are integrated, such as the transactional framework (Hankin & Abramson, 2001). In these models, maladaptive cognitive schemas predicted depressive symptoms and vice versa. Inconsistencies with previous studies could be caused by differences in the developmental period that were captured or the timing and spacing of measurement intervals (Collins & Graham, 2002); however, there could be other reasons for this inconsistency.

One explanation could be that adolescents with elevated depressive symptoms are less able to use active problem-solving and they tend to revert to other strategies, which suggests the presence of a third factor. This argument is also supported by the meta-analysis by Compas et al. (2017). The researchers stated that due to the lack of longitudinal designs, little is known about the actual direction of coping with psychopathology symptoms. While researchers in general might be more interested in the effect of coping strategies on symptoms for practical reasons, Compas et al. (2017) argued that high levels of symptoms may impede the development of these skills or the ability to use them effectively. It would be interesting to investigate the initial level of symptoms or specific characteristics of depressive symptoms in relation to coping strategies.

Following this argument, another explanation might concern methodological issues. Most studies included cross-sectional designs; hence, the stability over time might have been missed. Multiple subsequent time points should be included in studies to generate substantial conclusions regarding the associations between coping strategies and depressive symptoms (Sameroff & Mackenzie, 2003). The current finding regarding significant associations between depressive symptoms and problem focusing was consistent across four measurements, which increases the reliability of our conclusion. Moreover, the associations between depressive symptoms and coping strategies were analyzed by including the within time correlations and stability in depressive symptoms and coping strategies subscales over time. Inclusion of these factors is important as this provides a more accurate image of the complex associations between depressive symptoms and coping strategies. Nevertheless, current findings need to be replicated before firm conclusions can be made.

Although the findings in the coping literature provide evidence for gender-specific coping preferences, little is known about the direction and effect of coping strategies on psychopathology symptoms. Adult studies have shown that females often report using seeking support, rumination, positive self-talk, and problem-solving while males tend to use avoidant strategies or passivity (Eschenbeck et al., 2007; Tamres, Janicki, & Helgeson, 2002). The differences between men and women regarding the efficacy of specific strategies have not been clarified yet. This study showed that for boys, elevated depressive symptoms were associated with lower levels of avoidance strategies, whereas in girls, elevated depressive symptoms were associated with higher levels of avoidance strategies. This is not in accordance with the findings in a recent meta-analysis by Schäfer et al. (2017), in which it was stated that maladaptive strategies, such as avoidance, were more strongly associated with depressive symptoms among men compared with women. Despite the fact that literature on this subject in adolescence is scarce, adult literature suggests that there are both differences and similarities in the relationship between coping and depressive symptoms in men and women. Holahan, Moos, Holahan, Brennan, and Schutte (2005) found that for women, avoidant coping was linked to depressive symptoms both directly as well as indirectly through life stressors. For men, life stressors completely explained the relationship between avoidant coping and depressive symptoms. Future research should examine the effect of gender on the relation between depressive symptoms and avoidance more closely to replicate our findings and to examine the consequences of this difference.

Strengths and Limitations

This study has some strengths and limitations that provide direction for future research. We used longitudinal data from a large general community sample of adolescents, which facilitates the generalization of the results. In addition, a sample drawn from the general population reduces biases known in clinical research, such as comorbidity, severity of symptoms, and treatment seeking (Goodman et al., 1997). However, most adolescents experienced no depressive symptoms and a small group experienced low to mild depressive symptoms which might have limited the results because of the lack of variance and attenuation of the associations that were examined. In addition, since the study included relatively healthy adolescents, the associations between coping strategies and depressive symptoms might be different for adolescents who experience depression or a higher level of depressive symptoms. Furthermore, the study was based on self-reported data and could therefore be vulnerable to biases. Also, we did not include an assessment regarding sources of the stress that the adolescents experienced, which is an important limitation because some strategies might be adaptive in one situation but not adaptive in another (e.g., problem focusing is less adaptive when one needs to cope with the loss of a loved one). Therefore, we recommend that future studies should include more information about the chronicity or controllability of the stressors.

