Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T00:03:38.554Z Has data issue: false hasContentIssue false

The role of social cognitions in the social gradient in adolescent mental health: A longitudinal mediation model

Published online by Cambridge University Press:  27 February 2023

Dominic Weinberg*
Affiliation:
Department of Psychology, Education & Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands Department of Interdisciplinary Social Science, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, The Netherlands
Gonneke W.J.M. Stevens
Affiliation:
Department of Psychology, Education & Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
Margot Peeters
Affiliation:
Department of Psychology, Education & Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
Kirsten Visser
Affiliation:
Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
Willem Frankenhuis
Affiliation:
Department of Psychology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, The Netherlands
Catrin Finkenauer
Affiliation:
Department of Psychology, Education & Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
*
Corresponding author: Dominic Weinberg, email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

The social gradient in adolescent mental health is well established: adolescents’ socioeconomic status is negatively associated with their mental health. However, despite changes in social cognition during adolescence, little is known about whether social cognitions mediate this gradient. Therefore, this study tested this proposed mediational path using three data waves, each 6 months apart, from a socioeconomically diverse sample of 1,429 adolescents (Mage = 17.9) in the Netherlands. Longitudinal modeling examined whether three social cognitions (self-esteem, sense of control, and optimism) mediated associations between perceived family wealth and four indicators of adolescent mental health problems (emotional symptoms, conduct problems, hyperactivity, and peer problems). There was evidence of a social gradient: adolescents with lower perceived family wealth reported more concurrent emotional symptoms and peer problems and an increase in peer problems 6 months later. Results also showed evidence of mediation through social cognitions, specifically sense of control: adolescents with lower perceived family wealth reported a decrease in sense of control (though not self-esteem nor optimism) 6 months later, and lower sense of control predicted increases in emotional symptoms and hyperactivity 6 months later. We found concurrent positive associations between perceived family wealth and all three social cognitions, and concurrent negative associations between social cognitions and mental health problems. The findings indicate that social cognitions, especially sense of control, may be an overlooked mediator of the social gradient in adolescent mental health.

Type
Regular Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Introduction

The social gradient in adolescent mental health is persistent and robust: adolescents with lower socioeconomic status (SES) have more mental health problems than adolescents with higher SES (Devenish et al., Reference Devenish, Hooley and Mellor2017; Reiss, Reference Reiss2013). A solid understanding of this social gradient is vital to improving adolescents’ mental health. Furthermore, this social gradient persists into adulthood and has enormous social and economic costs (Mackenbach et al., Reference Mackenbach, Meerding and Kunst2011; Patton et al., Reference Patton, Sawyer, Santelli, Ross, Afifi, Allen, Arora, Azzopardi, Baldwin, Bonell, Kakuma, Kennedy, Mahon, McGovern, Mokdad, Patel, Petroni, Reavley, Taiwo and Viner2016; Vigo et al., Reference Vigo, Thornicroft and Atun2016). Research on the mediators of this gradient has generally focused on the family context, documenting several factors that may explain the higher level of mental health problems in adolescents with lower SES. These include having fewer material resources, experiencing fewer stable and supportive relationships, and facing more stressful and threatening family and neighborhood environments (R. D. Conger & Donnellan, Reference Conger and Donnellan2007; McLoyd et al., Reference McLoyd, Kaplan, Purtell, Bagley, Hardaway, Smalls, Lerner and Steinberg2009). Less attention has been paid to adolescent-level factors, such as social cognitions – the psychological processes that adolescents use to make sense of themselves and others (Fiske & Taylor, Reference Fiske and Taylor2013). Social cognitions are influenced by the socioeconomic context in which people develop (Kraus et al., Reference Kraus, Piff, Mendoza-Denton, Rheinschmidt and Keltner2012; Stephens et al., Reference Stephens, Markus and Phillips2014). Furthermore, theories and evidence suggest three social cognitions – self-esteem, sense of control, and optimism – are important determinants of mental health (Carver et al., Reference Carver, Scheier and Segerstrom2010; Mann et al., Reference Mann, Hosman, Schaalma and de Vries2004; Orton et al., Reference Orton, Pennington, Nayak, Sowden, Petticrew, White and Whitehead2019; Taylor & Brown, Reference Taylor and Brown1988). Positive psychology has identified these three social cognitions to be key to well-being (Cummins & Nistico, Reference Cummins and Nistico2002; Myers & Diener, Reference Myers and Diener1995). Here, we consider the role of social cognitions in predicting mental health problems, which are recognized in the dual-factor model of mental health to be related to, but distinct from, well-being (Antaramian et al., Reference Antaramian, Huebner, Hills and Valois2010). In combination, these two propositions suggest that people with lower SES have more negative social cognitions, and those with more negative social cognitions have more mental health problems, and thus that social cognitions may mediate the social gradient in mental health.

However, little is known about whether, and which, social cognitions mediate this social gradient in adolescence (Adler & Tan, Reference Adler and Tan2017; E. Chen et al., Reference Chen, Matthews and Boyce2002; Heberle & Carter, Reference Heberle and Carter2015). This gap in the literature is striking because social cognitions are formed by making social comparisons and internalizing the views of others (Gecas, Reference Gecas1982), which are pronounced features of adolescence (Crone & Dahl, Reference Crone and Dahl2012; Jacobs et al., Reference Jacobs, Bleeker and Constantino2003). Adolescence is an important phase for the contemplation of educational and occupational futures, and preparing for adulthood (Flanagan et al., Reference Flanagan, Kim, Pykett, Finlay, Gallay and Pancer2014; Hagquist, Reference Hagquist2007; Schoon & Lyons-Amos, Reference Schoon and Lyons-Amos2017) and is therefore a critical juncture in the attainment of social status. As such, SES in adolescence has both objective (e.g., parental education, occupation or income) and subjective (i.e., perception of family SES) components, which are moderately correlated and are thought to have distinct but overlapping pathways to mental health (Quon & McGrath, Reference Quon and McGrath2014; Singh-Manoux et al., Reference Singh-Manoux, Marmot and Adler2005). Furthermore, during this life stage, when adolescents reorient from parents to peers (Brown & Larson, Reference Brown, Larson, Lerner and Steinberg2009; Prinstein, Reference Prinstein2017), social cognitions may be more strongly influenced by SES due to increased affiliation with peers from different SES backgrounds. These developmental changes may also increase the extent to which social cognitions play a role in the social gradient in mental health, so associations between SES, social cognitions, and mental health may be greater for adolescents than for children and adults. Therefore, this study explored whether three social cognitions – self-esteem, sense of control, and optimism – mediated the social gradient in adolescent mental health.

Mediation by social cognitions: self-esteem, sense of control, and optimism

Several reviews have emphasized the role of SES in the development of adaptive and reasonable social cognitions in the face of oppressive and inequitable systems (Frankenhuis & Nettle, Reference Frankenhuis and Nettle2020; Kraus et al., Reference Kraus, Piff, Mendoza-Denton, Rheinschmidt and Keltner2012; Pepper & Nettle, Reference Pepper and Nettle2017; Piff et al., Reference Piff, Kraus, Keltner and Olson2017; Sheehy-Skeffington, Reference Sheehy-Skeffington2020; Stephens et al., Reference Stephens, Markus and Phillips2014). Adolescents with lower SES are more likely than their higher SES peers to face more stressful and threatening family, school, and neighborhood environments. In turn, their families may contend with less supportive workplaces and health services and face more discriminatory policing and legal systems (see e.g., Amnesty International, 2021). In such contexts adolescents with lower SES may develop social cognitions which correspond with the uncertainty and stress of these contexts (Kraus et al., Reference Kraus, Piff, Mendoza-Denton, Rheinschmidt and Keltner2012; Sheehy-Skeffington, Reference Sheehy-Skeffington2020; Stephens et al., Reference Stephens, Markus and Phillips2014). We expect that SES influences three important social cognitions: self-esteem – the evaluation of one’s importance, worth, or value (Blascovich & Tomaka, Reference Blascovich, Tomaka, Robinson, Shaver and Wrightsman1991); sense of control – the belief that one’s actions determine outcomes (Lachman & Weaver, Reference Lachman and Weaver1998; Whitehead et al., Reference Whitehead, Pennington, Orton, Nayak, Petticrew, Sowden and White2016); and optimism – a generalized feeling of confidence in positive future outcomes (Carver et al., Reference Carver, Scheier and Segerstrom2010). Taylor and Brown’s (Reference Taylor and Brown1988) landmark paper identified these three social cognitions to be important in fostering mental health. Subsequent reviews have corroborated the evidence that adult mental health is indeed predicted by self-esteem (Mann et al., Reference Mann, Hosman, Schaalma and de Vries2004), sense of control (Orton et al., Reference Orton, Pennington, Nayak, Sowden, Petticrew, White and Whitehead2019), and optimism (Carver et al., Reference Carver, Scheier and Segerstrom2010), though most of this evidence is cross-sectional. However, less is known about the role of these three social cognitions in adolescent mental health, and there is almost no longitudinal research on this topic. Below, we outline both overlapping and distinct reasons why each of the three social cognitions may mediate the social gradient in adolescent mental health.

First, adolescents with lower SES may be more likely than their counterparts with higher SES to feel inferior and receive stigmatizing treatment (Bosma et al., Reference Bosma, Simons, Groffen and Klabbers2012; McLoyd et al., Reference McLoyd, Kaplan, Purtell, Bagley, Hardaway, Smalls, Lerner and Steinberg2009), experiences expected to lead to lower self-esteem (Falci, Reference Falci2011; Heberle & Carter, Reference Heberle and Carter2015; Rosenberg & Pearlin, Reference Rosenberg and Pearlin1978). In turn, adolescents with lower self-esteem than their peers may have more mental health problems, perhaps through processes of seeking and receiving less social support, experiencing more stress, and applying detrimental coping cognitions and behaviors (Donnellan et al., Reference Donnellan, Trzesniewski, Robins, Moffitt and Caspi2005; Orth et al., Reference Orth, Robins and Widaman2012). A meta-analysis has found robust evidence that objective SES is positively related to self-esteem during adolescence (Twenge & Campbell, Reference Twenge and Campbell2002). Individuals with lower self-esteem in early adolescence, as compared to those with higher self-esteem, showed greater increases in mental health problems later in adolescence (Ciarrochi et al., Reference Ciarrochi, Heaven and Davies2007; Masselink et al., Reference Masselink, Van Roekel and Oldehinkel2018; Orth et al., Reference Orth, Robins and Roberts2008). One cross-sectional study in late adolescents found that subjective SES was positively related to self-esteem and self-esteem positively related to life satisfaction (Yan et al., Reference Yan, Yang, Wang, You and Kong2020). Yet, to our knowledge, no longitudinal research has considered self-esteem as a mediator of the social gradient in adolescent mental health.

