stress processes and trajectories of depressive symptoms in early life: gendered development

STRESS PROCESSES AND

TRAJECTORIES OF DEPRESSIVE

SYMPTOMS IN EARLY LIFE:

GENDERED DEVELOPMENT$

Daniel E. Adkins, Victor Wang and Glen H. Elder Jr.

ABSTRACT

Despite considerable advances, significant gaps remain in our knowledgeof how gender differences in depression develop over the life course.Applying mixed model growth curves to the National Longitudinal Surveyof Adolescent Health, this study investigates gendered variation in thecauses and course of depressive symptom trajectories across early life.Results show curvilinear trajectories, rising through adolescence, andfalling in young adulthood, with female disadvantage persistent, butnarrowing over time. The effects of stressful life events (SLEs) and social

$We are grateful to Amanda Byrd for comments and suggestions that contributed significantly

to this chapter. This research uses data from the Add Health Study designed by J. Richard

Udry, Peter S. Bearman, and Kathleen Mullan Harris and the Add Health Wave IV Program

Project directed by Kathleen Mullan Harris (Grant 3P01 HD031921), funded by the National

Institute of Child Health and Human Development with cooperative funding from 17 other

agencies. We gratefully acknowledge support from NICHD to Glen H. Elder, Jr. and Michael

J. Shanahan through their subproject to the Add Health Wave IV Program Project (Grant 3P01

HD031921).

Stress Processes across the Life Course

Advances in Life Course Research, Volume 13, 107–136

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1040-2608/doi:10.1016/S1040-2608(08)00005-1

107

DANIEL E. ADKINS ET AL.108

support on depressive symptoms are notably larger for females. Overall,results indicate that stress processes contributing to depression are highlygendered in early life with females generally experiencing higher levelsof depressive symptoms and showing greater sensitivity to both thedetrimental effects of SLEs and the buffering effect of social support.

The significant gender difference in depression among adults is one of themost robust findings in the mental health literature (Nolen-Hoeksema, 1990).Rates of depression are approximately two times higher among women thanmen cross-culturally, regardless of the diagnostic scheme or interview method(Culbertson, 1997). Research indicates that this gender differential emerges inearly adolescence (e.g., Allgood-Merten, Lewinsohn, & Hops, 1990; Angold,Costello, & Worthman, 1998), with approximately half of age 15 adolescentgirls experiencing weekly depressive symptoms compared to only a third oftheir male peers (Scheidt, Overpeck, Wyatt, & Aszmann, 2000).

While scholars now have a fairly clear conception of gender differences indepression across adolescence and young adulthood, questions remainregarding gendered variation in causes of depression during this importantdevelopmental period. Indeed, gender differences in exposure and sensitivityto well-known predictors of depression are still debated for both adolescentand adult populations. For instance, gender variation in the influence of stressremains a contested topic, with researchers variously arguing for the influenceof differential exposure and vulnerability. Thus, one prominent perspectivesuggests that women’s social roles expose them to more stress than men,asserting that women, like individuals with low socioeconomic status (SES),are often situated in social roles where they are expected to perform lessdesirable tasks with little recognition or reward (Turner & Lloyd, 1999;Turner & Avison, 1989). Another related perspective holds that regardless ofgender differences in exposure, women are more vulnerable to the negativeeffects of stress on mental health (Ge, Lorenz, Conger, Elder, & Simons, 1994;Kessler, 1979). In response to this perspective, Aneshensel, Rutter, andLachenbrach (1991) and others have argued that gender differences in stressreactivity are disorder specific, with women tending toward internalizing andmen predisposed to externalizing reactions (Hagan & Foster, 2003). Evenless is known regarding early life gender heterogeneity in the effects of otherwell-established predictors such as social support and SES.

