Depressive Symptoms and Loneliness in Early Adolescence: The Role of Empathy and Emotional Self-Efficacy

Abstract

Knowledge about the role of empathy, emotional self-efficacy, and loneliness on early adolescents’ depressive symptoms is scarce. The main aims of the study were to investigate the following: (a) the role of empathy and emotional self-efficacy (additive and interactive) on loneliness and depressive symptoms, taking into account gender differences and (b) the possible mediating role of loneliness in the relationship between emotional predictors and depressive symptoms. Three hundred forty-eight Italian early adolescents (48% girls; mean age, 13; SD = 0.3) completed an anonymous self-report questionnaire at two time points (1 year apart). Structural equation modeling (SEM) was used to analyze data. Results indicated the following: (a) high empathy and low emotional self-efficacy were related to higher subsequent loneliness and depressive symptoms; (b) emotional self-efficacy moderated the relationship between empathy and loneliness and, only for girls, between empathy and depressive symptoms; (c) no mediation role of loneliness between emotional predictors (empathy and emotional self-efficacy) and depressive symptoms was found. Results are discussed in relation to preventive interventions targeting early adolescents.

Keywords

depressive symptoms loneliness empathy emotional self-efficacy gender differences

Introduction

Literature has repeatedly stressed that symptoms of depression increase developmentally from childhood to adolescence (McLaughlin & King, 2015; Olino, Stepp, Keenan, Loeber, & Hipwell, 2014; van Oort, Greaves-Lord, Verhulst, Ormel, & Huizink, 2009).

Early adolescence is a period of great developmental challenges. During early adolescence, boys and girls have to face the pubertal transition that is associated with many physical and psychological changes. Pubertal transition can be linked not only with the increase of emotional and social skills but also with the increasing vulnerability for depressive symptoms, especially for girls (Hamilton, Hamlat, Stange, Abramson, & Alloy, 2014). In fact, gender differences in depression begin to emerge during early adolescence (around 12–13 years of age) and become more pronounced across adolescence, with girls twice as likely to be depressed as boys (Avenevoli, Knight, Kessler, & Merikangas, 2008; Hankin, Mermelstein, & Roesch, 2007; McLaughlin & King, 2015). Even though early adolescents’ depressive symptoms often do not reach the clinical threshold for diagnosis of depression, they negatively influence school achievement, peer and family relationships, and global psychosocial well-being (Branje, Hale, Frijns, & Meeus, 2010; Horowitz & Garber, 2006). Moreover, scientific literature proved that depressive symptoms in early adolescence are predictive of depression in adolescence and adulthood (Hankin, 2015) and depressed early adolescents are at high risk for suicide (Nock et al., 2013). Given these negative implications, it is relevant to understand which factors might contribute to the development of depressive symptoms among early adolescents.

In this regard, the lack of emotional and social competencies has been identified as a risk factor of depression in middle and late adolescence (Abela & Hankin, 2008). However, less research has been conducted on the role of these aspects in early adolescence, despite the fact that it is a crucial period for the acquisition of the ability of understanding, sharing, managing, and controlling emotions, as well as of forming and maintaining peer relationships (Santrock, 2019). For this reason, in the present study, we focused on the role of empathy (understanding and sharing others’ emotions), emotional self-efficacy (managing and controlling one’s emotions), and peer-related loneliness with respect to depressive symptoms in early adolescence. Understanding the role of these variables as possible risk or protective factors in early adolescence would allow to timely individuate boys and girls more at risk for subsequent depression and to implement timely interventions to promote positive developmental trajectories.

Empathy

Empathy is defined as an emotional response to the affective state or situation of other people, and it is considered a multidimensional construct, including the ability to recognize and understand another’s feelings (cognitive dimension) and to share and vicariously experience those emotions (affective dimension; Feshbach, 1997; Hoffman, 2008). During early adolescence, the improvement of formal thinking and the changes in moral reasoning make possible the acquisition of a more mature form of empathy than that of childhood (Allemand, Steiger, & Fend, 2015; Eisenberg, Spinrad, & Morris, 2013; van Lissa et al., 2014). Although literature mostly focused on empathy during adolescence, research targeting early adolescents is scarce. Concerning gender differences, most research found that adolescent girls generally report higher empathy than boys, especially in the affective component (Grazzani, Corti, Ornaghi, Antoniotti, & Pepe, 2015; Mestre, Samper, Frias, & Tur, 2009). Research on the role of empathy with respect to the adolescents’ adjustment stressed the positive role of empathy in increasing adolescents’ interpersonal and mental health outcomes (Chow, Ruhl, & Buhrmester, 2013). Affective and cognitive empathy are related to adolescents’ interpersonal functioning, promoting prosocial behavior (van der Graaff, Carlo, Crocetti, Koot, & Branje, 2018) and inhibiting aggressive and externalizing problem behaviors (Batanova & Loukas, 2012). Low empathy is instead associated with more conflicts and externalizing behavior, particularly aggression and bullying (Euler, Steinlin, & Stadler, 2017). In particular, individuals with low empathy are less able to imagine the consequences of their behavior and the potential harm they might cause to others.

Despite many studies highlighting a positive role of empathy with respect to adolescents’ adjustment, contrasting results emerge when considering the relationships between high levels of empathy and depressive symptoms. Although some studies suggested that deficits in empathy were related to higher depression among adolescents (Domes et al., 2016; Schreiter, Pijnenborg, & Aan Het Rot, 2013), other research suggested that also high levels of empathy might expose individuals to internalizing problems (Calandri et al., 2019). In particular, some studies found that extreme sympathy and compassion, as a response to other people’s suffering, may lead to consequent prolonged and exhausting empathic reactions or “empathic fatigue” (Oakley, Knafo, & McGrath, 2012). This situation of personal distress is related to withdrawal, avoidance of empathy-inducing situations, and depression (Decety & Lamm, 2009; Schreiter et al., 2013). Smith and Rose (2011), in a study on adolescents’ friendship, developed the construct of “empathetic distress” to define a situation of strongly sharing friends’ suffering to the point of taking on the others’ distress and experiencing it as one’s own. In this study, higher social perspective taking, associated with co-rumination with friends, was associated with empathetic distress, especially among girls (Smith & Rose, 2011). Starting from this literature, some recent research explored if the association between high empathy and depressive symptoms might be moderated by other variables, such as the ability of regulating emotions. In particular, a recent study on young adults (Tully, Ames, Garcia, & Donohue, 2016) stressed that extremely high levels of empathy were associated with higher depression, especially in combination with poor emotion regulation, and only moderate empathy was a protective factor against depression.