Second, we expect adolescents with lower SES to have fewer social and material resources to exercise control over their environment than adolescents with higher SES (Marmot, Reference Marmot2004; Stephens et al., Reference Stephens, Markus and Phillips2014). They may also be socialized into holding autonomy-limiting beliefs, through their greater likelihood of experiencing authoritarian parenting or living in disadvantaged neighborhoods (K. J. Conger et al., Reference Conger, Williams, Little, Masyn and Shebloski2009; Lareau, Reference Lareau2003). Adolescents with lower SES are thus expected to have a lower sense of control (Bosma et al., Reference Bosma, Theunissen, Verdonk and Feron2014; Shifrer, Reference Shifrer2019; Wheaton, Reference Wheaton1980). Adolescents with lower sense of control, are more likely than adolescents with higher sense of control to feel trapped, frustrated, and anxious, and be at risk of mental health problems (Bosma et al., Reference Bosma, Theunissen, Verdonk and Feron2014; Chorpita & Barlow, Reference Chorpita and Barlow1998; Whitehead et al., Reference Whitehead, Pennington, Orton, Nayak, Petticrew, Sowden and White2016). Evidence from two longitudinal studies in mid-late adolescence has shown, independently, that objective SES was positively associated with sense of control 6 years later (Ahlin & Antunes, Reference Ahlin and Antunes2015), and that sense of control was negatively associated with mental health problems 2 years later (Sullivan et al., Reference Sullivan, Thompson, Kounali, Lewis and Zammit2017). We know of only one longitudinal study examining sense of control as a mediator of the social gradient in adolescent mental health, which found that sense of control at age 16 mediated the association between objective SES at age 5 and depression at age 18 (Culpin et al., Reference Culpin, Stapinski, Miles, Araya and Joinson2015). However, as these constructs were measured at one time point only, unmeasured and uncontrolled exogenous variables may have biased these results (Cole & Maxwell, Reference Cole and Maxwell2003).

Third, we expect adolescents with lower SES to be less optimistic than adolescents with higher SES, because they have fewer resources to achieve their future goals (Brumley et al., Reference Brumley, Jaffee and Brumley2017; McLoyd et al., Reference McLoyd, Kaplan, Purtell, Bagley, Hardaway, Smalls, Lerner and Steinberg2009) and experience more stressful events that can be projected onto their own futures (Boehm et al., Reference Boehm, Chen, Williams, Ryff and Kubzansky2015; Gallo & Matthews, Reference Gallo and Matthews2003). Optimism helps adolescents cope with threat and stress, motivates persistence and agentic action (Hitlin et al., Reference Hitlin, Erickson and Brown2015), and supports the maintenance of social relationships (McWhirter & McWhirter, Reference McWhirter and McWhirter2008), all of which are key drivers of positive mental health (S. Cohen & Wills, Reference Cohen and Wills1985). Indeed, some evidence suggests that less optimistic adolescents are at greater risk of developing mental health problems (Patton et al., Reference Patton, Tollit, Romaniuk, Spence, Sheffield and Sawyer2011). Two cross-sectional studies in late adolescence found optimism to mediate the association between SES (both objective and subjective) and depression (Piko et al., Reference Piko, Luszczynska and Fitzpatrick2013; Zou et al., Reference Zou, Xu, Hong and Yuan2020), but longitudinal research is lacking.

Although the studies described above provide insight into the mediating role of social cognitions in the social gradient in adolescent mental health, they do not give a complete picture. First, they studied self-esteem, sense of control, and optimism in isolation, yet, these three social cognitions are interrelated (Ben-Zur, Reference Ben-Zur2003; Hitlin & Johnson, Reference Hitlin and Johnson2015; Kim et al., Reference Kim, Bassett, So and Voisin2019). Establishing the robustness of each of these mediational contributions is a stepping-stone toward better understanding the suitability of interventions and services which aim to establish better mental health and more positive social cognitions in adolescents with lower SES (Goyer et al., Reference Goyer, Garcia, Purdie-Vaughns, Binning, Cook, Reeves, Apfel, Taborsky-Barba, Sherman and Cohen2017; Yeager et al., Reference Yeager, Dahl and Dweck2018). Such interventions must be mindful of the adaptive and reasonable nature of these social cognitions. Second, existing studies have used only one indicator of adolescent mental health (depression or life satisfaction), and we are unaware of research which includes both internalizing and externalizing problems, despite differences in the strength of the social gradient by adolescent mental health outcome (Devenish et al., Reference Devenish, Hooley and Mellor2017; Quon & McGrath, Reference Quon and McGrath2014; Reiss, Reference Reiss2013). Third, only one previous study tested mediation with longitudinal data, and that study did not include controls for stability in the constructs over time, an important facet in establishing evidence for causal effects (Cole & Maxwell, Reference Cole and Maxwell2003; MacKinnon et al., Reference MacKinnon, Fairchild and Fritz2007). Our study addresses all three issues.

The current study

The current study extends the literature by investigating three potential mediators of the association between SES and different indicators of adolescent mental health: self-esteem, sense of control, and optimism. The study uses a sample of adolescents in vocational education, who followed a range of study paths that are likely to influence their educational and occupational futures. Furthermore, it uses autoregressive path analysis, specifically a lagged panel model design with three waves of longitudinal data spanning 1 year (Cole & Maxwell, Reference Cole and Maxwell2003). This design is well suited to testing the between-person effects outlined above (Orth et al., Reference Orth, Clark, Donnellan and Robins2021). We expected that all three social cognitions would mediate the association between SES and adolescent mental health.

This study focused on subjective SES (perceived family wealth) based on recent findings in the Netherlands showing that it is more strongly associated with adolescent mental health than objective measures of SES (Weinberg et al., Reference Weinberg, Stevens, Duinhof and Finkenauer2019). This stronger association may be because subjective SES is a more precise measure of social position (i.e., it reflects a cognitive averaging of various markers of family SES, such as parental occupation, educational level, and wealth) or because the experience of one’s relative social position is particularly important for mental health (Singh-Manoux et al., Reference Singh-Manoux, Marmot and Adler2005). It may also be that subjective SES partially mediates the effect of objective SES (Moreno-Maldonado et al., Reference Moreno-Maldonado, Rivera, Ramos and Moreno2018). Furthermore, several recent findings suggest that perceived, rather than objective (i.e., actual), experience of childhood adversity is associated with mental health (e.g., Pollak & Smith, Reference Pollak and Smith2021), potentially in a causal manner (Baldwin & Degli Esposti, Reference Baldwin and Degli Esposti2021). We included four indicators of mental health problems: emotional symptoms, conduct problems, hyperactivity, and peer problems. These indicators capture dimensions of mental health related to the main categories of mental health problems seen in classification systems (A. Goodman et al., Reference Goodman, Lamping and Ploubidis2010).

The study coincided with the Covid-19 pandemic, which began during data collection. There is emerging international evidence from systematic reviews that lower SES adolescents experienced more negative consequences from the pandemic (Collin-Vézina et al., Reference Collin-Vézina, Fallon and Caldwell2022; Kauhanen et al., Reference Kauhanen, Wan Mohd Yunus, Lempinen, Peltonen, Gyllenberg, Mishina, Gilbert, Bastola, Brown and Sourander2022), analysis of the sample used for the current study suggests that the impact of the pandemic on adolescent mental health may not have varied strongly by family SES (Stevens et al., Reference Stevens, Buyukcan-Tetik, Maes, Weinberg, Vermeulen, Visser and Finkenauer2022).

Method

We preregistered an analysis plan at the Open Science Framework, although the eventual analyses deviated from this plan.Footnote 1 The preregistration and analysis scripts are available at https://osf.io/fsw3j/.

Sample

We used data from the Youth Got Talent project, an ongoing longitudinal study investigating the SES-mental health gradient in adolescence. Adolescents (aged 16+) attended classes (n = 72) in three vocational schools in the region of Utrecht in the Netherlands and participated in training mainly in creative, technical, and health education. Adolescents (N = 1,429) filled out questionnaires on three occasions: in autumn 2019/winter 2020 (T1, n = 1,231); roughly 6 months later in late spring 2020 (T2, n = 830); and roughly 1 year after the first wave in autumn 2020/winter 2021 (T3, n = 576). There was substantial attrition, with only about a quarter of the adolescents (386) participating at all three time points. Structural changes made to the project necessitated by the Covid-19 pandemic and education transitioning to being (largely) online in spring 2020 were substantially responsible for the attrition. We were also unable to reach participants that had dropped out of school between measurements. Roughly a quarter of the classes that participated in Wave 1 did not participate in Wave 2. Within classes that participated, the adolescent response rate was over 65% and about 15% of the non-responding adolescents had dropped out of school before Wave 2. In Wave 3, one school dropped out of the study, so nearly half of the classes that participated in Wave 1 did not participate in Wave 3. Within classes that participated, the adolescent response rate was over 60% and roughly 20% of the non-responding adolescents had dropped out of school before Wave 3. Researchers administrated self-report questionnaires in the classroom (T1) or during online lessons (T2 and T3) and these took about 20–30 min to complete. Adolescents gave active consent and were informed that data would be anonymized.

After taking into account differences between classes in the covariates (gender, age, and migration background), the class-level ICC for all main study variables (SES, social cognitions, and mental health variables) was small (i.e., <10% of the variance in the main variables was at the class level, see Hox, Reference Hox2010). For most of these variables, the ICC was negligible (<5%), so we determined that adjusting for clustering was unnecessary.

We included all participants in this study. Just over half of the adolescents were girls (57%), the rest were boys. Almost one fifth (19%) had a non-western migration background and this percentage is highly comparable to the Dutch population of 15–20-year-olds in 2019 (18%; Statistics Netherlands, CBS, 2022). The mean age of all participants at T1 was 17.9 years (with a standard deviation of 1.9 and ranging from 15 to 30). There was missing data based on attrition, but very little missing data per time point when an adolescent participated: in all three waves, over 90% of participants answered over 95% of the questions. Demographic characteristics of adolescents who participated at all three time points (n = 386) were compared to those of adolescents who participated in fewer than three time points (n = 1,043). Adolescents who participated in all waves: were younger (M age = 17.4 vs. M age = 18.0), less often had a non-western migration background (9% vs. 23%), and at T1 had higher family affluence (.55 vs. 48), higher perceived family wealth (3.12 vs. 2.98), lower self-esteem (4.61 vs. 4.84), and lower levels of conduct problems (0.81 vs. 1.00). All these differences between adolescents who participated at all three time points, and those who did not, were small (J. Cohen, Reference Cohen1992), and we found no differences between the groups at T1 in sense of control, optimism, or the other mental health problems measured. The project was approved by the Ethics Assessment Committee of the Faculty of Social Sciences at Utrecht University in 2018 (FETC18-070; updated in 2020).

Measures

Socioeconomic status

At all three time points, adolescents reported perceived family wealth by answering the question, “How well off do you think your family is?” The item had a 5-point response scale from 1 (very well off) to 5 (not at all well off), and we reversed the scale so that higher scores indicated higher perceived family wealth. In the Dutch version of this scale, the term used is ‘rijk’, which generally translates to ‘rich’, emphasizing the economic aspect of this measure. The measure is easy to answer for adolescents and reflects the subjective dimension of SES (Inchley et al., Reference Inchley, Currie, Cosma, Piper and Spanou2017).