Regarding the statistical/methodological strengths and limitations, we made use of item parceling which has several advantages and disadvantages (see T. D. Little et al., 2002). Regarding this discussion, researchers concluded that parceling is effective when items within a parcel measure the same construct and this was the case in the current study (Bandalos & Finney, 2001). A statistical limitation is that the large autocorrelations of the latent variables over time could decrease the chances for finding cross-lagged effects. Controlling for past levels of predictors will dramatically reduce the magnitude of cross-lagged effects, especially if autoregressive paths (stability paths) are strong (Adachi & Willoughby, 2015). However, small effect sizes for cross-lagged effects (standardized regression weights) in longitudinal models are the rule rather than the exception. Current guidelines to judge effect sizes are mostly based on cross-sectional research (were stability over time is not controlled). The small effect sizes in longitudinal studies must be assessed according to standards based on longitudinal research. In models with high stability paths, as in this study, small cross-lagged significant effects are not trivial (Adachi & Willoughby, 2015).

Implications for Practice and Future Research

This study has implications for practice and provides interesting new directions for future research. First, the finding that depressive symptoms only seem to interfere with the ability to use problem-focused coping strategies rather than the other way around when facing stress implies that altering coping strategies through depression prevention would not necessarily decrease depressive symptoms. This is in line with a review of Stice et al. (2009) who stated that the content of depression prevention programs was weakly or not associated with a decrease in depressive symptoms. Although more research is necessary to draw conclusions about the effectiveness or ineffectiveness of targeting coping strategies in prevention or intervention, practitioners and researchers should investigate other concepts in the prevention of depression, such as examining techniques that are more behavioral rather than cognitive.

Behavioral activation technique, for instance, focuses on mood monitoring and daily activities to increase positive activities and positive interactions with the environment. Behavioral activation has already proven to be effective in depression treatments and would be a cost-effective solution for prevention programs targeting adolescents with elevated depressive symptoms (Cuijpers et al., 2007). To investigate the effect of activation on adolescents’ mood, the experience sample method (ESM) is an appropriate methodology. ESM is a daily diary method that requires the participants are to report their thoughts and feelings in the moment at multiple time intervals, using, for instance, smart phone mobile apps. In addition to investigating fluctuations over short-time rather than long-term developments (de Haan-Rietdijk, Voelkle, Keijsers, & Hamaker, 2017), technological developments have made it possible to give participants personalized feedback and thus give insight into their mood and activities (Myin-Germeys, Klippel, Steinhart, & Reininghaus, 2016). Research has shown that this is a promising and feasible solution to increase positive affect in people with depressive symptoms (Van Roekel et al., 2017).

Second, future research should include longitudinal and dynamic methodology to investigate the concept of coping in relation to depressive symptoms to draw more substantial conclusions. Facing these demands, a promising solution would be to utilize person-centered and data-driven methods to identify profiles of adolescents who use similar patterns of coping strategies (Aldao, 2013). The few studies that have used a person-centered approach have shown that this approach can be successful in extracting coping profiles (Dixon-Gordon, Aldao, & De Los Reyes, 2015; Lougheed & Hollenstein, 2012; Van den Heuvel, Stikkelbroek, Bodden, & van Baar, 2020). Moreover, this provides the opportunity to investigate divergence in developmental trajectories of coping strategies as well as the development of depressive symptoms. Regarding coping, it would be interesting to investigate whether strategies are stable across adolescence or transform with development. Prevention programs could benefit from information on the course of depressive symptoms by adapting programs to the needs of subgroups or to establish the ideal starting point for prevention. Innovative approaches in future research are required to improve our prevention programs focused on reducing depression among adolescents.

Footnotes

Appendix

Table A5.

Unstandardized Results of Cross-Lagged Analyses Between Coping Strategies and Depressive Symptoms.