Emotional Self-Efficacy

Improving the ability of regulating emotions is a crucial developmental task in early adolescence, a period of life characterized by emotional intensity and instability (Zins & Elias, 2006). In the present study, we focused on the construct of emotional self-efficacy, defined as the perceived ability to regulate and express positive and negative emotions (Bandura, Caprara, Barbaranelli, Gerbino, & Pastorelli, 2003), a construct that has a central role in the process of emotion regulation in early adolescence (Grazzani et al., 2015). Although literature mostly investigated the role of emotional self-efficacy on depressive symptoms in adolescence, studies involving early adolescents are lacking, despite the relevance of this emerging ability in this period of life. Research targeting adolescent samples found that girls generally reported lower emotional self-efficacy than boys, especially in managing negative emotions (Bandura et al., 2003; Grazzani et al., 2015) and lower emotional self-efficacy was related to higher depression among adolescents, especially girls (Bandura et al., 2003). Emotional self-efficacy refers not only to the management of negative emotions but also to the expression of positive ones. Previous work highlighted that the expression of positive emotions is equally important for adolescents’ psychological well-being (Feng et al., 2009; Forbes & Dahl, 2005). Moreover, depression is related not only to negative affect but also to a reduced positive affect and scarce ability to experience enjoyment (Clark & Watson, 1991). Moving from this literature, we investigated if emotional self-efficacy is related to lower depressive symptoms among early adolescents. Moreover, starting from research highlighting that the ability to regulate emotions can moderate the relationship between high empathy and depression (Tully et al., 2016), the present study aimed at exploring if emotional self-efficacy can be a moderating factor in the relationship between high empathy and depressive symptoms.

Peer-Related Loneliness

In addition to empathy and emotional self-efficacy, peer-related loneliness has also been considered a factor related to depressive symptoms in adolescence (Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006; Heinrich & Gullone, 2006). With reference to a multidimensional approach on loneliness (Marcoen & Goossens, 1993), peer-related loneliness has been defined as a stressful situation that results from the absence of social relationships or from a lack of correspondence between desired and actual relationships (Goossens et al., 2009; Lodder, Scholte, Goossens, & Verhagen, 2017). Peer-related loneliness has been found to peak during early adolescence, around 13 years, especially for girls, (Heinrich & Gullone, 2006; Qualter et al., 2013). The ability to form and maintain peer relationships and to gain group acceptance are crucial developmental tasks at this age (Cattelino et al., 2014; Palmonari, 2011), and failures in establishing satisfactory social relationships might lead boys and girls to feelings of exclusion and loneliness.

On one hand, studies on the links between emotional skills and loneliness found that higher ability of understanding and regulating emotions is related to lower loneliness during adolescence (Vanhalst, Luyckx, Van Petegem, & Soenens, 2018; Wols, Scholte, & Qualter, 2015). On the other hand, studies on the relationships between loneliness and depression found that they are interrelated (Heinrich & Gullone, 2006; Ladd & Ettekal, 2013) and influence each other over the course of adolescence, with the direction from loneliness to depressive symptoms stronger than the reversed (Vanhalst et al., 2012). Starting from the evidence that deficits in emotional skills are related to loneliness and loneliness is related to depression, a mediating role of loneliness in the relationship between emotional skills and depression might be hypothesized. In this regard, some studies proved that loneliness is a mediator between difficulties in peer relationships and depression among adolescents (Fontaine et al., 2009; Nangle, Erdley, Newman, Mason, & Carpenter, 2003), but to our knowledge, only one study specifically focused on early adolescence and emotional aspects (Caputi, Pantaleo, & Scaini, 2017). In particular, the authors found a mediating role of loneliness in the relationship between cognitive empathy and the presence of depressive symptoms in a sample of early adolescents. Starting from these results, in the present study, we examined the relationships among empathy, emotional self-efficacy, peer-related loneliness, and depressive symptoms in early adolescence. In particular, we investigated if high empathy and low emotional self-efficacy are related to depressive symptoms with a mediating role of loneliness.

The Present Study

The present study expanded the existing literature on correlates of depressive symptoms among early adolescents, by considering the role of empathy, emotional self-efficacy and loneliness.¹ Data were collected at two time points (time 1 [T1] and time 2 [T2]) with a 1-year interval. Study participants were aged between 12 and 14 years, a period that represents a window of opportunity to investigate correlates of depressive symptoms. In fact, literature outlined that at this age, early adolescents refine their emotional skills, but they are also more vulnerable to increasing depressive symptoms and feelings of isolation from peers (Grazzani et al., 2015; McLaughlin & King, 2015; Qualter et al., 2013; van Lissa et al., 2014).

The aims of the study were the following:

To describe empathy, emotional self-efficacy, loneliness, and depressive symptoms in a group of early adolescents, examining their gender differences. In accordance with literature, girls were expected to report higher empathy (Mestre et al., 2009), lower emotional self-efficacy (Bandura et al., 2003; Grazzani et al., 2015), higher loneliness (Heinrich & Gullone, 2006), and higher depressive symptoms (McLaughlin & King, 2015; Olino et al., 2014; van Oort et al., 2009) than boys.

To examine the role of empathy (T1) on subsequent loneliness (T2) and depressive symptoms (T2). Higher empathy was expected to be related to higher loneliness (Decety & Lamm, 2009; Schreiter et al., 2013) and higher depressive symptoms, especially for girls (Oakley et al., 2012; Schreiter et al., 2013; Smith & Rose, 2011).

To examine the role of emotional self-efficacy (T1) on subsequent loneliness (T2) and depressive symptoms (T2). Lower emotional self-efficacy was expected to be related to higher loneliness (Vanhalst et al., 2018; Wols et al., 2015) and higher depressive symptoms (Bandura et al., 2003). We explored if this relationship was further moderated by gender.

To examine if the strength of the relation between empathy (T1) and subsequent loneliness (T2) and depressive symptoms (T2) was conditioned by the levels of emotional self-efficacy (T1). In particular, higher empathy was expected to be related to higher loneliness and higher depressive symptoms when levels of emotional self-efficacy were lower (Tully et al., 2016). We expected that this relationship would be further moderated by gender; in particular, higher empathy combined with lower emotional self-efficacy would be more strongly associated with higher depressive symptom and loneliness for girls than for boys (Smith & Rose, 2011).

To examine the role of loneliness as a mediator of the relation between empathy (T1) and emotional self-efficacy (T1) and subsequent depressive symptoms (T2). Starting from studies on the ability of regulating emotions and loneliness (Vanhalst et al., 2018; Wols et al., 2015), as well as on loneliness and depression (Heinrich & Gullone, 2006), and on the mediating role of loneliness between empathy and depressive symptoms (Caputi et al., 2017), we expected that higher empathy and lower emotional self-efficacy would be associated to higher loneliness, which, in turn, would be related to depressive symptoms. Finally, we explored if this relationship was further moderated by gender.

Method

Participants

The first wave of data collection (T1) involved 403 Italian early adolescents aged between 12 and 14 years attending the second year of middle school.² Students came from middle schools located in urban centers in the northwest of Italy. The second wave of data collection (T2) took place 1 year apart, when students were attending the third year of middle school. A total of 348 students were present at both waves (N = 167, 48% girls, aged between 12 and 14 years, mean age = 13, SD = 0.3). The attrition rate was 14%. Only participants who completed the two assessments were included in the present study; they did not differ from the overall sample on demographic or study variables. The majority of participants (85%) lived with both parents and had brothers or sisters (85%). Parents’ level of education was medium high (high school diploma for 36.2% of mothers and 32.8% of fathers; degree for 25.7% of mothers and 17.8% of fathers). The majority of parents were employed full time (55.7% of mothers and 82.7% of fathers).³

Procedure

A convenience sample of seven middle schools was selected to participate in the study. The research project was presented to each school, and a total of 26 classes were enrolled. The study was approved by the Bioethics Committee of the University of Turin (Italy), and written informed consent was obtained from the parents of the participants before the questionnaire was administered. Parental consent was given for 96% of the students originally contacted to participate in the study. Participants completed an anonymous self-report questionnaire, administered by trained researchers in the schools during classroom time, without teachers present. Completed questionnaires were turned in immediately to the researchers. Students were requested to write a self-generated code to combine questionnaires of the two waves. Participants did not receive benefits for participating in the study.