Adolescent social cognitions

Social cognitions were measured with the same instruments at all three time points. Adolescents reported self-esteem using the single-item self-esteem scale (Robins et al., Reference Robins, Hendin and Trzesniewski2001). The item, “I have high self-esteem”, was measured on a 5-point Likert scale, ranging from 1 (not very true of me) to 7 (very true of me). Higher scores indicated higher self-esteem. This single-item scale is reliable, valid in older adolescents, has convergent correlation with the most widespread instrument for measuring self-esteem, is widely used, and is considered to be an appropriate brief instrument for measuring global self-esteem in longitudinal (online) studies (Brailovskaia & Margraf, Reference Brailovskaia and Margraf2020; Robins et al., Reference Robins, Hendin and Trzesniewski2001).

Adolescents reported sense of control using the sense of control scale (Lachman & Weaver, Reference Lachman and Weaver1998). The scale consists of 12 questions, covering two subscales of personal mastery (four questions) and perceived constraints (eight questions), measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Sample items are “I can do just about anything I really set my mind to” (personal mastery) and “What happens in my life is often beyond my control” (perceived constraints). We decided a priori to omit one item (in the constraints subscale, “I sometimes feel I am being pushed around in my life”), due to the lack of a suitable Dutch translation, leaving 11 questions. In line with previous research, we reverse-coded the perceived constraints subscale and then computed the mean of the two scales to create a measure of control, with higher scores indicating higher sense of control (Lachman & Agrigoroaei, Reference Lachman and Agrigoroaei2010).Footnote 2 The scale has good psychometric properties (Lachman & Weaver, Reference Lachman and Weaver1998), including in adolescents (B. Chen et al., Reference Chen, Luo, Wu, Chen and Zhao2021). In the current study, the scale had good internal consistency (α = .79/.79/.81 at T1/T2/T3).

Adolescents reported optimism using the future emotions questions scale (Hektner, Reference Hektner1995; Liebenberg et al., Reference Liebenberg, Sanders, Munford and Thimasarn-Anwar2015). The scale asked, “When thinking about the future, to what extent do you feel any of the following?”. The scale listed seven emotions, with responses measured on a 5-point Likert scale running from 1 (not at all) to 5 (very much). The scale has good internal as well as convergent and divergent validity in adolescents (Liebenberg et al., Reference Liebenberg, Sanders, Munford and Thimasarn-Anwar2015). In line with previous research, we calculated the mean of the three positive emotions – confident, enthusiastic, powerful – as our measure of optimism (Boden et al., Reference Boden, Sanders, Munford, Liebenberg and McLeod2016). In the current study, the items had good internal consistency (α = .82/.79/.83 at T1/T2/T3).

Adolescent mental health problems

Mental health problems were measured with the same instruments at all three time points. Adolescents reported emotional symptoms, conduct problems, hyperactivity, and peer problems using the SDQ-R: a revised version of the self-report Strengths and Difficulties Questionnaire (SDQ) that has better psychometric properties in adolescents than the original (Duinhof et al., Reference Duinhof, Lek, de Looze, Cosma, Mazur, Gobina, Wüstner, Vollebergh and Stevens2019; R. Goodman, Reference Goodman1997). The SDQ-R asks about behavior and feelings over the past 6 months – sample items are “I get very angry and often lose my temper” and “I worry a lot”. The SDQ-R has a 3-point ordinal response scale: 0 (not true), 1 (somewhat true), 2 (certainly true). The SDQ-R consists of 15 items measuring four subscales: emotional symptoms (5 items); conduct problems (4 items); hyperactivity–inattention problems (3 items); and peer relationship problems (3 items). In this study, two subscales, emotional symptoms (ordinal α = .82/.82/.84 at T1/T2/T3) and hyperactivity–inattention problems (ordinal α = .79/.80/.81 at T1/T2/T3), had good internal consistency (Gadermann et al., Reference Gadermann, Guhn and Zumbo2012), though internal consistency for conduct problems (ordinal α = .58/.71/.67 at T1/T2/T3) and peer problems subscales (ordinal α = .53/.51/.59 at T1/T2/T3) was less adequate, in line with former research (Duinhof et al., Reference Duinhof, Lek, de Looze, Cosma, Mazur, Gobina, Wüstner, Vollebergh and Stevens2019). For participants who completed more than half of the subscale items, we computed mean scores, which were then multiplied by five to retain comparability with the original SDQ, such that higher subscale scores indicated more problems (ranging from 0 to 10).

Potential confounding variables

We included four confounding variables, given the likely effect of gender, age, migration background, and family affluence on adolescent mental health in the Netherlands (Duinhof et al., Reference Duinhof, Smid, Vollebergh and Stevens2020). At T1 (or later, if missing at T1), adolescents reported: whether they were a girl (coded 0) or boy (coded 1); month and year of birth (used to calculate age at the date of data collection); parents’ birth countries; and family affluence. We also considered adding dummy variables for the three schools, but these were not associated with any of the social cognitions or mental health problems, thus were not included in the models.

Conforming with previous research in the Netherlands, and Dutch statistical agencies, we measured migration background by distinguishing between: adolescents with both parents born in the Netherlands; adolescents with at least one parent with a western immigration background; and adolescents with at least one parent with a non-western immigration background (Duinhof et al., Reference Duinhof, Smid, Vollebergh and Stevens2020; Statistics Netherlands, CBS, 2020). Only 6% of adolescents had a western immigration background, so we merged this group with adolescents whose parents were born in the Netherlands, as both groups are western.

Adolescents reported family affluence using the Family Affluence Scale (FAS), which consists of six items about family material assets: car(s)/van(s), own bedroom, holiday(s) abroad, computer(s), dishwasher, and bathroom(s) (Torsheim et al., Reference Torsheim, Cavallo, Levin, Schnohr, Mazur, Niclasen and Currie2016). For participants who completed all scale items, we summed item scores, then ridit-transformed the sum score into a continuous family affluence score (range = 0–1; mean = 0.5), with a higher score indicating more material assets (Elgar et al., Reference Elgar, Xie, Pförtner, White and Pickett2017). The FAS is a reliable and valid instrument that enables adolescents to report their family affluence (Torsheim et al., Reference Torsheim, Cavallo, Levin, Schnohr, Mazur, Niclasen and Currie2016).

Data analysis

We investigated descriptive statistics to see whether school, gender, age, migration status, school, and family affluence were associated with perceived family wealth and mental health and thus needed to be treated as confounders. We followed guidelines for using path analysis to test mediational hypotheses with longitudinal data, accounting for stability of, and prior associations between, the variables (Cole & Maxwell, Reference Cole and Maxwell2003). Confounders were included in all models, and error covariances and autoregressive paths were constrained to be time-invariant. We used R, version 4.0.3 (R Core Team, 2020), and the lavaan package, version 0.6-5 (Rosseel, Reference Rosseel2012). We modeled missing data using Full Information Maximum Likelihood (FIML). Given our clustered data, with students within classes, we analyzed robust standard errors by employing the MLR estimator (maximum likelihood estimation with robust (Huber–White) standard errors; see McNeish et al., Reference McNeish, Stapleton and Silverman2017). We evaluated goodness-of-fit using two measures, with good model fit indicated by CFI ≥ .95 and RMSEA < .06 (Hu & Bentler, Reference Hu and Bentler1999).

In our initial model (Model 1), we specified a longitudinal model to examine whether SES predicted later increases in mental health problems (see Figure 1 for diagram showing results). Next, we investigated mediation of the path from perceived family wealth to mental health through social cognitions, entering one social cognition at a time (Models 2a–c; see Figures 24 for diagrams showing results) in order to enable comparisons with existing research which examined a single social cognition in isolation. Finally, we investigated a multiple mediation model with all three social cognitions (Model 2d; see also Figures 24), though we interpreted these results cautiously, given the risk of multicollinearity between the social cognition variables. To examine mediation, we tested the significance of indirect effects (i.e., the product of the path from perceived family wealth at T1 to social cognition at T2, and the path from social cognition at T2 to mental health problem at T3) with the lavaan package, which uses the delta method. To control for inflation of Type I error rates based on multiple testing, we applied the Benjamini–Hochberg procedure with a false discovery rate of 0.05 (Benjamini & Hochberg, Reference Benjamini and Hochberg1995). We interpreted standardized regression coefficients as negligible (|r| < 0.1), small (|r| = 0.1–0.3), medium (|r| = 0.3–0.5), or large (|r| > 0.5) (J. Cohen, Reference Cohen1992).

Figure 1. Model showing associations between confounders, perceived family wealth, and adolescent mental health problems (Model 1). Notes. Standardised coefficients. Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05), not shown. Only significant coefficients for confounders, and associations between perceived family wealth at T1 and mental health at T1 and T3 (1 year later), are shown. This model was the basis for Models 2a–d. Model fit 1 – χ2 (111) = 365.8, p < 0.001, CFI = 0.944, RMSEA = 0.042. aReference category: girl. bReference category: Dutch/western. *p < .05. **p < .01.

Figure 2. The association between SES and adolescent mental health problems mediated by self-esteem (Models 2a and 2d). Notes. Standardized coefficients (same values constrained to equality may differ slightly after standardization). The first coefficient indicates Model 2a result (self-esteem only), the second coefficient indicates the Model 2d result (all three social cognitions included in the model). Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05). Dashed thick lines indicate significance of path differs between Models 2a and 2d. All paths were estimated in the same models, but results are presented in four panels (i.e., for each mental health outcome) for clarity. Associations with confounders and covariances between mental health problems are not shown. Key variables in the hypothesized mediation path are highlighted with a double border. Model fit 2a – χ2 (150) = 455.9, p < 0.001, CFI = 0.948, RMSEA = 0.040. Model fit 2d – χ2 (249) = 697.7, p < 0.001, CFI = 0.946, RMSEA = 0.037. *p < .05. **p < .01.

Figure 3. The association between SES and adolescent mental health problems mediated by sense of control (Models 2b and 2d). Notes. Standardized coefficients (same values constrained to equality may differ slightly after standardization). The first coefficient indicates Model 2b result (sense of control only), the second coefficient indicates the Model 2d result (all three social cognitions included in the model). Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05). Dashed thick lines indicate significance of path differs between Models 2b and 2d. All paths were estimated in the same models, but results are presented in four panels (i.e., for each mental health outcome) for clarity. Associations with confounders and covariances between mental health problems are not shown. Key variables in hypothesized mediation path highlighted with double border. Model fit 2b – χ2 (150) = 443.3, p < 0.001, CFI = 0.947, RMSEA = 0.039. Model fit 2d – χ2 (249) = 697.7, p < 0.001, CFI = 0.946, RMSEA = 0.037. *p < .05. **p < .01.