Model	Path	B	SE	p
1. Problem Focusing	PF1–Dep2	−.01	−.01	.40
	Dep1–PF2	−.23	.08	.00
	PF2–Dep3	−.01	.01	.25
	Dep2–PF3	−.29	.08	.00
	PF3–Dep4	−.01	.01	.38
	Dep3–PF4	−.24	.11	.03
	PF4–Dep5	−.00	.01	.92
	Dep4–PF5	−.14	.10	.17
2. Positive Cognitive Reframing	PCF1–Dep2	−.01	.01	.22
	Dep1–PCF2	−.20	.09	.02
	PCF2–Dep3	−.02	.01	.08
	Dep2–PCF3	−.19	.09	.03
	PCF3–Dep4	−.01	.02	.58
	Dep3–PCF4	−.16	.09	.08
	PCF4–Dep5	−.00	.01	.75
	Dep4–PCF5	−.04	.08	.61
3. Distraction	DS1–Dep2	.00	.01	.92
	Dep1–DS2	−.03	.06	.60
	DS2–Dep3	.01	.02	.47
	Dep2–DS3	.07	.06	.27
	DS3–Dep4	.01	.02	.59
	Dep3–DS4	−.17	.08	.04
	DS4–Dep5	.03	.02	.18
	Dep4–DS5	.05	.07	.51
4. Avoidance	AV1–Dep2	.00	.01	.90
	Dep1–AV2	−.07	.09	.40
	AV2–Dep3	.02	.02	.43
	Dep2–AV3	.11	.07	.09
	AV3–Dep4	.02	.02	.33
	Dep3–AV4	−.12	.08	.10
	AV4–Dep5	.02	.02	.22
	Dep4–AV5	.12	.08	.11
5. Seeking support	SS1–Dep2	−.01	.01	.49
	Dep1–SS2	−.28	.11	.01
	SS2–Dep3	−.03	.01	.00
	Dep2–SS3	−.24	.09	.01
	SS3–Dep4	.00	.01	.80
	Dep3–SS4	−.17	.10	.09
	SS4–Dep5	−.01	.01	.16
	Dep4–SS5	−.13	.11	.22

Note. Dep = depressive symptoms, PF = problem focusing, PCF = positive cognitive reframing, AV = avoidance, DS = distraction, SS = seeking support.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The municipality of Oss, The Netherlands, provided funding for this study. ZonMw, The Netherlands Organization for Health Research and Development, funded the original study of .

Ethical Approval

The current submission does not overlap with any other published, in press, or in preparation articles, books, or proceedings and has not been posted on a website. Our research is not under consideration elsewhere and has been conducted in accordance with ethical standards in the field.

Informed Consent

Passive parental consent was obtained, and all students included in the study participated voluntarily.

Data

The dataset generated analyzed during the current study are available from the corresponding author on reasonable request.

ORCID iD

Karlijn W. J. de Jonge-Heesen

Author Biographies

Karlijn W. J. de Jonge-Heesen is PhD candidate and psychologist at GGZ Oost Brabant. She is specialized in psychopathology in adolescence, especially depression and suicidality.

Sanne P. A. Rasing is working as senior research fellow at GGZ Oost Brabant and University Utrecht. She has a particular research interest in research in practice.

Ad A. Vermulst is statistician and connected to GGZ Oost Brabant. Although he is formally retired, he assists several researchers with great enthusiasm.

Yuli R. Tak works as clinical psychologist at Radboud UMC. In her work as researcher, she is specialized in depression prevention in adolescence.

Rutger C. M. E. Engels works as full director at Erasmus University Rotterdam. He is responsible for education, research and impact, policies for academic employees, science communication, and information for students. He is also professor of development psychopathology at Erasmus School of Social and Behavioural Sciences (ESSB).

Daan H. M. Creemers works as clinical psychologist and senior research fellow at GGZ Oost Brabant. In addition, he is head of the child- and adolescent department.

References

Adachi

Willoughby

(2015). Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science. European Journal of Developmental Psychology, 12, 116-128.

Aldao

(2013). The future of emotion regulation research: Capturing context. Perspectives on Psychological Science, 8, 155-172.

Aldao

Nolen-Hoeksema

(2010). Specificity of cognitive emotion regulation strategies: A transdiagnostic examination. Behaviour Research and Therapy, 48, 974-983.

Aldao

Nolen-Hoeksema

Schweizer

(2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30, 217-237.

Avenevoli

Swendsen

J.-P.

Burstein

Merikangas

K. R.

(2015). Major depression in the National Comorbidity Survey–Adolescent Supplement: Prevalence, correlates, and treatment. Journal of the American Academy of Child & Adolescent Psychiatry, 54, 37-44.e32.

Bandalos

D. L.

Finney

S. J.

(2001). Item parceling issues in structural equation modeling. In n Marcoulides

G. A.

Schumacker

R. E.

(Eds.), New developments and techniques in structural equation modeling (pp. 269-296). Mahwah, NJ: Lawrence Erlbaum.

Byrne

B. M.

(2005). Factor analytic models: Viewing the structure of an assessment instrument from three perspectives. Journal of Personality Assessment, 85, 17-32.