This study addresses these gaps in knowledge, employing the stress processframework to investigate gendered variation in the antecedents of andchanges in depressive symptoms across early life. Over the past 25 years, the

Stress Processes and Trajectories of Depressive Symptoms in Early Life 109

stress process has become the central sociological paradigm in explainingadult mental health disparities (see Pearlin, Menaghan, Lieberman, &mullan, 1981; Thoits, 1991; Turner & Lloyd, 1999). Stress process models areinstructive from a developmental perspective because they elucidate the earlysocial structuring of adversity and privilege that ultimately shape long-termpatterns of psychological well-being. Indeed, numerous studies show thatchildhood SES influences mental health in early life (McLeod & Shanahan,1993; Costello, Compton, Keeler, & Angold, 2003 [see Case 2004]), andthough stressful life events (SLEs) are also known to be influential (e.g., Geet al., 1994; Ge, Conger, & Elder, 2001; Ge, Natsuaki, & Conger, 2006;Meadows, Brown, & Elder, 2006), it is less clear how SES and SLEs exerttheir effects longitudinally. Recent research has sought to address thislimitation by examining ‘‘depression trajectories’’ to better understand thedevelopment of negative affect along with its social etiology (e.g., Ge et al.,1994, 2006). Despite considerable advances, research has yet to fullyintegrate the principal components of the stress process with the life courseconstruct of a depression trajectory.

To investigate gendered variation in the trajectories of depressivesymptoms across early life, this study employs Add Health, the largestnationally representative panel study of U.S. adolescents and young adults.This research is one of the first longitudinal analyses to test the majorcomponents of the stress process on age-based trajectories of early lifedepression by gender. The inquiry is guided by several key questions. First,how do patterns of depressive symptoms differ by gender as adolescentstransition to young adulthood? Second, to what extent, and throughwhat mechanisms, does the stress process function differently betweengenders? Specifically, do the various components of childhood SES haveequal impacts on depressive symptoms in both males and females? Aregender differences in depressive symptom trajectories related to differentialexposure and/or sensitivity to SLEs and social support? We conclude bydiscussing the implications of our findings for future research.

NORMATIVE DEVELOPMENT AND

DEPRESSIVE SYMPTOMS

Though relatively few trajectory analyses of the development of depressionduring adolescence and young adulthood have been conducted, there ismounting evidence, from both cross-sectional and longitudinal studies, of


a normative curvilinear course of depressive symptoms through early life.This conclusion is supported by longitudinal research finding curvilineartrajectories in samples of individuals moving through adolescence and youngadulthood, as well as by research in younger samples showing linear increasethrough middle adolescence and studies of young adult samples showinglinear decrease or stability through the twenties. For instance, analyzing 11waves of longitudinal data covering ages 12–23, Ge et al. (2006) foundcurvilinear trajectories of depressive symptoms, rising in early and middleadolescence and declining in late adolescence. Likewise, Wight, Sepulveda,and Aneshensel (2004) examined depressive symptoms in three datasets(one adolescent sample and two adult samples) and found increasing levels inthe adolescent sample, while the adult samples showed both lower initiallevels and a steady decline over time. Similar findings have been found inseveral other analyses (e.g., Wade, Cairney, & Pevalin, 2002; Hankin et al.,1998; Ge et al., 1994) and considered collectively, this literature offers strongsupport of an inverted-U curvilinear trajectory of depressive symptoms acrossadolescence and young adulthood for both genders.

In addition to investigating the overall course of depressive symptomsacross early life, researchers have also examined gender differentials across thisperiod. Following early cross-sectional findings indicate that female disadvan-tage in depression emerges in early adolescence (e.g., Allgood-Merton et al.,1990; Nolen-Hoeksema, 1990), Ge and colleagues (1994) were among the firstto apply the trajectory methods to examine the emergence of gender disparityin depression, tracing the origin of the disparity to ages 13–15. This finding hasproven robust over the years, garnering support from numerous, methodo-logically diverse studies (see Hankin & Abramson, 2001, for review). Movingbeyond the origins of the gender gap, research has recently begun tracing thegender gap in depression across late adolescence and young adulthood, withGe, Conger, and Elder (2001) showing growth in the gap across adolescenceand Ge et al. (2006) showing widening of the gap from early to lateadolescence, and narrowing from the late teens to age 23. However, giventhe few methodologically rigorous analyses on the topic and the non-representative nature of the samples analyzed, further research is clearlyneeded to better characterize variations in the gender gap across early life.