Figure 4. The association between SES and adolescent mental health problems mediated by optimism (Models 2c and 2d). Notes. Standardized coefficients (same values constrained to equality may differ slightly after standardization). The first coefficient indicates Model 2c result (optimism only), the second coefficient indicates the Model 2d result (all three social cognitions included in the model). Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05). Dashed thick lines indicate significance of path differs between Models 2c and 2d. All paths were estimated in the same models, but results are presented in four panels (i.e., for each mental health outcome) for clarity. Associations with confounders and covariances between mental health problems are not shown. Key variables in hypothesized mediation path highlighted with double border. Model fit 2c – χ2 (150) = 430.3, p < 0.001, CFI = 0.948, RMSEA = 0.038. Model fit 2d – χ2 (249) = 697.7, p < 0.001, CFI = 0.946, RMSEA = 0.037. *p < .05. **p < .01.

Results

Descriptive statistics

Table 1 shows variable means and standard deviations, all correlations between confounders and the main study variables, and concurrent correlations between the variables. Compared to scale midpoints, adolescents’ perceptions of their family wealth remained fairly average over the three time points, and they reported relatively high self-esteem, sense of control, and optimism, all of which were fairly stable across time. The mean levels of mental health problems were relatively low, except for hyperactivity, which was somewhat higher, and there was substantial variation in all mental health problems. Emotional symptoms and hyperactivity increased from T1 to T3. Associations between the confounders and the main study variables were fairly stable over time. There were several exceptions to this: slight changes in associations between the confounders and optimism from T1 to T3, and changes in associations between migration background, and to some extent, family affluence, and the main study variables at T3. These changes may have been due to changes in the sample characteristics across the time points (see the sample description above).

Table 1. Descriptive statistics (means, standard deviations, ranges, ns, and correlations) for study variables

Notes. Correlations between main study variables are shown per time-point. aParticipants under 16 at the start of data collection were not included at T1, but participated in T2 and/or T3 once they had reached 16. bReference category: girl. cReference category: Dutch/western. *p < .05. **p < .01.

Concurrent associations between the main study variables were generally stable over time. At all time points, perceived family wealth was positively associated with social cognitions (rs ranged from .11 to .21) and negatively associated with emotional symptoms and peer problems (rs range from −.11 to −.18). At all time points, the social cognitions were positively associated with each other (rs range from .43 to .53) and negatively associated with mental health problems (rs range from −.13 to −.60), with one exception: self-esteem was not associated with conduct problems (r = −.05/−.07/.00 at T1/T2/T3). Apart from hyperactivity and peer problems, which had a small, or no, association (r = .11/.07/.05 at T1/T2/T3), all other associations between mental health problems were positive (rs range from .21 to .36).

Associations between confounders, perceived family wealth and mental health problems

In Model 1, when we included confounding variables and perceived family wealth only, we found that older adolescents reported higher levels of emotional symptoms and peer problems at T1; girls reported higher levels of emotional symptoms at T1 than boys, and boys reported higher levels of conduct problems at T1 than girls; adolescents with a Dutch/western migration background reported more emotional symptoms and more hyperactivity at T1 than adolescents with a non-western migration background; and adolescents with higher family affluence reported fewer emotional symptoms at T1. Perceived family wealth at T1 was associated with emotional symptoms and peer problems at T1. All autoregressive paths for perceived family wealth and the mental health problems were significant, indicating stability in these constructs. Perceived family wealth at T1 was only associated with peer problems at T3 (1 year later; see Figure 1).

Mediation of associations between perceived family wealth and mental health problems

Models 2a-d examined indirect effects of perceived family wealth on mental health problems. Considering the three social cognitions one at a time, the results of the model with single mediators (2a–c) were compared to those of the multiple mediation model (which included the three social cognitions in concert, 2d). Mediation paths were constrained to be time-invariant (i.e., T1–T2 paths were equal to T2–T3 paths), so results showed whether perceived family wealth predicted change in social cognitions 6 months later, and whether social cognitions predicted change in mental health 6 months later.

Testing mediation through self-esteem

Models 2a and 2d showed that perceived family wealth at T1 was positively associated with self-esteem at T1, but there was no evidence perceived family wealth predicted change in self-esteem 6 months later. Self-esteem at T1 was concurrently negatively associated with emotional symptoms, hyperactivity, and peer problems (i.e., at T1). Lower self-esteem also predicted increases in emotional symptoms 6 months later, a result which attenuated slightly, but still held, in the multiple mediation model (2d). In this model, lower self-esteem also predicted increases in later conduct problems. There was no evidence for indirect effects of perceived family wealth on mental health problems through self-esteem. In sum, we found no mediation, because perceived family wealth did not predict changes in self-esteem, though lower self-esteem did predict increases in later emotional symptoms and decreases in later conduct problems.

Testing mediation through sense of control

Models 2b and 2d showed that perceived family wealth at T1 was concurrently positively associated with sense of control, and perceived family wealth also positively predicted sense of control 6 months later. Sense of control at T1 was concurrently negatively associated with all four mental health problems. Lower sense of control also predicted increases in emotional symptoms and hyperactivity 6 months later, though the former result did not hold in the multiple mediation model (2d). There were indirect effects of perceived family wealth on both emotional symptoms and hyperactivity through sense of control, though both findings disappeared in the multiple mediation model. In sum, we found evidence for mediation: lower perceived family wealth predicted a decrease in sense of control, and lower sense of control predicted increases in later emotional symptoms and hyperactivity (though only in univariate model but not in a multivariate model with all three social cognitions).

Testing mediation through optimism

Finally, Models 2c and 2d showed that perceived family wealth at T1 was positively associated with optimism at T1, but we found no evidence that perceived family wealth predicted later optimism. Optimism at T1 was concurrently negatively associated with all four mental health problems, and less optimism also predicted increases in all four mental health problems 6 months later. However, these findings all attenuated, and were not found in the multivariate model which included all three social cognitions (2d). There was no evidence of indirect effects of perceived family wealth on mental health through optimism. In sum, we found no mediation, because perceived family wealth did not predict changes in optimism, though less optimism did predict increases in later mental health problems (but not once self-esteem and sense of control were also taken into account).

Discussion

This study was the first, to our knowledge, to explore whether three social cognitions – self-esteem, sense of control, and optimism – mediated the social gradient in adolescent mental health. Using longitudinal models, thereby controlling for stability in the constructs over time, we found evidence that sense of control mediated this social gradient. Adolescents with lower perceived family wealth reported a decrease in sense of control 6 months later, and lower sense of control predicted increases in emotional symptoms and hyperactivity 6 months later (though this was not seen in the multivariate model with all three social cognitions). In contrast, perceived family wealth predicted neither later self-esteem nor later optimism, so there was no longitudinal evidence for mediation through either self-esteem or optimism. However, these two social cognitions did predict later mental health: adolescents with lower self-esteem reported a later increase in emotional symptoms and a decrease in conduct problems, while adolescents with less optimism reported a later increase in all four mental health problems.

To better understand possible links between perceived family wealth, social cognitions, and adolescent mental health, we also considered concurrent associations, which helped to contextualize previous cross-sectional research findings. At T1, adolescents with lower perceived family wealth reported more negative social cognitions (lower self-esteem, lower sense of control, and less optimism), more emotional problems, and more peer problems. Additionally, adolescents with more negative social cognitions – lower self-esteem, lower sense of control, and less optimism – had more mental health problems (for all four outcomes).

Our finding that adolescents with lower perceived family wealth reported (compared to adolescents with higher perceived family wealth) higher levels of emotional symptoms and peer problems and reported a relative increase in peer problems 6 months later, builds on previous cross-sectional findings in the Netherlands (Weinberg et al., Reference Weinberg, Stevens, Duinhof and Finkenauer2019). The longitudinal design enabled us to test more stringently the directionality of the association between perceived family wealth and adolescent mental health (see also E. Goodman et al., Reference Goodman, Huang, Schafer-Kalkhoff and Adler2007; Rahal et al., Reference Rahal, Huynh, Cole, Seeman and Fuligni2020). Further longitudinal research could replicate our finding that subjective SES precedes adolescent peer problems or explore this relationship in another country or over a different length of time. Furthermore, given the possibility of nonlinear associations between subjective SES and adolescent mental health, with adolescents with high SES also potentially at risk (Luthar et al., Reference Luthar, Kumar and Zillmer2020), future studies could also test quadratic terms.

We found evidence that sense of control was a mediator: it was concurrently associated with perceived family wealth and all four adolescent mental health outcomes and was a longitudinal mediator of paths from perceived family wealth to emotional symptoms and hyperactivity. Our findings built on previous research, which measured SES, sense of control, and depression at a single time point (Culpin et al., Reference Culpin, Stapinski, Miles, Araya and Joinson2015). Adolescents with a lower sense of control tend to have more feelings of anxiety, frustration, powerlessness, and being trapped, than their peers with higher sense of control, and these emotional responses may explain why adolescents with lower sense have higher levels of mental health problems (Bosma et al., Reference Bosma, Theunissen, Verdonk and Feron2014; Chorpita & Barlow, Reference Chorpita and Barlow1998; Jung et al., Reference Jung, Krahé and Busching2018; Whitehead et al., Reference Whitehead, Pennington, Orton, Nayak, Petticrew, Sowden and White2016). Most previous research on the role of sense of control in mental health has been focused on adults and been based on experiences of control in the workplace (Whitehead et al., Reference Whitehead, Pennington, Orton, Nayak, Petticrew, Sowden and White2016); our results support proposals to pay further attention to how sense of control develops in adolescence and its role in the social gradient in adolescent mental health (Pearce et al., Reference Pearce, Dundas, Whitehead and Taylor-Robinson2019). For example, research could explore mechanisms that may link SES and sense of control.

We found no longitudinal evidence that self-esteem mediated the social gradient in adolescent mental health, because perceived family wealth did not predict self-esteem 6 months later. Possibly, the absence of findings was due to the stability in these constructs during the 1 year of this study, and further longer-term research into these associations may be fruitful given we found concurrent associations between perceived family wealth, self-esteem, and mental health problems, which supported existing research (Yan et al., Reference Yan, Yang, Wang, You and Kong2020). We did find that adolescents with lower self-esteem than their peers reported an increase in later emotional symptoms. Additionally, and intriguingly, once we had taken sense of control and optimism into account, higher self-esteem predicted more conduct problems 6 months later. This result supports suggestions that higher self-esteem can be a risk factor for conduct problems when it indicates narcissism – insecure and inauthentic self-esteem which is vulnerable to ego threats (Menon et al., Reference Menon, Tobin, Corby, Menon, Hodges and Perry2007).