Measures

Empathy

Students were asked to complete the scale How I feel in different situations (HIFDS, Feschbach et al., 1991, Italian validation Bonino, Lo Coco, & Tani, 1998). It is composed of 12 items investigating cognitive empathy (6 items; for example, “I can sense how my friends feel from the way they behave”) and affective empathy (6 items; for example, “When somebody tells me a nice story, I feel as if the story is happening to me”) on a 4-point Likert-type scale from 0 (never true) to 3 (always true). The total score ranges from 0 to 36 (Cronbach’s α = .84).

Emotional self-efficacy

Students completed the Multidimensional Negative Regulatory Emotional Self-Efficacy Scale (Caprara, Di Giunta, Pastorelli, & Eisenberg, 2013) and the Positive Regulatory Emotional Self-Efficacy Scale (Caprara et al., 2008). The first scale is composed of 15 items which evaluate the perceived ability to regulate negative affect (anger, sadness, fear, shame, and guilt) on a 5-point Likert-type scale ranging from 1 (not able at all) to 5 (very able). The second scale is composed of 4 items and evaluates the perceived ability to express positive affect on the same Likert-type scale. The total score of the two scales (19 items) ranges from 19 to 95 (Cronbach’s α = .81).

Loneliness

Students completed the peer-related loneliness subscale of the Loneliness and Aloneness Scale for Children and Adolescents (LACA; Marcoen, Goossens, & Caes, 1987, Italian validation Melotti, Corsano, Majorano, & Scarpuzzi, 2006). The subscale includes 12 items which investigate perceived loneliness in peer relationships (e.g., “I think I have fewer friends than others”). Items were answered on a 4-point Likert-type scale ranging from 1 (never) to 4 (often) with higher scores indicating higher levels of perceived loneliness (range, 12–48; Cronbach’s α = .92).

Depressive symptoms

Students completed the Italian validation of the Center for Epidemiological Studies Scale—short version 10 items (CESD-10; Pierfederici et al., 1982). The scale evaluates the frequency of depressive symptoms during the past week on a 4-point Likert-type scale from 0 (rarely or none of the time) to 3 (most or all of the time; range, 0–30; Cronbach’s α = .70).

Statistical Analysis

A preliminary check on missing data indicated that the percentage of missing response for the study scales was less than 10%. The Missing Completely at Random (MCAR) test (Little, 1988) showed nonsignificant results for all the study variables; thus, missing were imputed in SPSS using the Expectation-Maximization (EM) procedure. Descriptive analyses included t tests for gender differences in study variables and Pearson’s bivariate correlations.

Structural equation modeling (SEM) with Mplus 7.3 (Muthén & Muthén, 1998–2017) was used to examine our hypotheses regarding the relation between the study variables. As suggested by Cole and Maxwell (2003), prior to conducting structural analyses, we examined the measurement model to check the factorial structure of each scale. After confirming the factorial structure, we specified a measurement model including both T1 and T2 measures, and we checked measurement invariance between the two time points.

Measurement models and measurement invariance testing

For each of the four scales, a confirmatory factor analysis was conducted on scores at T1 to assess the goodness of fit of the theoretical factor structure. Specifically, it was estimated a one-factor model for both the loneliness and the depressive symptoms scales; a two-factor model for the empathy scale, distinguishing between the cognitive and affective dimensions; and a six-factor model for the emotional self-efficacy scale, distinguishing between the perceived ability to regulate anger, sadness, fear, shame, guilt, and positive affect. Goodness of fit was evaluated according to the following criteria: root mean square error of approximation (RMSEA) < .08; comparative fit index (CFI) > .95, and standardised root mean square residual (SRMR) <.08 (Browne & Cudeck, 1993; Hu & Bentler, 1999).

After confirming the factorial structure of the separate scales scores, measurement models including all the scale scores at T1 and T2 were estimated to assess measurement invariance (configural, metric, and scalar invariance) across the two time points. Multiple indicator latent variables were created using the parcels of items from the relevant scales as a strategy aimed at reducing the number of estimated parameters and improving the normality of the variables’ distributions (Bandalos & Finney, 2001; Landis, Beal, & Tesluk, 2000). In particular, for empathy and self-efficacy, items belonging to the same subscales were aggregated forming two and six parcels, respectively; depressive symptoms were measured with three parcels, each formed by 3 or 4 items, and loneliness had four parcels made of 3 items each. A worsening greater or equal to .010 and .015 for ΔCFI and ΔRMSEA, respectively (Chen, 2007), when moving from configural to metric invariance model and from metric to scalar invariance model was considered as a lack of invariance.

For both confirmatory factor analyses and invariance testing, the estimation method was MLMV.

Structural model

Gender and the latent variables with parceled indicators used in the invariance model were the set of variables employed in the structural longitudinal model. In particular, the four latent variables at T2 were the outcome measures (SE T2; EMP T2; LONE T2; and DEPR T2), and the predictors that were common to all the four outcome variables were the corresponding latent variable at T1 (SE T1; EMP T1; LONE T1; and DEPR T1) and gender (GEND). With regard to depressive symptoms (DEPR T2) and loneliness (LONE T2), the following additional regressors were included: emotional self-efficacy (SE T1), empathy (EMP T1), and their interaction (SE T1 × EMP T1); the last term is to assess the conditional role of emotional self-efficacy on the relation between empathy and depressive symptoms. Moreover, to investigate whether the previous associations were different between boys and girls (i.e., the above relations were moderated by gender), all the interactions with gender were specified (GEND × SE T1; GEND × EMP T1; GEND × SE T1 × EMP T1). The mediation role of loneliness on the relation between the two predictors (emotional self-efficacy and empathy) and depressive symptoms was examined following the approach suggested by Cole and Maxwell (2003) when only two measurement points are available. After estimating path a (mediator at T2 regressed on predictors at T1, controlling for mediator at T1) and path b (outcome at T2 regressed on the mediator at T1, controlling for the outcome at T1), the products ab provide an estimate of the indirect pathway between the predictors and the outcome. Thus, for estimating the mediation of loneliness and the possible moderation role of gender on this mediation (moderated mediation model; Hayes, 2017), the following additional regressors were introduced in the equation for DEPR T2: loneliness at T1 (LONE T1) and its interaction with gender (GEND × LONE T1).

A refined structural model was then estimated by omitting statistically nonsignificant paths not involved in higher order interaction terms that were statistically significant.