Carlson

G. A.

Grant

K. E.

(2008). The roles of stress and coping in explaining gender differences in risk for psychopathology among African American urban adolescents. Journal of Early Adolescence, 28, 375-404.

Challen

A. R.

Machin

S. J.

Gillham

J. E.

(2014). The UK Resilience Programme: A school-based universal nonrandomized pragmatic controlled trial. Journal of Consulting and Clinical Psychology, 82, 75-89.

10.

Chen

Curran

P. J.

Bollen

K. A.

Kirby

Paxton

(2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36, 462-494.

11.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255.

12.

Cohen

West

Aiken

(2003). Applied multiple correlation/regression analysis for the behavioral sciences. Abingdon, UK: Taylor & Francis.

13.

Collins

L. M.

Graham

J. W.

(2002). The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: Temporal design considerations. Drug and Alcohol Dependence, 68, 85-96.

14.

Compas

B. E.

Connor-Smith

J. K.

Saltzman

Thomsen

A. H.

Wadsworth

M. E.

(2001). Coping with stress during childhood and adolescence: Problems, progress, and potential in theory and research. Psychological Bulletin, 127, 87-127.

15.

Compas

B. E.

Jaser

S. S.

Bettis

A. H.

Watson

K. H.

Gruhn

M. A.

Dunbar

J. P.

… Thigpen

J. C.

(2017). Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychological Bulletin, 143, 939-991.

16.

Compas

B. E.

Orosan

P. G.

Grant

K. E.

(1993). Adolescent stress and coping: Implications for psychopathology during adolescence. Journal of Adolescence, 16, 331-349.

17.

Costello

D. M.

Swendsen

Rose

J. S.

Dierker

L. C.

(2008). Risk and protective factors associated with trajectories of depressed mood from adolescence to early adulthood. Journal of Consulting and Clinical Psychology, 76(2), 173-183.

18.

Costello

E. J.

Maughan

(2015). Annual research review: Optimal outcomes of child and adolescent mental illness. Journal of Child Psychology and Psychiatry, 56, 324-341.

19.

Cuijpers

Van Straten

Warmerdam

(2007). Behavioral activation treatments of depression: A meta-analysis. Clinical Psychology Review, 27, 318-326.

20.

de Boo

G. M.

Wicherts

J. M

. (2009). Assessing cognitive and behavioral coping strategies in children. Cognitive Therapy and Research, 33(1), Article 1.

21.

de Haan-Rietdijk

Voelkle

M. C.

Keijsers

Hamaker

E. L

. (2017). Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8, Article 1849.

22.

Dixon-Gordon

K. L.

Aldao

De Los Reyes

(2015). Repertoires of emotion regulation: A person-centered approach to assessing emotion regulation strategies and links to psychopathology. Cognition and Emotion, 29, 1314-1325.

23.

Donaldson

Prinstein

M. J.

Danovsky

Spirito

(2000). Patterns of children’s coping with life stress: Implications for clinicians. American Journal of Orthopsychiatry, 70, 351-359.

24.

Ekers

Webster

Van Straten

Cuijpers

Richards

Gilbody

(2014). Behavioural activation for depression: An update of meta-analysis of effectiveness and sub group analysis. PLoS ONE, 9(6), Article e100100.

25.

Eschenbeck

Kohlmann

C.-W.

Lohaus

(2007). Gender differences in coping strategies in children and adolescents. Journal of Individual Differences, 28, 18-26.

26.

Frydenberg

Lewis

(1993). Boys play sport and girls turn to others: Age, gender and ethnicity as determinants of coping. Journal of Adolescence, 16, 253-266.

27.

Garnefski

Boon

Kraaij

(2003). Relationships between cognitive strategies of adolescents and depressive symptomatology across different types of life event. Journal of Youth and Adolescence, 32, 401-408.

28.

Garnefski

Legerstee

Kraaij

van Den Kommer

Teerds

(2002). Cognitive coping strategies and symptoms of depression and anxiety: A comparison between adolescents and adults. Journal of Adolescence, 25, 603-611.

29.

Gillham

J. E.

Reivich

K. J.

Freres

D. R.

Chaplin

T. M.

Shatté

A. J.

Samuels

… Gallop

(2007). School-based prevention of depressive symptoms: A randomized controlled study of the effectiveness and specificity of the Penn Resiliency Program. Journal of Consulting and Clinical Psychology, 75, 9-19.

30.