Similarly, we found no longitudinal evidence that optimism mediated the social gradient in adolescent mental health. Though perceived family wealth was concurrently associated with optimism, it did not predict change in optimism 6 months later. Optimism is relatively stable over time (Carver et al., Reference Carver, Scheier and Segerstrom2010), and an alternative explanation for this cross-sectional association is that optimism is a precursor to perceived family wealth. Being optimistic may help adolescents perceive their SES more positively and may also help them improve educational outcomes and other markers of SES (Ciarrochi et al., Reference Ciarrochi, Heaven and Davies2007), which could also increase future perceptions of wealth. This possibility was not modeled in our study, nor in prior research on optimism’s role in the social gradient (Piko et al., Reference Piko, Luszczynska and Fitzpatrick2013; Zou et al., Reference Zou, Xu, Hong and Yuan2020). However, though we found no evidence optimism mediated the social gradient, we found that adolescents with less optimism, as compared to adolescents with more optimism, reported a later increase in all four mental health problems. This finding supports evidence that optimism can reduce mental health problems (Patton et al., Reference Patton, Tollit, Romaniuk, Spence, Sheffield and Sawyer2011), perhaps because it helps adolescents cope with stress, persist in the face of challenges, and develop good relationships (S. Cohen & Wills, Reference Cohen and Wills1985; Patton et al., Reference Patton, Tollit, Romaniuk, Spence, Sheffield and Sawyer2011).

Along with specific findings for each social cognition, the study provides some support for our general hypothesis that adolescents’ social cognitions mediate the social gradient in adolescent mental health. The results showed that adolescents with lower perceived family wealth had more negative social cognitions, perhaps in response to the uncertainty and stress of their developmental context (Kraus et al., Reference Kraus, Piff, Mendoza-Denton, Rheinschmidt and Keltner2012; Sheehy-Skeffington, Reference Sheehy-Skeffington2020; Stephens et al., Reference Stephens, Markus and Phillips2014). We also found, in general, that adolescents with more negative social cognitions had more mental health problems (cf., Taylor & Brown, Reference Taylor and Brown1988). Notably, several longitudinal associations between the social cognitions and mental health problems disappeared in the multivariate model which took all three social cognitions into account. Alongside the medium–large correlations we saw between the social cognitions, this suggests that the three social cognitions share pathways to mental health, perhaps through processes such as adaptive coping, persistence, and relationship maintenance (Carver et al., Reference Carver, Scheier and Segerstrom2010; Hitlin et al., Reference Hitlin, Erickson and Brown2015; McWhirter & McWhirter, Reference McWhirter and McWhirter2008). These social cognitions may be particularly important during adolescence, and further research could investigate their role in the social gradient in well-being as well as mental health problems.

Much existing research on the social gradient in adolescent mental health has focused on family factors (R. D. Conger & Donnellan, Reference Conger and Donnellan2007; Devenish et al., Reference Devenish, Hooley and Mellor2017), yet our findings emphasize that adolescents’ internal cognitions – particularly, how adolescents interpret and make sense of themselves (Adler & Tan, Reference Adler and Tan2017; E. Chen et al., Reference Chen, Matthews and Boyce2002; Fiske & Taylor, Reference Fiske and Taylor2013) – may also be relevant to this social gradient. During this age period, when adolescents orient toward peers, think about their future, and reflect on social status, links between SES, social cognitions, and mental health may be greater, and further research attention on this topic is warranted (Brown & Larson, Reference Brown, Larson, Lerner and Steinberg2009; Crone & Dahl, Reference Crone and Dahl2012; Flanagan et al., Reference Flanagan, Kim, Pykett, Finlay, Gallay and Pancer2014). Future research could look at dynamic relations between social cognitions and family factors during adolescence and their role in the social gradient in adolescent mental health (see also Boylan et al., Reference Boylan, Cundiff, Jakubowski, Pardini and Matthews2018; R. D. Conger & Donnellan, Reference Conger and Donnellan2007; Kim et al., Reference Kim, Bassett, So and Voisin2019). It may also be important to use person-centered approaches, given evidence that positive social cognitions may be unreasonable for some adolescents in some social contexts (Pepper & Nettle, Reference Pepper and Nettle2017; Stephens et al., Reference Stephens, Markus and Phillips2014). One possibility is that adolescents may benefit from the development of critical consciousness (awareness, attention, and analysis of societal structures and inequality; Freire, 1970/Reference Freire1993; Yosso, Reference Yosso2005), because critical consciousness is a ‘positive social cognition’ that is not merely based on accepting and adapting to injustice but can enable (collective) action to challenge oppressive and inequitable systems (Phillips et al., Reference Phillips, Adams and Salter2015). However, placing too much emphasis on individual responsibility for inequality and minimizing attention toward changing oppressive and inequitable systems may be considered a fundamental attribution error (Chater & Loewenstein, Reference Chater and Loewenstein2022; Ross, Reference Ross and Berkowitz1977) and structural solutions are also necessary.

Strengths and Limitations

This study has several strengths, including its longitudinal mediation model design with three waves of data, which could distinguish effects over time after taking into account the stability in constructs. It also used a socioeconomically diverse sample of adolescents in vocational education and included multiple indicators of adolescents’ social cognitions and mental health problems. However, our study also has limitations. First, though longitudinal data gives an indication of the temporal precedence necessary for studying mediation, our results do not rule out reverse causation (social cognitions or mental health may precede perceptions of family wealth), nor that personality and genetic factors may confound these associations (Hoebel & Lampert, Reference Hoebel and Lampert2018). Furthermore, our panel model design is unable to disentangle the between-person effects (our focus) and within-person effects, and we cannot rule out bias from time-varying confounders or shared method variance (Orth et al., Reference Orth, Clark, Donnellan and Robins2021). Future studies using alternative strategies, such as using random-intercept cross-lagged panel models (Hamaker et al., Reference Hamaker, Kuiper and Grasman2015) to investigate within-person mediation, or with multi-informant data, would provide further insight into the links between SES, social cognitions, and mental health during adolescence. Second, we were restricted to studying associations over a 1-year period, which may not have been long enough for some associations (e.g., between perceived family wealth and optimism) to evolve. Third, there were fairly low levels of mental health problems in the sample and two SDQ scales (peer problems and conduct problems) showed fairly low reliability. However, the SDQ has shown adequate psychometric properties in other studies with older adolescents and young adults, so it seems appropriate for assessing mental health in this sample (Brann et al., Reference Brann, Lethbridge and Mildred2018). Fourth, self-esteem was measured with a single-item scale, which may have lower test–retest reliability than multi-item measures of self-esteem, and be unable to distinguish between different dimensions of self-esteem (Donnellan et al., Reference Donnellan, Trzesniewski, Robins, Boyle, Saklofske and Matthews2015). However, the item has convergent correlation with the most widespread scale for measuring self-esteem and is therefore an appropriate brief instrument (Robins et al., Reference Robins, Hendin and Trzesniewski2001). Fifth, although this single-item measure of subjective SES shows similar associations with adolescent health outcomes as other measures (Quon & McGrath, Reference Quon and McGrath2014), research with additional indicators of SES measured repeatedly would provide further support for the robustness for our findings. Sixth, the representativeness of our sample was limited in that there was more attrition of adolescents with lower SES backgrounds and of those with a non-western migration background. Additionally, these results for adolescents in vocational education in the Netherlands may not generalize to adolescents in pre-university education or in other cultural contexts. For example, research in adults suggests social cognitions may play a different role in social gradients in health in more collectivistic countries (Kan et al., Reference Kan, Kawakami, Karasawa, Love, Coe, Miyamoto, Ryff, Kitayama, Curhan and Markus2014). Future work could explore the mediating role of social cognitions in late adolescence across several countries. Seventh, the study coincided with the Covid-19 pandemic, which began between T1 and T2. However, there is evidence that mental health effects of the pandemic were experienced by our entire sample, regardless of SES (Stevens et al., Reference Stevens, Buyukcan-Tetik, Maes, Weinberg, Vermeulen, Visser and Finkenauer2022); so, at this post-pandemic stage it remains unclear whether the pandemic influenced the generalizability of the findings.

Conclusion

Overall, by studying three mediating social cognitions, four adolescent mental health problems, and using longitudinal modeling, the results of this study illuminate the social gradient in adolescent mental health. In particular, adolescent’s sense of control appears to be an important mediator of this social gradient. Our results serve as a valuable starting point for further investigation into the role of adolescent social cognitions in the pathways between SES and adolescent mental health.

Funding statement

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

Conflicts of interest

None.

Footnotes

1 After submitting a preregistration, which described a cross-sectional study, it became possible to address the research questions more thoroughly using longitudinal data. The analytic plan described in this paper therefore differs somewhat from the plan in the preregistration.

2 A slightly different approach to calculating the sense of control scale was outlined in our preregistration. The change, made at the stage of examining descriptive statistics, was made to preserve scale values (whereas the approach outlined in the preregistration included standardization and thus did not).