STRESS PROCESSES AND DEPRESSIVE SYMPTOMS

A longstanding axiom in the sociological study of health is that much of thevariation in health outcomes can be explained by differences in social


experiences. This perspective asserts that structural dimensions (e.g., SES,race/ethnicity, and gender) position individuals in social locations more orless conducive to health. Stress process theory extends this logic, theorizingthe mechanisms through which social structure impacts health. In theseminal statement of the theory, Pearlin and colleagues (1981) argue thatstress exposure is a primary determinant of mental health. They developa conceptual model distinguishing various types of stress exposure andtheorizing that the impact of stress is mediated and/or moderated bybuffering personal resources such as social support. In later work Pearlin(1989) explicitly contextualizes this stress process model, arguing thatindividuals’ exposure to stress and access to buffering resources is largelya function of their structural position in society. Here, we employ thestress process paradigm to conceptualize the social etiology of depression,dichotomizing the components as distal, structural, socioeconomic causes,and proximate factors including stressful events and social support.

Childhood Socioeconomic Status and Depressive Symptoms

The influence of SES on mental health has also been the subject of extensiveempirical investigation. Indeed, the significant positive correlation of SES andmental health is one of the most consistent empirical findings in the socialsciences over the last 50 years (see Haas, 2006, for review). However, the causaldirection of this effect has been the subject of considerable debate.1 While muchof the research to date has used cross-sectional, observational data incapableof supporting strong causal inference, there are several studies employingmethodologically rigorous designs indicating support for both social selection(health- status) and causation (status- health). For instance, Costello et al.(2003) examined data from the Great Smoky Mountains Study, in which acasino opened midway through the study giving every American Indian anincome supplement. This exogenous shock raised 14% of sample families outof poverty, resulting in a significant reduction in emotional symptoms(i.e., depression and anxiety) for the children transitioning out of poverty(Costello et al., 2003 [see Case 2003]). This and other analyses using robustanalytic approaches have indicated substantial social causation effects.

While there is a substantial body of literature demonstrating the influenceof childhood SES on depression, less research has considered genderdifferences in this effect. Of the few, mostly cross-sectional, studies toexamine this issue, some have indicated that childhood SES, operationalizedas parental occupation (Gilman, Kawachi, Fitzmaurice, & Buka, 2002),


parental education, and income (Gore, Aseltine, & Colton, 1992), has agreater impact among females. Although the authors of these studies offerlittle theoretical interpretation for these findings, the empirical results aresuggestive in this regard. For instance, Gilman and colleagues (2002) findthat female disadvantage virtually disappears at the highest SES levels, whilebeing quite large among low SES youth, indicating that when householdresources (material and/or psychosocial) are abundant, children of bothgenders enjoy relatively low depression levels, but when resources are scarce,girls are disproportionately affected. However, given the sparseness of thisevidence, it is also possible that there is no true gender difference and thatthese are chance findings based on relatively small samples. Support of thelatter possibility is found in the one extant trajectory analysis examining thistopic, which found no gender difference in the effect of income ontrajectories of depressive symptoms across early life (Ge et al., 2006). Thus,the question of gender differences in the influence of childhood SES ondepression across early life remains open and the large, nationallyrepresentative sample, multiple childhood SES indicators and longitudinalmethods used here are well-suited to advance understanding on this issue.

Stressful Life Events, Social Support and Depressive Symptoms

In the past 30 years many studies have examined the influence of recentSLEs on depression, providing consistent evidence of a significant effect(e.g., Paykel, 1978; Kendler, Karkowski, & Prescott, 1999; Ge et al., 2006).While most of this research has examined adult samples, consistent patternshave also been found among children and adolescents (Goodyer, Kolvin, &Gatzanis, 1985). For instance, using an index of 43 SLEs, Ge et al. (2001)found that SLEs were highly predictive of depressive symptoms in bothgenders across 7th to 12th grades. While the consistency of associationbetween event accumulation and disorder clearly demonstrate that SLEsindices yield meaningful estimates of stress exposure (Turner & Wheaton,1995), debate remains regarding gender differences in both sensitivity(Dornbush, Mont-Reynand, Ritter, Chen, & Steinberg, 1991; Ge et al.,1994; Aneshensel et al., 1991) and exposure (Turner, Wheaton, & Lloyd,1995; Turner & Butler, 2003) to SLEs.

Regarding gender differences in exposure to stressful events, research onadult populations has generally indicated that women experience a highervolume of SLEs than men (Turner et al., 1995). Early work in the areatheorized the cause of this gender disparity to lie in the homemaker role, which


researchers characterized as poorly rewarded, socially isolating, and generallyunsatisfying (Gove & Tudor, 1973; Gove & Geerken, 1977). However, laterresearch demonstrated the salience of factors beyond the homemaker status byshowing higher levels of psychological distress for women even among theemployed (Turner & Avison, 1989). Consequently, recent theoretical explana-tions have emphasized the stress entailed by the multiple role demands womenoften experience. For instance, researchers have pointed to the overload androle conflict experienced by employed women who also have primaryresponsibility for children and housework (e.g., Mirowsky & Ross, 1989).