Goodman

S. H.

Lahey

B. B.

Fielding

Dulcan

Narrow

Regier

(1997). Representativeness of clinical samples of youths with mental disorders: A preliminary population-based study. Journal of Abnormal Psychology, 106, 3-14.

31.

Griffith

M. A.

Dubow

E. F.

Ippolito

M. F.

(2000). Developmental and cross-situational differences in adolescents’ coping strategies. Journal of Youth and Adolescence, 29, 183-204.

32.

Guenole

Brown

(2014). The consequences of ignoring measurement invariance for path coefficients in structural equation models. Frontiers in Psychology, 5, Article 980.

33.

Hampel

Petermann

(2005). Age and gender effects on coping in children and adolescents. Journal of Youth and Adolescence, 34(2), 73-83.

34.

Hankin

B. L.

Abramson

L. Y.

(2001). Development of gender differences in depression: An elaborated cognitive vulnerability–transactional stress theory. Psychological Bulletin, 127(6), 773.

35.

Holahan

C. J.

Moos

R. H.

Holahan

C. K.

Brennan

P. L.

Schutte

K. K.

(2005). Stress generation, avoidance coping, and depressive symptoms: A 10-year model. Journal of Consulting and Clinical Psychology, 73, 658-666.

36.

Horowitz

J. L.

Garber

(2006). The prevention of depressive symptoms in children and adolescents: A meta-analytic review. Journal of Consulting and Clinical Psychology, 74, 401-415.

37.

Kaplan

(2000). Structural equation modeling: Foundations and extensions. Thousand Oaks, CA: Sage.

38.

Keith

T. Z.

(2014). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. New York, NY: Routledge.

39.

Kessler

R. C.

Berglund

Demler

Jin

Merikangas

K. R.

Walters

E. E.

(2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593-602.

40.

Kessler

R. C.

Petukhova

Sampson

N. A.

Zaslavsky

A. M.

Wittchen

H. U.

(2012). Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. International Journal of Methods in Psychiatric Research, 21, 169-184.

41.

Kim-Cohen

Caspi

Moffitt

T. E.

Harrington

Milne

B. J.

Poulton

(2003). Prior juvenile diagnoses in adults with mental disorder: Developmental follow-back of a prospective-longitudinal cohort. Archives of General Psychiatry, 60, 709-717.

42.

Kline

R. B.

(1998). Principles and practice of structural equation modeling. New York, NY: Guilford Press.

43.

Kovacs

(1985). The Children’s Depression Inventory (CDI). Psychopharmacology Bulletin, 21, 995-998.

44.

Little

R. J.

(1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112-1121.

45.

Little

T. D.

Cunningham

W. A.

Shahar

Widaman

K. F.

(2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173.

46.

Lougheed

J. P.

Hollenstein

(2012). A limited repertoire of emotion regulation strategies is associated with internalizing problems in adolescence. Social Development, 21, 704-721.

47.

Meijer

Smit

Schoemaker

Cuijpers

(2006). Gezond verstand. Evidence-based preventie van psychische stoornissen. Retrieved from https://www.rivm.nl/publicaties/gezond-verstand-evidence-based-preventie-van-psychische-stoornissen

48.

Merikangas

K. R.

J.-P.

Brody

Fisher

P. W.

Bourdon

Koretz

D. S.

(2010). Prevalence and treatment of mental disorders among US children in the 2001–2004 NHANES. Pediatrics, 125, 75-81.

49.

Merry

S. N.

Hetrick

S. E.

Cox

G. R.

Brudevold-Iversen

Bir

J. J.

McDowell

(2012). Cochrane Review: Psychological and educational interventions for preventing depression in children and adolescents. Evidence-Based Child Health: A Cochrane Review Journal, 7, 1409-1685.

50.

Muthén

(1998-2015). Mplus user’s guide (Version 6). Los Angeles, CA: Author.

51.

Myin-Germeys

Klippel

Steinhart

Reininghaus

(2016). Ecological momentary interventions in psychiatry. Current Opinion in Psychiatry, 29, 258-263.

52.

Nakagawa

(2015). Missing data: Mechanisms, methods and messages. Ecological Statistics: Contemporary Theory and Application, 81-105.

53.

Nolen-Hoeksema

Wisco

B. E.

Lyubomirsky

(2008). Rethinking rumination. Perspectives on Psychological Science, 3, 400-424.

54.