References

Adler, N. E., & Tan, J. J. (2017). Commentary: Tackling the health gap: The role of psychosocial processes. International Journal of Epidemiology, 46(4), 13291331. https://doi.org/10.1093/ije/dyx167 CrossRefGoogle ScholarPubMed
Ahlin, E. M., & Antunes, M. J. L. (2015). Locus of control orientation: Parents, peers, and place. Journal of Youth and Adolescence, 44(9), 18031818. https://doi.org/10.1007/s10964-015-0253-9 CrossRefGoogle ScholarPubMed
Amnesty International (2021). Xenophobic machines: Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal (EUR 35/4686/2021). https://www.amnesty.org/en/documents/eur35/4686/2021/en/ Google Scholar
Antaramian, S. P., Huebner, E. S., Hills, K. J., & Valois, R. F. (2010). A dual-factor model of mental health: Toward a more comprehensive understanding of youth functioning. American Journal of Orthopsychiatry, 80(4), 462472. https://doi.org/10.1111/j.1939-0025.2010.01049.x CrossRefGoogle Scholar
Baldwin, J. R., & Degli Esposti, M. (2021). Triangulating evidence on the role of perceived versus objective experiences of childhood adversity in psychopathology. JCPP Advances, 1(1), e12010. https://doi.org/10.1111/jcv2.12010 CrossRefGoogle ScholarPubMed
Ben-Zur, H. (2003). Happy adolescents: The link between subjective well-being, internal resources, and parental factors. Journal of Youth and Adolescence, 32(2), 6779. https://doi.org/10.1023/A:1021864432505 CrossRefGoogle Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 57(1), 289300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x CrossRefGoogle Scholar
Blascovich, J., & Tomaka, J. (1991). Measures of self-esteem. In Robinson, J. P., Shaver, P. R., & Wrightsman, L. S. (Eds.), Measures of personality and social psychological attitudes (Vol. 1, pp. 115160). Academic Press.CrossRefGoogle Scholar
Boden, J. M., Sanders, J., Munford, R., Liebenberg, L., & McLeod, G. F. H. (2016). Paths to positive development: A model of outcomes in the New Zealand youth transitions study. Child Indicators Research, 9(4), 889911. https://doi.org/10.1007/s12187-015-9341-3 CrossRefGoogle Scholar
Boehm, J. K., Chen, Y., Williams, D. R., Ryff, C., & Kubzansky, L. D. (2015). Unequally distributed psychological assets: Are there social disparities in optimism, life satisfaction, and positive affect? PLOS ONE, 10(2), e0118066. https://doi.org/10.1371/journal.pone.0118066 CrossRefGoogle ScholarPubMed
Bosma, H., Simons, A., Groffen, D. A. I., & Klabbers, G. (2012). Stigmatization and socio-economic differences in health in modern welfare states. European Journal of Public Health, 22(5), 616617. https://doi.org/10.1093/eurpub/cks072 CrossRefGoogle ScholarPubMed
Bosma, H., Theunissen, M.-J., Verdonk, P., & Feron, F. (2014). Low control beliefs in relation to school dropout and poor health: Findings from the SIODO case-control study. BMC Public Health, 14, 1237. https://doi.org/10.1186/1471-2458-14-1237 CrossRefGoogle ScholarPubMed
Boylan, J. M., Cundiff, J. M., Jakubowski, K. P., Pardini, D. A., & Matthews, K. A. (2018). Pathways linking childhood SES and adult health behaviors and psychological resources in Black and White men. Annals of Behavioral Medicine, 52(12), 10231035. https://doi.org/10.1093/abm/kay006 CrossRefGoogle ScholarPubMed
Brailovskaia, J., & Margraf, J. (2020). How to measure self-esteem with one item? Validation of the German single-item self-esteem scale (G-SISE). Current Psychology, 39(6), 21922202. https://doi.org/10.1007/s12144-018-9911-x CrossRefGoogle Scholar
Brann, P., Lethbridge, M. J., & Mildred, H. (2018). The young adult Strengths and Difficulties Questionnaire (SDQ) in routine clinical practice. Psychiatry Research, 264, 340345. https://doi.org/10.1016/j.psychres.2018.03.001 CrossRefGoogle Scholar
Brown, B. B., & Larson, J. (2009). Peer relationships in adolescence. In Lerner, R. M., & Steinberg, L. (Eds.), Handbook of adolescent psychology (pp. 74103). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470479193.adlpsy002004 Google Scholar
Brumley, L. D., Jaffee, S. R., & Brumley, B. P. (2017). Pathways from childhood adversity to problem behaviors in young adulthood: The mediating role of adolescents’ future expectations. Journal of Youth and Adolescence, 46(1), 114. https://doi.org/10.1007/s10964-016-0597-9 CrossRefGoogle ScholarPubMed
Carver, C. S., Scheier, M. F., & Segerstrom, S. C. (2010). Optimism. Clinical Psychology Review, 30(7), 879889. https://doi.org/10.1016/j.cpr.2010.01.006 CrossRefGoogle ScholarPubMed
Chater, N., & Loewenstein, G. (2022). The i-frame and the s-frame: How focusing on individual-level solutions has led behavioral public policy astray. The Behavioral and Brain Sciences, 160. https://doi.org/10.1017/S0140525X22002023 Google ScholarPubMed
Chen, B., Luo, L., Wu, X., Chen, Y., & Zhao, Y. (2021). Are the lower class really unhappy? Social class and subjective well-being in Chinese adolescents: Moderating role of sense of control and mediating role of self-esteem. Journal of Happiness Studies, 22(2), 825843. https://doi.org/10.1007/s10902-020-00253-5 CrossRefGoogle Scholar
Chen, E., Matthews, K. A., & Boyce, W. T. (2002). Socioeconomic differences in children’s health: How and why do these relationships change with age? Psychological Bulletin, 128(2), 295329. https://doi.org/10.1037//0033-2909.128.2.295 CrossRefGoogle ScholarPubMed
Chorpita, B. F., & Barlow, D. H. (1998). The development of anxiety: The role of control in the early environment. Psychological Bulletin, 124(1), 321. https://doi.org/10.1037/0033-2909.124.1.3 CrossRefGoogle ScholarPubMed
Ciarrochi, J., Heaven, P. C. L., & Davies, F. (2007). The impact of hope, self-esteem, and attributional style on adolescents’ school grades and emotional well-being: A longitudinal study. Journal of Research in Personality, 41(6), 11611178. https://doi.org/10.1016/j.jrp.2007.02.001 CrossRefGoogle Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155159. https://doi.org/10.1037//0033-2909.112.1.155 CrossRefGoogle ScholarPubMed
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310357. https://doi.org/10.1037/0033-2909.98.2.310 CrossRefGoogle ScholarPubMed
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), 558577. https://doi.org/10.1037/0021-843X.112.4.558 CrossRefGoogle ScholarPubMed
Collin-Vézina, D., Fallon, B., & Caldwell, J. (2022). Children and youth mental health: Not all equal in the face of the COVID-19 pandemic. Reference Module in Neuroscience and Biobehavioral Psychology. https://doi.org/10.1016/B978-0-323-91497-0.00072-2 Google Scholar
Conger, K. J., Williams, S. T., Little, W. M., Masyn, K. E., & Shebloski, B. (2009). Development of mastery during adolescence: The role of family problem-solving. Journal of Health and Social Behavior, 50(1), 99114. https://doi.org/10.1177/002214650905000107 CrossRefGoogle ScholarPubMed
Conger, R. D., & Donnellan, M. B. (2007). An interactionist perspective on the socioeconomic context of human development. Annual Review of Psychology, 58(1), 175199. https://doi.org/10.1146/annurev.psych.58.110405.085551 CrossRefGoogle ScholarPubMed
Crone, E. A., & Dahl, R. E. (2012). Understanding adolescence as a period of social-affective engagement and goal flexibility. Nature Reviews Neuroscience, 13(9), Article 9. https://doi.org/10.1038/nrn3313 CrossRefGoogle ScholarPubMed
Culpin, I., Stapinski, L., Miles, Ö.B., Araya, R., & Joinson, C. (2015). Exposure to socioeconomic adversity in early life and risk of depression at 18 years: The mediating role of locus of control. Journal of Affective Disorders, 183, 269278. https://doi.org/10.1016/j.jad.2015.05.030 CrossRefGoogle ScholarPubMed
Cummins, R. A., & Nistico, H. (2002). Maintaining life satisfaction: The role of positive cognitive bias. Journal of Happiness Studies, 3(1), 3769.CrossRefGoogle Scholar
Devenish, B., Hooley, M., & Mellor, D. (2017). The pathways between socioeconomic status and adolescent outcomes: A systematic review. American Journal of Community Psychology, 59(1-2), 219238. https://doi.org/10.1002/ajcp.12115 CrossRefGoogle ScholarPubMed
Donnellan, M. B., Trzesniewski, K. H., & Robins, R. W. (2015). Measures of self-esteem. In Boyle, G. J., Saklofske, D. H., & Matthews, G. (Eds.), Measures of personality and social psychological constructs (pp. 131157). Academic Press. https://doi.org/10.1016/B978-0-12-386915-9.00006-1 CrossRefGoogle Scholar
Donnellan, M. B., Trzesniewski, K. H., Robins, R. W., Moffitt, T. E., & Caspi, A. (2005). Low self-esteem is related to aggression, antisocial behavior, and delinquency. Psychological Science, 16(4), 328335. https://doi.org/10.1111/j.0956-7976.2005.01535.x CrossRefGoogle ScholarPubMed
Duinhof, E. L., Lek, K. M., de Looze, M. E., Cosma, A., Mazur, J., Gobina, I., Wüstner, A., Vollebergh, W. A. M., & Stevens, G. W. J. M. (2019). Revising the self-report Strengths and Difficulties Questionnaire for cross-country comparisons of adolescent mental health problems: The SDQ-R. Epidemiology and Psychiatric Sciences, 29(e35), 110. https://doi.org/10.1017/S2045796019000246 Google ScholarPubMed
Duinhof, E. L., Smid, S. C., Vollebergh, W. A. M., & Stevens, G. W. J. M. (2020). Immigration background and adolescent mental health problems: The role of family affluence, adolescent educational level and gender. Social Psychiatry and Psychiatric Epidemiology, 55(4), 435445. https://doi.org/10.1007/s00127-019-01821-8 CrossRefGoogle ScholarPubMed
Elgar, F. J., Xie, A., Pförtner, T.-K., White, J., & Pickett, K. E. (2017). Assessing the view from bottom: How to measure socioeconomic position and relative deprivation in adolescents. SAGE Publications Ltd. https://doi.org/10.4135/9781526406347 CrossRefGoogle Scholar
Falci, C. D. (2011). Self-esteem and mastery trajectories in high school by social class and gender. Social Science Research, 40(2), 586601. https://doi.org/10.1016/j.ssresearch.2010.12.013 CrossRefGoogle ScholarPubMed
Fiske, S. T., & Taylor, S. E. (2013). Social cognition: From brains to culture. SAGE.CrossRefGoogle Scholar
Flanagan, C. A., Kim, T., Pykett, A., Finlay, A., Gallay, E. E., & Pancer, M. (2014). Adolescents’ theories about economic inequality: Why are some people poor while others are rich? Developmental Psychology, 50(11), 25122525. https://doi.org/10.1037/a0037934 CrossRefGoogle ScholarPubMed
Frankenhuis, W. E., & Nettle, D. (2020). The strengths of people in poverty. Current Directions in Psychological Science, 29(1), 1621. https://doi.org/10.1177/0963721419881154 CrossRefGoogle Scholar
Freire, P. (1993). Pedagogy of the oppressed (M. B. Ramos, Trans.; 20th-Anniversary ed.). Continuum. (Original work published 1970).Google Scholar
Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research, and Evaluation, 17(1), 113. https://doi.org/10.7275/n560-j767 Google Scholar
Gallo, L. C., & Matthews, K. A. (2003). Understanding the association between socioeconomic status and physical health: Do negative emotions play a role? Psychological Bulletin, 129(1), 1051. https://doi.org/10.1037/0033-2909.129.1.10 CrossRefGoogle ScholarPubMed
Gecas, V. (1982). The self-concept. Annual Review of Sociology, 8(1), 133. https://doi.org/10.1146/annurev.so.08.080182.000245 CrossRefGoogle Scholar
Goodman, A., Lamping, D. L., & Ploubidis, G. B. (2010). When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): Data from British parents, teachers and children. Journal of Abnormal Child Psychology, 38(8), 11791191. https://doi.org/10.1007/s10802-010-9434-x CrossRefGoogle ScholarPubMed
Goodman, E., Huang, B., Schafer-Kalkhoff, T., & Adler, N. E. (2007). Perceived socioeconomic status: A new type of identity that influences adolescents’ self-rated health. Journal of Adolescent Health, 41(5), 479487. https://doi.org/10.1016/j.jadohealth.2007.05.020 CrossRefGoogle ScholarPubMed
Goodman, R. (1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581586. https://doi.org/10.1111/j.1469-7610.1997.tb01545.x CrossRefGoogle ScholarPubMed
Goyer, J. P., Garcia, J., Purdie-Vaughns, V., Binning, K. R., Cook, J. E., Reeves, S. L., Apfel, N., Taborsky-Barba, S., Sherman, D. K., & Cohen, G. L. (2017). Self-affirmation facilitates minority middle schoolers’ progress along college trajectories. Proceedings of the National Academy of Sciences, 114(29), 75947599. https://doi.org/10.1073/pnas.1617923114 CrossRefGoogle ScholarPubMed
Hagquist, C. (2007). Health inequalities among adolescents—The impact of academic orientation and parents’ education. European Journal of Public Health, 17(1), 2126. https://doi.org/10.1093/eurpub/ckl087 CrossRefGoogle ScholarPubMed
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102116. https://doi.org/10.1037/a0038889 CrossRefGoogle ScholarPubMed
Heberle, A. E., & Carter, A. S. (2015). Cognitive aspects of young children’s experience of economic disadvantage. Psychological Bulletin, 141(4), 723746. https://doi.org/10.1037/bul0000010 CrossRefGoogle ScholarPubMed
Hektner, J. M. (1995). When moving up implies moving out: Rural adolescent conflict in the transition to adulthood. Journal of Research in Rural Education, 11(1), 314.Google Scholar
Hitlin, S., Erickson, L. D., & Brown, J. S. (2015). Agency and mental health: A transition to adulthood paradox. Society and Mental Health, 5(3), 163181. https://doi.org/10.1177/2156869315573632 CrossRefGoogle Scholar
Hitlin, S., & Johnson, M. K. (2015). Reconceptualizing agency within the life course: The power of looking ahead. American Journal of Sociology, 120(5), 14291472. https://doi.org/10.1086/681216 CrossRefGoogle ScholarPubMed
Hoebel, J., & Lampert, T. (2018). Subjective social status and health: Multidisciplinary explanations and methodological challenges. Journal of Health Psychology, 25(2), 113. https://doi.org/10.1177/1359105318800804 Google ScholarPubMed
Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). Routledge, Taylor & Francis.CrossRefGoogle Scholar
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 155. https://doi.org/10.1080/10705519909540118 CrossRefGoogle Scholar
Inchley, J., Currie, D., Cosma, A., Piper, A., & Spanou, G. (Eds.) (2017). Health Behaviour in School-aged Children (HBSC) Internal Protocol 2017/18. CAHRU. http://www.hbsc.org/methods/index.html Google Scholar
Jacobs, J. E., Bleeker, M. M., & Constantino, M. J. (2003). The self-system during childhood and adolescence: Development, influences, and implications. Journal of Psychotherapy Integration, 13(1), 3365. https://doi.org/10.1037/1053-0479.13.1.33 CrossRefGoogle Scholar
Jung, J., Krahé, B., & Busching, R. (2018). Beyond the positive reinforcement of aggression: Peers’ acceptance of aggression promotes aggression via external control beliefs. International Journal of Behavioral Development, 42(1), 7382. https://doi.org/10.1177/0165025416671613 CrossRefGoogle Scholar
Kan, C., Kawakami, N., Karasawa, M., Love, G. D., Coe, C. L., Miyamoto, Y., Ryff, C. D., Kitayama, S., Curhan, K. B., & Markus, H. R. (2014). Psychological resources as mediators of the association between social class and health: Comparative findings from Japan and the USA. International Journal of Behavioral Medicine, 21(1), 5365. https://doi.org/10.1007/s12529-012-9249-y CrossRefGoogle ScholarPubMed