While the above theory may help explain gender differences in stressexposure in the latter, young adult, portion of the longitudinal sampleexamined here, it is less useful in understanding gender differences in stressexposure in early life, before females take on multiple roles. Addressing thistopic, experts have suggested that the elevated volume of SLEs experiencedby adolescent girls is primarily driven by stressful events in the domain ofpeer interpersonal relationships (see Hankin & Abramson, 2001). Forinstance, Gore and colleagues (1992) suggest that due to adolescent girls’greater preoccupation with social standing and peer relationships, they tendto experience greater exposure to SLEs primarily through conflict ininterpersonal friendships and peer rejections. However, considering elevatedlevels of academic problems and risk behaviors among adolescent boys(Crick & Zahn-Waxler, 2003, for review), a contrasting argument suggestinghigher levels of SLEs among boys, at least in these domains, could easily beformulated. Furthermore, given that some studies of early life samples havefailed to find gender differences in SLE exposure (e.g., Turner & Butler,2003), further research into this topic is clearly needed.

The literature on gender difference in sensitivity to SLEs in early life has alsogenerally suggested female disadvantage, but researchers disagree as to why.Early research noting greater association between SLEs and depression amongwomen theorized that women were globally more vulnerable to stress due tofactors such as deficits in coping strategies (e.g., Kessler, 1979). Suchexplanations were later challenged by perspectives arguing that stress reactivitywas likely to be disorder specific (e.g., Aneshensel et al., 1991). Recent researchhas generally supported the latter notion, showing that men and women tendto react to stress in different ways, with females tending toward internalizingreaction, such as depression, and males tending toward externalizing behaviorssuch as alcohol and substance abuse (Hagan & Foster, 2003). However, theissue is far from settled, with some findings indicating no gender differences inthe effect of SLEs on depression (see Gore & Colten, 1991). Given thisambiguity, the current study has the potential to significantly advance


understanding, particularly given the dynamic nature of the longitudinalsample examined, which spans both adolescence and young adulthood.

Another central issue in investigations of the stress–depression relation-ship concerns moderation of the depressive effects of stress. Clearly,individuals differ substantially in how depressively they respond to stress,and social and personal resources, such as social support, have long beentheorized as primary buffers moderating the stress–depression relationship(Pearlin et al., 1981; Pearlin, 1989). Empirical analyses have consistentlysupported this proposition, indicating social support to be among thestrongest buffering resources typically examined (Turner & Lloyd, 1999).

Beyond assessing the role of social support as a buffering resource in thepopulation as a whole, advances have also been made in elucidating thesocial distribution of support. Thus, several studies have shown that womengenerally experience higher levels of social support than men (e.g., Goreet al., 1992; Turner & Marino, 1994). Indeed, gender differences in socialsupport have been shown to be one facet of a more pervasive genderdifference in interpersonal relationship styles. Thus, research has shown thatwomen generally place more emphasis on intimacy, emotional disclosureand empathy in interpersonal relationships (Bell, 1981; Gilligan, 1982),while male norms tend to discourage emotional expressiveness and disclosure(Lowenthal & Haven, 1968). Furthermore, while no direct, empirical test hasyet been conducted, this literature offers substantial indirect evidence thatwomen are likely to exhibit greater sensitivity to variation in support.Specifically, several studies have shown that the mental health detrimentassociated with deficits in interpersonal relationships is greater amongwomen. For instance, it has been shown that compared to men, women reactto marital conflict with greater psychological distress (Turner, 1994) and alsoexhibit higher levels of emotional reliance on others (Turner & Turner, 1999).Cumulatively, this research suggests that women are more sensitive to thequality of their interpersonal relationships. Thus, while no direct empiricalevidence is available, our reading of the literature suggests that the associationof social support to depression is likely greater among women.