Maximum likelihood estimation using a numerical integration algorithm was used, and model fit was evaluated by means of likelihood ratio test (LR test) and Akaike information criterion (AIC) and Bayesian information criterion (BIC) because the ordinary statistics, such as RMSEA, CFI, and χ², are no longer available in nonlinear SEM (Maslowsky, Jager, & Hemken, 2015). Moreover, boostrap-based confidential intervals (1,000 draws) were used for the indirect pathways and the simple slope analysis. Finally, standardized regression coefficients and R² were calculated according to Muthén (2012).

Results

Descriptive Statistics

Means and standard deviations of study variables at both waves are reported in Table 1. Concerning gender differences, at both waves, depressive symptoms were higher for girls than for boys. Statistically significant gender differences also emerged for the other variables at both waves; in particular, girls reported lower levels of emotional self-efficacy, higher levels of empathy, and higher levels of loneliness than boys.

Table 1.

Descriptive Statistics of the Study Variables in the Total Group of Participants and by Gender in the Two Waves (T1 and T2; N = 348).

	Total (N = 348)		Girls (N = 167)		Boys (N = 181)		t	df	p
Study Variables	M	ds	M	ds	M	ds	t	df	p
Emotional self-efficacy T1	62.7	10.2	60.2	10.0	65.0	9.9	−4.53	346	.000
Empathy T1	17.3	6.5	18.7	6.2	15.9	6.4	4.11	346	.000
Loneliness T1	18.7	7.6	19.6	8.1	17.9	7.0	2.07	346	.039
Depressive symptoms T1	7.2	4.1	8.1	4.4	6.3	3.7	4.19	346	.000
Emotional self-efficacy T2	63.0	11.7	59.3	11.4	66.4	10.9	−5.96	346	.000
Empathy T2	17.3	6.4	18.4	6.3	16.3	6.3	3.04	346	.003
Loneliness T2	19.0	7.0	20.3	7.4	17.8	6.4	3.45	346	.001
Depressive symptoms T2	8.1	4.9	9.5	5.7	6.8	3.8	5.15	346	.000

Results of correlation analysis are reported in Table 2. Synchronic correlations, which were both statistically significant and not marginal as effect size (≥.20), have shown that higher depressive symptoms were related to lower emotional self-efficacy and higher loneliness and that higher loneliness was related to lower emotional self-efficacy. As regards the diachronic correlations, all the four variables at T2 have shown high positive relations with their counterpart at T1, higher empathy at T1 was related to higher loneliness at T2 and higher depressive symptoms at T2, whereas higher emotional self-efficacy at T1 was related to lower loneliness and lower depressive symptoms at T2.

Table 2.

Bivariate Correlations Between the Study Variables.

Study Variables	1	2	3	4	5	6	7	8
1. Empathy T1	—
2. Emotional self-efficacy T1	−.04	—
3. Loneliness T1	.19**	−.27**	—
4. Depressive symptoms T1	.09	−.42**	.44**	—
5. Empathy T2	.56**	.02	.05	.03	—
6. Emotional self-efficacy T2	−.11*	.51**	−.21**	−.35**	−.01	—
7. Loneliness T2	.22**	−.23**	.54**	.31**	.10	−.39**	—
8. Depressive symptoms T2	.20**	−.31**	.24**	.46**	.12*	−.47**	.44**	—

p < .05. **p < .01.

Measurement Models and Measurement Invariance Testing

All fit indices indicated the measurement models of empathy (two-factor model at T1, RMSEA = .061, 90% confidence interval [CI] = [.047, .075]; CFI = .922; SRMR = .052), emotional self-efficacy (six-factor model at T1, RMSEA = .034, 90% CI = [.021, .045]; CFI = .938; SRMR = .048), and loneliness T1 (one-factor model at T1, RMSEA = .065, 90% CI = [.051, .079]; CFI = .941; SRMR = .035) fit the data well. The measurement model of depressive symptoms obtained excellent fit indices (one-factor model at T1, RMSEA = .044, 90% CI = [.019, .066]; CFI = .946; SRMR = .042) after removing 1 item (“I felt hopeful about the future”). However, for conservative purpose, the poor fitted item was not excluded from the analyses. The longitudinal measurement model with parceled items showed metric and scalar invariance for all measures (ΔCFI = −.002; ΔRMSEA < .001, imposing loadings invariance and ΔCFI = −.006; ΔRMSEA = .001, when constraining both loadings and intercepts to be equal across the two waves). Moreover, the goodness of fit of the model with loadings and intercepts constrains was satisfactory (RMSEA = .040, 90% CI = [.034, .046]; CFI = .922; SRMR = .069). The standardized estimates of this last model are reported in Appendix.

Structural Model

According to Maslowsky et al. (2015), the fit of the hypothesized model was evaluated by performing an LR test with respect to a more parsimonious model in which all the interaction terms were removed. The more parsimonious model fitted the data well (RMSEA = .056; 90% CI = [.050, .061]; CFI = .908; SRMR = .078), and the LR test was statistically significant, LR(5) = 36.21, p < .001, meaning that the more parsimonious model produced a significant loss in fit relative to the hypothesized model. This result was coherent with those provided by the information criteria (AIC = 17,972.42, BIC = 18,407.06 for the hypothesize model; AIC = 17,990.17, BIC = 18,486.36 for the more parsimonious model), indicating that the hypothesized model was a well-fitted model. In the final model, in which only the statistically significant paths from the hypothesized model were estimated, AIC and BIC values were 17,965.94 and 18,369.82, respectively, and high values of R² were obtained (Figure 1). For T2 empathy, the unique significant predictor was T1 empathy, whereas for T2 emotional self-efficacy, significant predictors were T1 emotional self-efficacy and gender. Regarding T2 loneliness, significant predictors were T1 loneliness, T1 empathy, and T1 emotional self-efficacy. The results indicated that higher loneliness was related to higher empathy and lower emotional self-efficacy. Also, the interaction T1 emotional self-efficacy × T1 empathy was statistically significant, and it will be discussed in the section regarding moderation analysis. For T2 depressive symptoms, significant predictors were T1 depressive symptoms, T1 empathy, and T1 emotional self-efficacy. Higher depressive symptoms were related to higher empathy and lower emotional self-efficacy. Also, the interactions T1 emotional self-efficacy × T1 empathy, gender × T1 empathy, and gender × T1 emotional self-efficacy × T1 empathy were statistically significant, and they will be discussed further.

Figure 1.

Standardized solution for the final model.

Moderation Analysis

Four interactions resulted statistically significant. First, T2 loneliness was related to T1 empathy × T1 emotional self-efficacy. The increase in the explained variance after entering the interactions was relevant (3.7%). The results indicated that the relationship between empathy and loneliness was moderated by emotional self-efficacy; in particular, the slope analysis indicated that higher empathy was associated with higher loneliness in the presence of low levels of emotional self-efficacy (b =.35; p =.003; 95% CI = [.07, 1.10]; Figure 2).

Figure 2.

Moderating effect of emotional self-efficacy on the relationship between empathy and loneliness.