Kauhanen, L., Wan Mohd Yunus, W. M. A., Lempinen, L., Peltonen, K., Gyllenberg, D., Mishina, K., Gilbert, S., Bastola, K., Brown, J. S. L., & Sourander, A. (2022). A systematic review of the mental health changes of children and young people before and during the COVID-19 pandemic. European Child & Adolescent Psychiatry. https://doi.org/10.1007/s00787-022-02060-0 Google Scholar
Kim, D. H., Bassett, S. M., So, S., & Voisin, D. R. (2019). Family stress and youth mental health problems: Self-efficacy and future orientation mediation. American Journal of Orthopsychiatry, 89(2), 125133. https://doi.org/10.1037/ort0000371 CrossRefGoogle ScholarPubMed
Kraus, M. W., Piff, P. K., Mendoza-Denton, R., Rheinschmidt, M. L., & Keltner, D. (2012). Social class, solipsism, and contextualism: How the rich are different from the poor. Psychological Review, 119(3), 546572. https://doi.org/10.1037/a0028756 CrossRefGoogle ScholarPubMed
Lachman, M. E., & Agrigoroaei, S. (2010). Promoting functional health in midlife and old age: Long-term protective effects of control beliefs, social support, and physical exercise. PLOS ONE, 5(10), e13297. https://doi.org/10.1371/journal.pone.0013297 CrossRefGoogle ScholarPubMed
Lachman, M. E., & Weaver, S. L. (1998). The sense of control as a moderator of social class differences in health and well-being. Journal of Personality and Social Psychology, 74(3), 763773. https://doi.org/10.1037//0022-3514.74.3.763 CrossRefGoogle ScholarPubMed
Lareau, A. (2003). Unequal childhoods: Class, race, and family life. University of California Press.Google Scholar
Liebenberg, L., Sanders, J., Munford, R., & Thimasarn-Anwar, T. (2015). Validation of the Hektner future emotions questions as a scale for use with youth in New Zealand. Child Indicators Research, 8(3), 641655. https://doi.org/10.1007/s12187-014-9269-z CrossRefGoogle Scholar
Luthar, S. S., Kumar, N. L., & Zillmer, N. (2020). High-achieving schools connote risks for adolescents: Problems documented, processes implicated, and directions for interventions. American Psychologist, 75(7), 983995. https://doi.org/10.1037/amp0000556 CrossRefGoogle ScholarPubMed
Mackenbach, J. P., Meerding, W. J., & Kunst, A. E. (2011). Economic costs of health inequalities in the European Union. Journal of Epidemiology and Community Health, 65(5), 412419. https://doi.org/10.1136/jech.2010.112680 CrossRefGoogle ScholarPubMed
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58(1), 593614. https://doi.org/10.1146/annurev.psych.58.110405.085542 CrossRefGoogle ScholarPubMed
Mann, M., Hosman, C. M. H., Schaalma, H. P., & de Vries, N. K. (2004). Self-esteem in a broad-spectrum approach for mental health promotion. Health Education Research, 19(4), 357372. https://doi.org/10.1093/her/cyg041 CrossRefGoogle Scholar
Marmot, M. (2004). Status syndrome: How your social standing directly affects your health. Bloomsbury.CrossRefGoogle Scholar
Masselink, M., Van Roekel, E., & Oldehinkel, A. J. (2018). Self-esteem in early adolescence as predictor of depressive symptoms in late adolescence and early adulthood: The mediating role of motivational and social factors. Journal of Youth and Adolescence, 47(5), 932946. https://doi.org/10.1007/s10964-017-0727-z CrossRefGoogle ScholarPubMed
McLoyd, V. C., Kaplan, R., Purtell, K. M., Bagley, E., Hardaway, C. R., & Smalls, C. (2009). Poverty and socioeconomic disadvantage in adolescence. In Lerner, R. M., & Steinberg, L. (Eds.), Handbook of adolescent psychology (pp. 444491). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470479193.adlpsy002014 Google Scholar
McNeish, D., Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22(1), 114140. https://doi.org/10.1037/met0000078 CrossRefGoogle ScholarPubMed
McWhirter, E. H., & McWhirter, B. T. (2008). Adolescent future expectations of work, education, family, and community: Development of a new measure. Youth & Society, 40(2), 182202. https://doi.org/10.1177/0044118X08314257 CrossRefGoogle Scholar
Menon, M., Tobin, D. D., Corby, B. C., Menon, M., Hodges, E. V. E., & Perry, D. G. (2007). The developmental costs of high self-esteem for antisocial children. Child Development, 78(6), 16271639. https://doi.org/10.1111/j.1467-8624.2007.01089.x CrossRefGoogle ScholarPubMed
Moreno-Maldonado, C., Rivera, F., Ramos, P., & Moreno, C. (2018). Measuring the socioeconomic position of adolescents: A proposal for a composite index. Social Indicators Research, 136(2), 517538. https://doi.org/10.1007/s11205-017-1567-7 CrossRefGoogle Scholar
Myers, D. G., & Diener, E. (1995). Who is happy? Psychological Science, 6(1), 1019.CrossRefGoogle Scholar
Orth, U., Clark, D. A., Donnellan, M. B., & Robins, R. W. (2021). Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. Journal of Personality and Social Psychology, 120(4), 10131034. https://doi.org/10.1037/pspp0000358 CrossRefGoogle ScholarPubMed
Orth, U., Robins, R. W., & Roberts, B. W. (2008). Low self-esteem prospectively predicts depression in adolescence and young adulthood. Journal of Personality and Social Psychology, 95(3), 695708. https://doi.org/10.1037/0022-3514.95.3.695 CrossRefGoogle ScholarPubMed
Orth, U., Robins, R. W., & Widaman, K. F. (2012). Life-span development of self-esteem and its effects on important life outcomes. Journal of Personality and Social Psychology, 102(6), 12711288. https://doi.org/10.1037/a0025558 CrossRefGoogle ScholarPubMed
Orton, L. C., Pennington, A., Nayak, S., Sowden, A., Petticrew, M., White, M., & Whitehead, M. (2019). What is the evidence that differences in control over destiny lead to socioeconomic inequalities in health? A theory-led systematic review of high-quality longitudinal studies on pathways in the living environment. Journal of Epidemiology and Community Health, 73(10), 929934. https://doi.org/10.1136/jech-2019-212565 CrossRefGoogle ScholarPubMed
Patton, G. C., Sawyer, S. M., Santelli, J. S., Ross, D. A., Afifi, R., Allen, N. B., Arora, M., Azzopardi, P., Baldwin, W., Bonell, C., Kakuma, R., Kennedy, E., Mahon, J., McGovern, T., Mokdad, A. H., Patel, V., Petroni, S., Reavley, N., Taiwo, K., …Viner, R. M. (2016). Our future: A Lancet commission on adolescent health and wellbeing. The Lancet, 387(10036), 24232478. https://doi.org/10.1016/S0140-6736(16)00579-1 CrossRefGoogle ScholarPubMed
Patton, G. C., Tollit, M. M., Romaniuk, H., Spence, S. H., Sheffield, J., & Sawyer, M. G. (2011). A prospective study of the effects of optimism on adolescent health risks. Pediatrics, 127(2), 308316. https://doi.org/10.1542/peds.2010-0748 CrossRefGoogle ScholarPubMed
Pearce, A., Dundas, R., Whitehead, M., & Taylor-Robinson, D. (2019). Pathways to inequalities in child health. Archives of Disease in Childhood, 104(10), 9981003. https://doi.org/10.1136/archdischild-2018-314808 CrossRefGoogle ScholarPubMed
Pepper, G. V., & Nettle, D. (2017). The behavioural constellation of deprivation: Causes and consequences. Behavioral and Brain Sciences, 40(e314), 166. https://doi.org/10.1017/S0140525X1600234X CrossRefGoogle ScholarPubMed
Phillips, N. L., Adams, G., & Salter, P. S. (2015). Beyond adaptation: Decolonizing approaches to coping with oppression. Journal of Social and Political Psychology, 3(1), Article 1. https://doi.org/10.5964/jspp.v3i1.310 CrossRefGoogle Scholar
Piff, P. K., Kraus, M. W., & Keltner, D. (2017). Unpacking the inequality paradox: The psychological roots of inequality and social class. In Olson, J. M. (Ed.), Advances in experimental social psychology (Vol. 57, pp. 53124). Academic Press. https://doi.org/10.1016/bs.aesp.2017.10.002 Google Scholar
Piko, B. F., Luszczynska, A., & Fitzpatrick, K. M. (2013). Social inequalities in adolescent depression: The role of parental social support and optimism. International Journal of Social Psychiatry, 59(5), 474481. https://doi.org/10.1177/0020764012440788 CrossRefGoogle ScholarPubMed
Pollak, S. D., & Smith, K. E. (2021). Thinking clearly about biology and childhood adversity: Next steps for continued progress. Perspectives on Psychological Science, 16(6), 14731477. https://doi.org/10.1177/17456916211031539 CrossRefGoogle ScholarPubMed
Prinstein, M. J. (2017). Popular: The power of likability in a status-obsessed world. Viking.Google Scholar
Quon, E. C., & McGrath, J. J. (2014). Subjective socioeconomic status and adolescent health: A meta-analysis. Health Psychology, 33(5), 433447. https://doi.org/10.1037/a0033716 CrossRefGoogle ScholarPubMed
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ Google Scholar
Rahal, D., Huynh, V., Cole, S., Seeman, T., & Fuligni, A. (2020). Subjective social status and health during high school and young adulthood. Developmental Psychology, 56(6), 12201232. https://doi.org/10.1037/dev0000919 CrossRefGoogle ScholarPubMed
Reiss, F. (2013). Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Social Science & Medicine, 90, 2431. https://doi.org/10.1016/j.socscimed.2013.04.026 CrossRefGoogle ScholarPubMed
Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001). Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Personality and Social Psychology Bulletin, 27(2), 151161. https://doi.org/10.1177/0146167201272002 CrossRefGoogle Scholar
Rosenberg, M., & Pearlin, L. I. (1978). Social class and self-esteem among children and adults. American Journal of Sociology, 84(1), 5377. https://doi.org/10.1086/226740 CrossRefGoogle Scholar
Ross, L. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. In Berkowitz, L. (Ed.), Advances in experimental social psychology (Vol. 10, pp. 173220). Elsevier.Google Scholar
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5-12 (BETA). Journal of Statistical Software, 48(2), 136. https://doi.org/10.18637/jss.v048.i02 CrossRefGoogle Scholar
Schoon, I., & Lyons-Amos, M. (2017). A socio-ecological model of agency: The role of psycho-social and socioeconomic resources in shaping education and employment transitions in England. Longitudinal and Life Course Studies, 8(1), 3556. https://doi.org/10.14301/llcs.v8i1.404 CrossRefGoogle Scholar
Sheehy-Skeffington, J. (2020). The effects of low socioeconomic status on decision-making processes. Current Opinion in Psychology, 33, 183188. https://doi.org/10.1016/j.copsyc.2019.07.043 CrossRefGoogle ScholarPubMed
Shifrer, D. (2019). The contributions of parental, academic, school, and peer factors to differences by socioeconomic status in adolescents’ locus of control. Society and Mental Health, 9(1), 7494. https://doi.org/10.1177/2156869318754321 CrossRefGoogle ScholarPubMed
Singh-Manoux, A., Marmot, M. G., & Adler, N. E. (2005). Does subjective social status predict health and change in health status better than objective status? Psychosomatic Medicine, 67(6), 855861. https://doi.org/10.1097/01.psy.0000188434.52941.a0 CrossRefGoogle ScholarPubMed
Statistics Netherlands, CBS (2022). Population; sex, age, generation and migration background, 1 January [filters: Age (15-20); Period (2019)]. CBS. https://opendata.cbs.nl/statline/#/CBS/en/dataset/37325eng/table?dl=6FAAF Google Scholar
Stephens, N. M., Markus, H. R., & Phillips, L. T. (2014). Social class culture cycles: How three gateway contexts shape selves and fuel inequality. Annual Review of Psychology, 65(1), 611634. https://doi.org/10.1146/annurev-psych-010213-115143 CrossRefGoogle ScholarPubMed
Stevens, G. W. J. M., Buyukcan-Tetik, A., Maes, M., Weinberg, D., Vermeulen, S., Visser, K., & Finkenauer, C. (2022). Examining socioeconomic disparities in changes in adolescent mental health before and during different phases of the COVID-19 pandemic. Stress and Health, 39(1), 169181. https://doi.org/10.1002/smi.3179 CrossRefGoogle Scholar
Sullivan, S. A., Thompson, A., Kounali, D., Lewis, G., & Zammit, S. (2017). The longitudinal association between external locus of control, social cognition and adolescent psychopathology. Social Psychiatry and Psychiatric Epidemiology, 52(6), 643655. https://doi.org/10.1007/s00127-017-1359-z CrossRefGoogle ScholarPubMed
Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103(2), 193210. https://doi.org/10.1037/0033-2909.103.2.193 CrossRefGoogle ScholarPubMed
Torsheim, T., Cavallo, F., Levin, K. A., Schnohr, C., Mazur, J., Niclasen, B., Currie, C., & The FAS Development Study Group (2016). Psychometric validation of the revised Family Affluence Scale: A latent variable approach. Child Indicators Research, 9(3), 771784. https://doi.org/10.1007/s12187-015-9339-x CrossRefGoogle Scholar
Twenge, J. M., & Campbell, W. K. (2002). Self-esteem and socioeconomic status: A meta-analytic review. Personality and Social Psychology Review, 6(1), 5971. https://doi.org/10.1207/S15327957PSPR0601_3 CrossRefGoogle Scholar
Vigo, D., Thornicroft, G., & Atun, R. (2016). Estimating the true global burden of mental illness. The Lancet Psychiatry, 3(2), 171178. https://doi.org/10.1016/S2215-0366(15)00505-2 CrossRefGoogle ScholarPubMed
Weinberg, D., Stevens, G. W. J. M., Duinhof, E. L., & Finkenauer, C. (2019). Adolescent socioeconomic status and mental health inequalities in the Netherlands, 2001-2017. International Journal of Environmental Research and Public Health, 16(19), 3605. https://doi.org/10.3390/ijerph16193605 CrossRefGoogle ScholarPubMed
Wheaton, B. (1980). The sociogenesis of psychological disorder: An attributional theory. Journal of Health and Social Behavior, 21(2), 100124. https://doi.org/10.2307/2136730 CrossRefGoogle ScholarPubMed
Whitehead, M., Pennington, A., Orton, L., Nayak, S., Petticrew, M., Sowden, A., & White, M. (2016). How could differences in control over destiny lead to socio-economic inequalities in health? A synthesis of theories and pathways in the living environment. Health & Place, 39, 5161. https://doi.org/10.1016/j.healthplace.2016.02.002 CrossRefGoogle ScholarPubMed
Yan, W., Yang, K., Wang, Q., You, X., & Kong, F. (2020). Subjective family socioeconomic status and life satisfaction in Chinese adolescents: The mediating role of self-esteem and social support. Youth & Society, 53(7), 119. https://doi.org/10.1177/0044118X20941344 Google Scholar
Yeager, D. S., Dahl, R. E., & Dweck, C. S. (2018). Why interventions to influence adolescent behavior often fail but could succeed. Perspectives on Psychological Science, 13(1), 101122. https://doi.org/10.1177/1745691617722620 CrossRefGoogle ScholarPubMed
Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 6991. https://doi.org/10.1080/1361332052000341006 CrossRefGoogle Scholar
Zou, R., Xu, X., Hong, X., & Yuan, J. (2020). Higher socioeconomic status predicts less risk of depression in adolescence: Serial mediating roles of social support and optimism. Frontiers in Psychology, 11, 1955. https://doi.org/10.3389/fpsyg.2020.01955 CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. Model showing associations between confounders, perceived family wealth, and adolescent mental health problems (Model 1). Notes. Standardised coefficients. Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05), not shown. Only significant coefficients for confounders, and associations between perceived family wealth at T1 and mental health at T1 and T3 (1 year later), are shown. This model was the basis for Models 2a–d. Model fit 1 – χ2 (111) = 365.8, p < 0.001, CFI = 0.944, RMSEA = 0.042. aReference category: girl. bReference category: Dutch/western. *p < .05. **p < .01.