METHODS

Sample and Procedures

Data from the three waves of the National Longitudinal Study ofAdolescent Health (Add Health) were used to develop our depressive


symptom trajectory models. Add Health is a nationally representative,school-based sample of 20,745 adolescents in grades 7–12 surveyed duringthe 1994–1995 academic year. The sampling frame consisted of all highschools in the United States. A total of 80 high schools were selected withprobabilities proportional to size and a sample of 52 feeder middle schoolswas attached to the sample of high schools. The response rate for the 134participating schools was 78.9%. Of the over 90,000 students whocompleted the in-school survey in 1994 a baseline sample of 20,745adolescents was selected for further data collection. The adolescents wereinterviewed three times during a 7-year period in 1994–1995, 1995–1996, and2001–2002. The overall sample is representative of U.S. schools with respectto region of the country, urbanicity, school type (e.g., public, parochial,private non-religious, military, etc.), and school size. Members of ethnicminority groups were over-sampled. Further details regarding the sampleare available at http://www.cpc.unc.edu/projects/addhealth/

Measures

Depressive SymptomsThe depressive symptoms scale is a 9-item derivative of the CES-D (Radloff,1991, 1997). Previous research has shown the 20-item CES-D to cluster intofour subfactors – somatic-retarded activity, depressed affect, positive affect,and interpersonal relationships. All four components are represented in the9-item scale used here. Individual items are coded on a four-point scale,from never or rarely (0) to most or all of the time (3) and refer to feelings therespondent had in the past week. The CES-D 9-item scale is consistentacross all three waves (a ¼ 0.79, wave one; a ¼ 0.80, wave two; a ¼ 0.80,wave three). The raw score means for the entire Add Health sample by waveare 5.66, 5.59, and 4.44, respectively.

Parental Socioeconomic StatusVariables measuring resident parent’s (generally, the mother’s) educationoriginate in the student surveys. Each respondent reports on the highestlevel of education that his or her resident parent completed. From thisinformation, the variable describing the mother’s educational attainmentwas derived. Additionally, mothers reported their education and that oftheir current partner, which were used to create a measure of father’seducation. The household income measure was also taken from the parentalquestionnaire. Income was measured in thousands of dollars of household


income in the previous year. Respondents are instructed to include theirown income, the income of everyone else in their household, and incomefrom welfare benefits, dividends, and all other sources. Bivariate correla-tions for the three SES indicators ranged 0.56–0.28, indicating collinearitywas not problematically high SES indicators were mean-centered to aid inmodel interpretation.2

Stressful Life EventsThe index of SLEs presented in Appendix A is derived from the measuresdeveloped by Ge et al. (1994). A major challenge of developing the currentmeasure of SLEs is to make it longitudinally accountable. As adolescentsmake the transition into adulthood, a number of stressors included in AddHealth data become irrelevant (e.g., expelled from school), and a numberof new stressors become appropriate (e.g., divorce, entering the militaryservice). To ensure stress is appropriately measured at different life stages,we used a slightly different set of items for wave III to capture the differentlife experiences. Complying with the most common practice for compar-ability (Turner & Wheaton, 1995), the current study selected only the eventsthat happened less than a year before the interview. Further, only acuteevents of sudden onset and of limited duration were included. Similar items(such as miscarriage and still birth, or dissolution of sexual nonromanticrelationship, romantic relationship, cohabitation, marriage) were groupedtogether to avoid making the measurement overly specific, at the same timeinsuring a sufficient volume of events to form a relatively continuousmeasurement. Additive indices were then created with raw score means forthe entire Add Health sample by wave equal to 2.37, 1.75, and 1.54,respectively. The index was standardized in the data analysis.

Social SupportThe social support index shown in Appendix B is a composite measure ofperceived social support. It assesses how the respondents feel about theirrelationship with their closest social ties such as family, teachers, andfriends. Additive indices were created by summing the items, with raw scoremeans for the entire Add Health sample by wave equal to 32.02 and 31.82,3

respectively. The index is mean-centered in the analysis.

Race/EthnicityRace/ethnicity was included as a control in all models. In keeping withthe new census policy, Add Health respondents were allowed to mark asmany race/ethnicity categories as they felt applied to them. Approximately


4% of the sample identified as multi-racial/ethnic. Given this, we used thecoding method used by the Add Health data manager as a way to obtainmutually exclusive race/ethnicity categories for the primary analysis. Thus,a single race is assigned to those reported multiple racial/ethnic back-grounds using the following criteria: if the respondent reported singlerace/ethnicity, he/she will be coded as is; if the respondent reported morethan one race, only one race will be selected from the races the respondentreported in the following order: Hispanic, Black, Asian, and White. In asensitivity analysis, a reduced sample composed of only individualsidentifying as one race/ethnicity was used and results were compared forrobustness.