Other three interactions emerged as significant predictors of depressive symptoms (T1 emotional self-efficacy × T1 empathy, gender × T1 empathy, and gender × T1 emotional self-efficacy × T1 empathy). The increase in the explained variance after entering the two-way interactions was 14.4%, and an additional 8.2% was explained by the three-way interaction. As observed for loneliness, the negative relationship between empathy and depressive symptoms was moderated by emotional self-efficacy; in particular, the slope analysis indicated that higher empathy was associated with higher depressive symptoms in the presence of low levels of emotional self-efficacy (b = 0.59, p < .001, 95% CI = [.10, 1.17]; Figure 3). Moreover, the relationship between empathy and depressive symptoms was moderated by gender, with high empathy related to higher depressive symptoms for girls (b = 0.49, p < .001), but this path was not statistically significant in terms of the boostrap-based confidential interval (95% CI = [−.50, 1.16]), probably in reason of collinearity. Finally, the three-way interaction indicated that gender moderates the conditional role of emotional self-efficacy on the relationship between empathy and depressive symptoms. The simple slope analysis revealed that only the slope for girls with low emotional self-efficacy was statistically significant (b = 0.97; p < .001; 95% CI = [.01, 1.75]; Figure 4).

Figure 3.

Moderating effect of emotional self-efficacy on the relationship between empathy and depressive symptoms.

Figure 4.

Moderating effect of emotional self-efficacy and gender on the relationship between empathy and depressive symptoms.

Mediation Analysis

No mediation was found because the pathway from T1 loneliness to T2 depressive symptoms (path b) resulted statistically nonsignificant.

Discussion

The study was aimed at investigating the role of empathy and emotional self-efficacy (additive and interactive) on subsequent loneliness and depressive symptoms, taking into account gender differences, and the possible mediating role of loneliness in the relationship between emotional predictors and depressive symptoms. Girls reported higher depressive symptoms, loneliness, and empathy and lower emotional self-efficacy than boys. High depressive symptoms and high loneliness at T2 were related to high empathy and low emotional self-efficacy at T1. The relationships between empathy and depressive symptoms, as well as between empathy and loneliness, were moderated by emotional self-efficacy. High empathy was related to high depressive symptoms when levels of emotional self-efficacy were low, only among girls. No mediation role of loneliness was found.

Concerning descriptive results, our study indicated that in a community sample of early adolescents, depressive symptoms were largely reported. The result is in line with reports indicating that the prevalence rates of mental disorders, including depression, are growing among early adolescents and adolescents (World Health Organization [WHO], 2013). Gender differences were in line with the literature, suggesting a higher vulnerability of girls with respect to depression (McLaughlin & King, 2015; Olino et al., 2014; van Oort et al., 2009) and feelings of loneliness (Heinrich & Gullone, 2006). Moreover, girls perceived themselves as more empathic than boys, but less competent in managing emotions, as also outlined in other studies (Bandura et al., 2003; Grazzani et al., 2015; Mestre et al., 2009). These gender differences could be traced to cultural models and educational practices, which encourage girls to be sensitive to others’ emotional difficulties. Moreover, girls generally give more importance to intimacy and sharing emotions than boys in peer relationships (Rubin, Bukowski, & Parker, 2006).

This study adds to empathy literature by examining the relationship between empathy, loneliness, and depressive symptoms. Our findings converge to suggest the critical role of high levels of empathy that can be a risk factor for early adolescents’ adjustment. Instead, emotional self-efficacy emerged as a protective factor with respect to depressive symptoms and loneliness, as hypothesized in this study and suggested in previous research (Bandura et al., 2003; Durbin & Shafir, 2008; Tully et al., 2016; Vanhalst et al., 2018). Moreover, in our longitudinal analysis, emotional self-efficacy interacted with empathy to predict loneliness and depressive symptoms 1 year later: high empathy, in combination with low levels of emotional self-efficacy, was linked to high depressive symptoms and loneliness. As expected, our results suggest that girls with high empathy are more vulnerable to depressive symptoms when they have lower abilities to regulate their own empathic involvement (Smith & Rose, 2011).

Empathy can, therefore, be considered an adaptive individual characteristic when combined with a good ability to regulate emotions; in this case, empathy is related with the greatest social benefits because it allows to understand others’ emotions and to get affectively involved without becoming overwhelmed (Tully et al., 2016).

The moderating role of emotional self-efficacy is particularly relevant for girls, and this result has important implications for prevention that will be discussed later.

The hypothesis that higher empathy and lower emotional self-efficacy contributes to higher loneliness, which, in turn, increases the risk of depressive symptoms was rejected. In our model, the mediation role of loneliness did not emerge, suggesting that loneliness and depressive symptoms are both outcomes of empathy and emotional self-efficacy. Previous research has suggested that loneliness and depressive symptoms are distinct constructs (Cacioppo et al., 2006; Spithoven et al., 2017), and they can, therefore, be considered as two indicators of maladjustment in early adolescence. Both loneliness and depressive symptoms are likely to be outcomes of empathy and emotional self-efficacy because they are characterized by common psychological variables, such as negative cognitive biases (Everaert, Koster, & Derakshan, 2012; Lodder et al., 2017), as well as poor social skills (Hames, Hagan, & Joiner, 2013; Segrin & Flora, 2000). Another possibility is that loneliness and depression share a common genetic influence, as suggested in the study of Matthews et al. (2016). On the other hand, it should be noted that loneliness and depressive symptoms are distinct, but partly overlapping constructs. Loneliness is often considered to be a specific form of emotional distress, whereas depressive symptoms are considered to be a more general form of emotional distress (Cheng & Furnham, 2002). Loneliness could be considered as one of the indicators of depressive symptoms, especially among early adolescents, in relation to the centrality of peer relationships for the psychological well-being at this age. Overall, further research is needed to explore the relationships between loneliness and depressive symptoms at this age. In particular, other variables should be considered, such as the quality of friendships (Nangle et al., 2003; Witvliet, Brendgen, van Lier, Koot, & Vitaro, 2010).

This study had some limitations. First, the sample is not representative, and this limits the generalizability of the results. Involving a larger and representative sample would allow to confirm the results of the present study. In particular, in light of sociocultural differences between Northern and Southern Italy (Bacchini, Miranda, & Affuso, 2011; Di Giunta & Iselin, 2014), data should be gathered in other Italian regions to have a more representative picture of the national context. Second, because early adolescence is a period of huge change and multiple developmental trajectories are possible, further research should include other waves of data collection to investigate relationships among the examined variables over time. In particular, a third measurement point would allow to deepen the study of the mediating role of loneliness in the relationship between emotional predictors and depressive symptoms. The relation between loneliness and subsequent depressive symptoms could not be stationary across time in early adolescence, and a third wave could help to disentangle the issue.

Despite these limitations, the study sheds more light on the complexity of relationships between empathy, emotional self-efficacy, loneliness, and depressive symptoms in early adolescence. In particular, emotional self-efficacy should be considered as a core protective factor for depressive symptoms at this age. These results have relevant implications for prevention and intervention in early adolescence and are consistent with the Collaborative for Academic, Social and Emotional Learning (CASEL) approach (Zins & Elias, 2006). Precocious interventions are crucial to prevent risk trajectories, especially for girls. Emotional self-efficacy should be promoted among early adolescents, teaching them adaptive strategies to be aware of their emotions and to effectively manage them. A final consideration concerns the interventions aimed at improving empathy in early adolescence. In fact, these interventions could expose early adolescents, especially girls, to a greater risk of depression, if not associated to activities aimed at promoting emotional self-efficacy skills.