Figure 1

Figure 2. The association between SES and adolescent mental health problems mediated by self-esteem (Models 2a and 2d). Notes. Standardized coefficients (same values constrained to equality may differ slightly after standardization). The first coefficient indicates Model 2a result (self-esteem only), the second coefficient indicates the Model 2d result (all three social cognitions included in the model). Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05). Dashed thick lines indicate significance of path differs between Models 2a and 2d. All paths were estimated in the same models, but results are presented in four panels (i.e., for each mental health outcome) for clarity. Associations with confounders and covariances between mental health problems are not shown. Key variables in the hypothesized mediation path are highlighted with a double border. Model fit 2a – χ2 (150) = 455.9, p < 0.001, CFI = 0.948, RMSEA = 0.040. Model fit 2d – χ2 (249) = 697.7, p < 0.001, CFI = 0.946, RMSEA = 0.037. *p < .05. **p < .01.

Figure 2

Figure 3. The association between SES and adolescent mental health problems mediated by sense of control (Models 2b and 2d). Notes. Standardized coefficients (same values constrained to equality may differ slightly after standardization). The first coefficient indicates Model 2b result (sense of control only), the second coefficient indicates the Model 2d result (all three social cognitions included in the model). Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05). Dashed thick lines indicate significance of path differs between Models 2b and 2d. All paths were estimated in the same models, but results are presented in four panels (i.e., for each mental health outcome) for clarity. Associations with confounders and covariances between mental health problems are not shown. Key variables in hypothesized mediation path highlighted with double border. Model fit 2b – χ2 (150) = 443.3, p < 0.001, CFI = 0.947, RMSEA = 0.039. Model fit 2d – χ2 (249) = 697.7, p < 0.001, CFI = 0.946, RMSEA = 0.037. *p < .05. **p < .01.

Figure 3

Figure 4. The association between SES and adolescent mental health problems mediated by optimism (Models 2c and 2d). Notes. Standardized coefficients (same values constrained to equality may differ slightly after standardization). The first coefficient indicates Model 2c result (optimism only), the second coefficient indicates the Model 2d result (all three social cognitions included in the model). Continuous thick lines indicate significant paths (p < 0.05); dashed thin lines indicate insignificant paths (p > 0.05). Dashed thick lines indicate significance of path differs between Models 2c and 2d. All paths were estimated in the same models, but results are presented in four panels (i.e., for each mental health outcome) for clarity. Associations with confounders and covariances between mental health problems are not shown. Key variables in hypothesized mediation path highlighted with double border. Model fit 2c – χ2 (150) = 430.3, p < 0.001, CFI = 0.948, RMSEA = 0.038. Model fit 2d – χ2 (249) = 697.7, p < 0.001, CFI = 0.946, RMSEA = 0.037. *p < .05. **p < .01.

Figure 4

Table 1. Descriptive statistics (means, standard deviations, ranges, ns, and correlations) for study variables