Reef

Diamantopoulou

Van Meurs

Verhulst

Van Der Ende

(2009). Child to adult continuities of psychopathology: A 24-year follow-up. Acta Psychiatrica Scandinavica, 120, 230-238.

55.

Rueger

S. Y.

Malecki

C. K.

Pyun

Aycock

Coyle

(2016). A meta-analytic review of the association between perceived social support and depression in childhood and adolescence. Psychological Bulletin, 142, 1017-1067.

56.

Sameroff

A. J.

Mackenzie

M. J.

(2003). Research strategies for capturing transactional models of development: The limits of the possible. Development and Psychopathology, 15, 613-640.

57.

Sass

D. A.

Smith

P. L.

(2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13, 566-586.

58.

Satorra

Bentler

P. M.

(2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243-248.

59.

Schäfer

J. Ö.

Naumann

Holmes

E. A.

Tuschen-Caffier

Samson

A. C.

(2017). Emotion regulation strategies in depressive and anxiety symptoms in youth: A meta-analytic review. Journal of Youth and Adolescence, 46, 261-276.

60.

Skinner

E. A.

Edge

Altman

Sherwood

(2003). Searching for the structure of coping: A review and critique of category systems for classifying ways of coping. Psychological Bulletin, 129, 216-269.

61.

Steenkamp

J.-B. E.

Baumgartner

(1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78-90.

62.

Stice

Shaw

Bohon

Marti

C. N.

Rohde

(2009). A meta-analytic review of depression prevention programs for children and adolescents: Factors that predict magnitude of intervention effects. Journal of Consulting and Clinical Psychology, 77, 486-503.

63.

Stone

L. B.

Hankin

B. L.

Gibb

B. E.

Abela

J. R.

(2011). Co-rumination predicts the onset of depressive disorders during adolescence. Journal of Abnormal Psychology, 120, 752-757.

64.

Tak

Y. R.

Lichtwarck-Aschoff

Gillham

J. E.

Van Zundert

R. M.

Engels

R. C.

(2016). Universal school-based depression prevention “Op Volle Kracht”: A longitudinal cluster randomized controlled trial. Journal of Abnormal Child Psychology, 44, 949-961.

65.

Tamres

L. K.

Janicki

Helgeson

V. S.

(2002). Sex differences in coping behavior: A meta-analytic review and an examination of relative coping. Personality and Social Psychology Review, 6, 2-30.

66.

Thompson

R. A.

Goodman

(2010). Development of emotion regulation: More than meets the eye. In Kring

A. M.

Sloan

D. M.

(Eds.), Emotion regulation and psychopathology: A transdiagnostic approach to etiology and treatment (pp. 38-58). New York, NY: Guilford Press.

67.

Timbremont

Braet

Dreessen

(2004). Assessing depression in youth: Relation between the Children’s Depression Inventory and a structured interview. Journal of Clinical Child and Adolescent Psychology, 33, 149-157.

68.

Timbremont

Braet

Roelofs

(2008). Children’s Depression Inventory. Handleiding (herziene uitgave). Amsterdam, The Netherlands: Pearson.

69.

Van den Heuvel

Stikkelbroek

Bodden

D. H. M.

van Baar

A. L

. (2020). Coping with stressful life events: Cognitive emotion regulation profiles and depressive symptoms in adolescents. Development and Psychopathology, 32(3), 985-995.

70.

Van Roekel

Vrijen

Heininga

V. E.

Masselink

Bos

E. H.

Oldehinkel

A. J

. (2017). An exploratory randomized controlled trial of personalized lifestyle advice and tandem skydives as a means to reduce anhedonia. Behavior Therapy, 48, 76-96.

71.

Wadsworth

M. E.

Compas

B. E.

(2002). Coping with family conflict and economic strain: The adolescent perspective. Journal of Research on Adolescence, 12, 243-274.

72.

Wang

Chen

C.-C.

Dai

C.-L.

Richardson

G. B.

(2018). A call for, and beginner’s guide to, measurement invariance testing in evolutionary psychology. Evolutionary Psychological Science, 4, 166-178.

73.

Yang

Dunson

D. B.

(2010). Bayesian semiparametric structural equation models with latent variables. Psychometrika, 75, 675-693.

74.

Zimmer-Gembeck

M. J.

Skinner

E. A.

(2010). Adolescents coping with stress: Development and diversity. School Nurse News, 27(2), 23-28.