Supplemental Material

Appendix – Supplemental material for Depressive Symptoms and Loneliness in Early Adolescence: The Role of Empathy and Emotional Self-Efficacy

Supplemental material, Appendix for Depressive Symptoms and Loneliness in Early Adolescence: The Role of Empathy and Emotional Self-Efficacy by Emanuela Calandri, Federica Graziano, Elena Cattelino and Silvia Testa in The Journal of Early Adolescence

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Emanuela Calandri

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Emanuela Calandri is an assistant Professor in developmental psychology at the University of Turin (Italy); she received her PhD in social and developmental psychology from the University of Turin. Her main research interests concern adolescent development, in particular, risk behavior in adolescence, and psychological adjustment to chronic disease (multiple sclerosis) in young adults.

Federica Graziano is a psychologist; she received her PhD in social and developmental psychology from the University of Turin (Italy). Her research interests include adolescent risk behavior, protective factors and health promotion in the school context, and psychological adjustment to chronic disease, in particular, multiple sclerosis, among adult patients.

Elena Cattelino is a full professor in developmental psychology at the University of Aosta Valley. Her scientific training was carried out at the universities of Turin (Italy) and Liège (Belgium). Her research interests include the relationship between cognitive abilities and social development, adolescent development, and risk prevention and well-being promotion during the life span.

Silvia Testa is an assistant professor in psychometrics at the University of Turin (Italy). Her main research interests are in the fields of quantitative research methods, development and adaptation of psychological measurement instruments, emotion psychology, and quality of life in both general and clinical populations.

References

Abela

J. R. Z.

Hankin

B. L.

(Eds.). (2008). Handbook of depression in children and adolescents. New York, NY: Guliford Press.

Allemand

Steiger

A. E.

Fend

H. A.

(2015). Empathy development in adolescence predicts social competencies in adulthood. Journal of Personality, 83(2), 229–241. doi:10.1111/jopy.12098

Avenevoli

Knight

Kessler

R. C.

Merikangas

K. R.

(2008). Epidemiology of depression in children and adolescents. In Abela

J. R. Z.

Hankin

B. L.

(Eds.), Handbook of depression in children and adolescents (pp. 6–32). New York, NY: Guilford Press.

Bacchini

Miranda

M. C.

Affuso

(2011). Effects of parental monitoring and exposure to community violence on antisocial behavior and anxiety/depression among adolescents. Journal of Interpersonal Violence, 26(2), 269–292. doi:10.1177/0886260510362879

Bandalos

D. L.

Finney

S. J.

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

G. A.

Schumacker

R. E.

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

Bandura

Caprara

G. V.

Barbaranelli

Gerbino

Pastorelli

(2003). Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning. Child Development, 74(3), 769–782. doi:10.1111/1467-8624.00567

Batanova

M. D.

Loukas

(2012). What are the unique and interacting contributions of school and family factors to early adolescents’ empathic concern and perspective taking? Journal of Youth and Adolescence, 41(10), 1382–1391. doi:10.1007/s10964-012-9768-5

Bonino

Lo Coco

Tani

(1998). Empatia [Empathy]. Firenze: Giunti.

Branje

S. J. T.

Hale

W. W. I.

Frijns

Meeus

W. H. J.

(2010). Longitudinal associations between perceived parent-child relationship quality and depressive symptoms in adolescence. Journal of Abnormal Child Psychology, 38, 751–763. doi:10.1007/s10802-010-9401-6

10.

Browne

M. W.

Cudeck

(1993). Alternative ways of assessing model fit. In Bollen

K. A.

Long

J. S.

(Eds.), Testing structural equation models (pp. 132–162). London, England: SAGE. Hu and Bentler, 1995.

11.

Cacioppo

J. T.

Hughes

M. E.

Waite

L. J.

Hawkley

L. C.

Thisted

R. A.

(2006). Loneliness as a specific risk factor for depressive symptoms: Cross-sectional and longitudinal analyses. Psychology and Aging, 21(1), 140–151.

12.

Calandri

Graziano

Testa

Cattelino

Begotti

(2019). Empathy and depression among early adolescents: the moderating role of parental support”, Frontiers in Psychology, doi:10.3389/fpsyg.2019.0144

13.

Caprara

G. V.

Di Giunta

Eisenberg

Gerbino

Pastorelli

Tramontano

(2008). Assessing regulatory emotional self efficacy in three countries. Psychological Assessment, 20, 227–237. doi:10.1037/1040-3590.20.3.227

14.

Caprara

G. V.

Di Giunta

Pastorelli

Eisenberg

(2013). Mastery of negative affect: A hierarchical model of emotional self-efficacy beliefs. Psychological Assessment, 25(1), 105–116. doi:10.1037/a0029136

15.

Caputi

Pantaleo

Scaini

(2017). Do feelings of loneliness mediate the relationship between sociocognitive understanding and depressive symptoms during late childhood and early adolescence? Journal of Genetic Psychology, 178(4), 207–216. doi:10.1080/00221325.2017.1317629

16.

Cattelino

Glowacz

Born

Testa

Bina

Calandri

(2014). Adolescent Risk Behaviours and Protective Factors Against Peer Influence. Journal of Adolescence, vol. 8(37): 1353–1362. doi:10.1016/j.adolescence.2014.09.013

17.

Chen

F. F.

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464–504.

18.

Cheng

Furnham

(2002). Personality, peer relations, and self-confidence as predictors of happiness and loneliness. Journal of Adolescence, 25, 327–339. doi:10.1006/jado.2002.0475

19.

Chow

C. M.

Ruhl

Buhrmester

(2013). The mediating role of interpersonal competence between adolescents’ empathy and friendship quality: A dyadic approach. Journal of Adolescence, 36(1), 191–200. doi:10.1016/j.adolescence.2012.10.004

20.

Clark

L. A.

Watson

(1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100(3), 316–336. doi:10.1037//0021-843x.100.3.316

21.

Cole

D. A.

Maxwell

S. E.

(2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577. doi:10.1037/0021-843X.112.4.558

22.

Decety

Lamm

(2009). Empathy versus personal distress—Recent evidence from social neuroscience. In Decety

Ickes

(Eds.), The social neuroscience of empathy (pp. 199–213). Cambridge, MA: MIT Press.

23.

Di Giunta

Iselin

A. M. R

. (2014). Adolescent depressive symptoms: An integrative examination of parenting and sadness regulation in Italy and the United States. ISSBD Bulletin, 1(65), 13–16.

24.

Domes

Spenthof

Radtke

Isaksson

Normann

Heinrichs

(2016). Autistic traits and empathy in chronic vs. episodic depression. Journal of Affective Disorders, 195, 144–147. doi:10.1016/j.jad.2016.02.006

25.

Durbin

E. C.

Shafir

D. M.

(2008). Emotion regulation and risk for depression. In Abela

J. R. Z.

Hankin

B. L.

(Eds.), Handbook of depression in children and adolescents (pp. 149–176). New York, NY: Guildford Press.

26.

Eisenberg

Spinrad

T. L.

Morris

A. S.

(2013). Prosocial development. In Zelazo

P. D.

(Ed.), The Oxford handbook of developmental psychology (300–325). Oxford, UK: Oxford University Press.

27.

Euler

Steinlin

Stadler

(2017). Distinct profiles of reactive and proactive aggression in adolescents: Associations with cognitive and affective empathy. Child and Adolescent Psychiatry and Mental Health, 11(1), 1–14. doi:10.1186/s13034-016-0141-4

28.

Everaert

Koster

E. H.

Derakshan

(2012). The combined cognitive bias hypothesis in depression. Clinical Psychology Review, 32, 413–424. doi:10.1016/j.cpr.2012.04.003

29.

Feng

Keenan

Hipwell

A. E.

Henneberger

A. K.

Rischall

M. S.

Butch

. . . Babinski

D. E.

(2009). Longitudinal associations between emotion regulation and depression in preadolescent girls: Moderation by the caregiving environment. Developmental Psychology, 45(3), 798–808. doi:10.1037/a0014617

30.

Feshbach

N. D.

(1997). Empathy: The formative years. Implications for clinical practice. In Bohart

A. C.

Greenberg

L. S.

(Eds.), Empathy reconsidered: New directions in psychotherapy (pp. 33–59). Washington, DC: American Psychological Association.

31.

Feschbach

N. D.

Caprara

G. V.

Lo Coco

Pastorelli

Manna

Menzes

(1991, July). Empathy and its correlates: Cross cultural data from Italy. Eleventh biennial meeting of the International Society for the Study of Behavioural Development, Minneapolis, MN.

32.

Fontaine

K. G.

Yang

Salzer Burks

Dodge

K. A.

Price

J. M.

Pettit

G. S.

Bates

J. E.

(2009). Loneliness as a partial mediator of the relation between low social preference in childhood and anxious/depressed symptoms in adolescence. Development and Psychopathology, 21, 79–491. doi:10.1017/s0954579409000261

33.

Forbes

E. E.

Dahl

R. E.

(2005). Neural systems of positive affect: Relevance to understanding child and adolescent depression? Development and Psychopathology, 17, 827–850. doi:10.1017/s095457940505039x

34.

Goossens

Lasgaard

Luyckx

Vanhalst

Mathias

Masy

(2009). Loneliness and solitude in adolescence: A confirmatory factor analysis of alternative models. Personality and Individual Differences, 47(8), 890–894. doi:10.1016/jpaid200907011.

35.

Grazzani

Corti

Ornaghi

Antoniotti

Pepe

(2015). Regolazione delle emozioni, autoefficacia emotiva ed empatia: una ricerca in preadolescenza [Emotion regulation, emotional self-efficacy and empathy: A research in preadolescence]. Psicologia clinica dello sviluppo, 19(3), 429–447.

36.

Hames

J. L.

Hagan

C. R.

Joiner

T. E.

(2013). Interpersonal processes in depression. Annual Review of Clinical Psychology, 9, 355–377. doi:10.1146/annurev-clinpsy-050212-185553

37.

Hamilton

J. L.

Hamlat

E. J.

Stange

J. P.

Abramson

L. Y.

Alloy

L. B.

(2014). Pubertal timing and vulnerabilities to depression in early adolescence: Differential pathways to depressive symptoms by sex. Journal of Adolescence, 37(2), 165–174.

38.

Hankin

B. L.

(2015). Depression from childhood through adolescence: Risk mechanisms across multiple systems and levels of analysis. Current Opinion in Psychology, 4, 13–20. doi:10.1016/j.copsyc.2015.01.003

39.

Hankin

B. L.

Mermelstein

Roesch

(2007). Sex differences in adolescent depression: Stress exposure and reactivity models. Child Development, 78, 279–295. doi:10.1111/j.1467-8624.2007.00997.x

40.

Hayes

A. F.

(2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.

41.

Heinrich

L. M.

Gullone

(2006). The clinical significance of loneliness: A literature review. Clinical Psychology Review, 26(6), 695–718. doi:10.1016/j.cpr.2006.04.002

42.

Hoffman

M. L.

(2008). Empathy and prosocial behavior. In Lewis

Haviland-Jones

J. M.

Barrett

L. F.

(Eds.), Handbook of emotions (pp. 440–455). New York, NY: Guilford Press.

43.

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(3), 401–415. doi:10.1037/0022-006X.74.3.401

44.

Bentler

P. M.

(1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 26, 1–55.

45.

Ladd

G. W.

Ettekal

(2013). Peer-related loneliness across early to late adolescence: Normative trends, intra-individual trajectories, and links with depressive symptoms. Journal of Adolescence, 36(6), 1269–1282. doi:10.1016/j.adolescence.2013.05.004

46.

Landis

R. S.

Beal

D. J.

Tesluk

P. E.

(2000). A comparison of approaches to forming composite measures in structural equation models. Organizational Research Methods, 3, 186–207.

47.

Little

R. J. A.

(1988). A test of missing completely at random for multivariate data with missing values. Journal of American Statistical Association, 83(404), 1198–1202. doi:10.1080/01621459.1988.10478722

48.

Lodder

G. M. A.

Scholte

R. H. J.

Goossens

Verhagen

(2017). Loneliness in early adolescence: Friendship quantity, friendship quality, and dyadic processes. Journal of Clinical Child & Adolescent Psychology, 46(5), 709–720. doi:10.1080/15374416.2015.1070352

49.

Marcoen

Goossens

(1993). Loneliness, attitude towards aloneness, and solitude: Age differences and developmental significance during adolescence. In Jackson

Rodriguez-Tomé

(Eds.), Adolescence and its social worlds. Hove, UK: Lawrence Erlbaum.

50.

Marcoen

Goossens

Caes

(1987). Loneliness in pre-through late adolescence: Exploring the contributions of a multidimensional approach. Journal of Youth & Adolescence, 16(6), 561–577. doi:10.1007/bf02138821

51.

Maslowsky

Jager

Hemken

(2015). Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method. International Journal of Behavioral Development, 39(1), 87–96. doi:10.1177/0165025414552301

52.

Matthews

Danese

Wertz

Odgers

C. L.

Ambler

Moffitt

T. E.

Arseneault

(2016). Social isolation, loneliness and depression in young adulthood: A behavioural genetic analysis. Social Psychiatry and Psychiatric Epidemiology, 51(3), 339–348.

53.

McLaughlin

K. A.

King

(2015). Developmental trajectories of anxiety and depression in early adolescence. Journal of Abnormal Child Psychology, 43(2), 311–323. doi:10.1007/s10802-014-9898-1

54.

Melotti

Corsano

Majorano

Scarpuzzi

(2006). An Italian application of the Louvain Loneliness Scale for Children and Adolescents (LLCA). Testing, Psychometrics, Methodology in Applied Psychology, 13(3), 237–255.

55.

Mestre

M. V.

Samper

Frias

M. D.

Tur

A. M.

(2009). Are women more empathetic than men? A longitudinal study in adolescence. The Spanish Journal of Psychology, 12, 76–83. doi:10.1017/s1138741600001499

56.

Muthén

(2012). Latent variable interactions, document. Retrieved from https://www.statmodel.com

57.

Muthén

L. K.

Muthén

B. O.

(1998–2017). Mplus user’s guide (8th ed.). Los Angeles, CA: Author.

58.

Nangle

D. W.

Erdley

C. A.

Newman

J. E.

Mason

C. A.

Carpenter

E. M.

(2003). Popularity, friendship quantity, and friendship quality: Interactive influences on children’s loneliness and depression. Journal of Clinical Child and Adolescent Psychology, 32, 546–555. doi:10.1207/s15374424jccp3204_7

59.

National Institute of Statistics. (2019). Annuario statistico italiano [Italian Statistical Yearbook]. Roma: Author.

60.

Nock

M. K.

Green

J. G.

Hwang

McLaughlin

K. A.

Sampson

N. A.

Zaslavsky

A. M.

Kessler

R. C.

(2013). Prevalence, correlates, and treatment of lifetime suicidal behavior among adolescents: Results from the National Comorbidity Survey Replication Adolescent Supplement. JAMA Psychiatry, 70(3), 300–310. doi:10.1001/2013.jamapsychiatry.55

61.

Oakley

Knafo

McGrath

(2012). Pathological altruism—An introduction. In Oakley

Knafo

Madhavan

Wilson

D. S.

(Eds.), Pathological altruism (pp. 3–9). New York, NY: Oxford University Press.

62.

Olino

T. M.

Stepp

S. D.

Keenan

Loeber

Hipwell

(2014). Trajectories of depression and anxiety symptoms in adolescent girls: A comparison of parallel trajectory approaches. Journal of Personality Assessment, 96(3), 316–326. doi:10.1080/00223891.2013.866570

63.

Palmonari

(2011). Psicologia dell’adolescenza [Psychology of adolescence]. Bologna, Italy: il Mulino.

64.

Pierfederici

Fava

G. A.

Munari

Rossi

Badaro

Pasquali Evangelisti

. . . Zecchino

(1982). Validazione italiana del CES-D per la misurazione della depressione [Italian validation of CES-D for depression measurement]. In Canestrari

(Ed.), Nuovi Metodi in Psicometria [New methods in psychometrics] (pp. 95–103). Firenze, Italy: Organizzazioni Speciali.

65.

Qualter

Brown

S. L.

Rotenberg

K. J.

Vanhalst

Harris

R. A.

Goossens

Munn

(2013). Trajectories of loneliness during childhood and adolescence: Predictors and health outcomes. Journal of Adolescence, 36(6), 1283–1293. doi:10.1016/j.adolescence.2013.01.005

66.

Rubin

K. H.

Bukowski

W. M.

Parker

J. G.

(2006). Peer interactions, relationships and groups. In Damon

Lerner

M. R.

(Eds.), Handbook of Child Psychology (6th ed.): Vol. 3. Social, emotional, and personality development (pp. 571–645). NewYork, NY: Wiley.

67.

Santrock

J. W.

(2019). Adolescence. McGraw-Hill Education.

68.

Schreiter

Pijnenborg

G. H.

Aan Het Rot

(2013). Empathy in adults with clinical or subclinical depressive symptoms. Journal of Affective Disorders, 150(1), 11–16. doi:10.1016/j.jad.2013.03.009

69.

Segrin

Flora

(2000). Poor social skills are a vulnerability factor in the development of psychosocial problems. Human Communication Research, 26, 489–514. doi:10.1111/j.1468-2958.2000.tb00766.x

70.

Smith

R. L.

Rose

A. J.

(2011). The “cost of caring” in youths’ friendships: Considering associations among social perspective-taking, co-rumination, and empathetic distress. Developmental Psychology, 47(6), 1792–1803. doi:10.1037/a0025309

71.

Spithoven

A. W. M.

Lodder

G. M. A.

Goossens

Bijttebier

Bastin

Verhagen

Scholte

R. H. J.

(2017). Adolescents’ loneliness and depression associated with friendship experiences and well-being: A person-centered approach. Journal of Youth and Adolescence, 46(2), 429–441.

72.

Tully

E. C.

Ames

A. M.

Garcia

S. E.

Donohue

M. R.

(2016). Quadratic associations between empathy and depression as moderated by emotion dysregulation. Journal of Psychology, 150(1), 15–35. doi:10.1080/00223980.2014.992382

73.

van der Graaff

Carlo

Crocetti

Koot

H. M.

Branje

. (2018). Prosocial behavior in adolescence: Gender differences in development and links with empathy. Journal of Youth and Adolescence, 47(5), 1086–1099. doi:10.1007/s10964-017-0786-1

74.

Vanhalst

Klimstra

T. A.

Luyckx

Scholte

R. H.

Engels

R. C.

Goossens

(2012). The interplay of loneliness and depressive symptoms across adolescence: Exploring the role of personality traits. Journal of Youth and Adolescence, 41(6), 776–787. doi:10.1007/s10964-011-9726-7

75.

Vanhalst

Luyckx

Van Petegem

Soenens

(2018). The detrimental effects of adolescents’ chronic loneliness on motivation and emotion regulation in social situations. Journal of Youth and Adolescence, 47(1), 162–176. doi:10.1007/s10964-017-0686-4

76.

van Lissa

C. J.

Hawk

S. T.

de Wied

Koot

H. M.

van Lier

Meeus

. (2014). The longitudinal interplay of affective and cognitive empathy within and between adolescents and mothers. Developmental Psychology, 50(4), 12–19. doi:10.1037/a0035050

77.

van Oort

F. V.

Greaves-Lord

Verhulst

F. C.

Ormel

Huizink

A. C

. (2009). The developmental course of anxiety symptoms during adolescence: The TRAILS study. Journal of Child Psychology and Psychiatry, 50(10), 1209–1217. doi:10.1111/j.1469-7610.2009.02092.x

78.

World Health Organization. (2013). Health statistics and health information systems. Geneva. http://www.who.int/healthinfo/global_burden_disease/definition_regions/en/

79.

Witvliet

Brendgen

van Lier

P. A. C.

Koot

H. M.

Vitaro

(2010). Early adolescent depressive symptoms: Prediction from clique isolation, loneliness, and perceived social acceptance. Journal of Abnormal Child Psychology, 38, 1045–1056. doi:10.1007/s10802-010-9426-x

80.

Wols

Scholte

R. H.

Qualter

(2015). Prospective associations between loneliness and emotional intelligence. Journal of Adolescence, 39, 40–48. doi:10.1016/j.adolescence.2014.12.007

81.

Zins

J. E.

Elias

M. J.

(2006). Social and emotional learning. In Bear

G. G.

Minke

K. M.

(Eds.), Children’s needs III: Development, prevention, and intervention (pp. 1–13). Bethesda: National Association of School Psychologists.

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