Analytic Strategy

While developmental theory posits age as the appropriate metric in thestudy of longitudinal change, Add Health data is not organized by age, butby wave. Thus, given the substantial age variation within each wave of AddHealth (Table 1), it was necessary to reorganize the data from wave to age inorder to address our research aims. While this approach is clearly indicatedfrom a developmental perspective, it is not without potential weaknesses.Specifically, the method entails grouping individuals from different cohortsinto the same synthetic cohort, leaving open the question of potential cohorteffects. To address this possibility, sensitivity analyses were conducted andall substantive findings were robust to the control of cohort effects.

To examine the development of depression across the ages 11–27 weemployed individual growth curve modeling within a mixed model (i.e.,hierarchical linear models, HLM) framework, which is a data analysistechnique especially designed to explore longitudinal panel data (Goldstein,1995; Bryk & Raudenbusch, 1992). Longitudinal panel data, such as in thepresent study, can be considered to be clustered or hierarchical data becauserepeated observations (first level) are nested within subjects (secondlevel) (Willett, Singer, & Martin, 1998). We chose individual growth curvemodeling over ordinary regression analysis, because the former methodaccounts for the dependency of the data owing to this clustering (Goldstein,1995). Ordinary regression analysis would estimate a single equation forall data, whereas individual growth curve modeling fits a curve for eachindividual subject. These curves (i.e., depression development by age) arecharacterized by their intercept (or baseline level) and slope (rate of change).The addition of independent variables to the model, such as education level

Table 1. Frequency Distribution of Age, by Wave (Counts andPercentages).

Age Wave I (1995) Wave II (1996) Wave III (2001–2002)

Freq Pct Freq Pct Freq Pct

11 6 0.06

12 292 2.94 5 0.07

13 1,224 12.31 351 4.73

14 1,422 14.3 1,054 14.19

15 1,870 18.81 1,251 16.85

16 1,927 19.38 1,593 21.45

17 1,811 18.22 1,561 21.02

18 1,217 12.24 1,139 15.34 78 1.02

19 156 1.57 400 5.39 790 10.29

20 14 0.14 67 0.9 1,104 14.37

21 3 0.03 5 0.07 1,293 16.83

22 1,465 19.07

23 1,427 18.58

24 1,148 14.95

25 332 4.32

26 39 0.51

27 4 0.05

28 1 0.01

Total 9,942 100 7,426 100 7,681 100


and childbearing status, is aimed at explaining between-subject variation(in intercept and slope) of the depression growth curves.

The method has a number of advantages over traditional statisticalmethods for analysis of quantitative longitudinal data. First, substantivequestions can be addressed within the multilevel framework, e.g., whethersome individuals experience faster rates of over time change in depressionlevels than others (Willett et al., 1998). Second, the method accounts for thedependency of observations caused by clustering (Goldstein, 1995). Third,any number of waves of data can be accommodated; the occasions ofmeasurement need not be equally spaced; and data-collection schedulescan be different for different individuals (Willett et al., 1998). Finally, theapproach is particularly suitable for dealing with incomplete data (Diggle &Kenward, 1994).

We began our investigation of gendered variation in trajectories of earlylife depression by modeling the unconditional (i.e., no predictors other thanage) growth curve stratified by gender. Comparisons of various trajectory


shapes (i.e., linear, quadratic, and cubic), indicated a quadratic growthcurve characterized by random intercepts and random linear and fixedquadratic age slope components was the best fit to the data according tonested likelihood ratio tests (LRTs) of model fit.4 After determining thegeneral modeling strategy of quadratic age-based growth curves stratifiedby gender, we then sequentially introduced groups of covariates in a nestedfashion to investigate gender differentials in the influence of race/ethnicity,SES, SLEs, and social support. t-Tests were conducted to formally testgender difference in the model parameters (see Table 3 note). Finally, thelast model presents the trimmed model in which only significant effects areretained. All analyses were conducted in Stata 9.2.

RESULTS

Descriptive Statistics

Table 2 presents the descriptive statistics for the analysis variables bygender. Although age is a continuous measure in our study, and depressivesymptoms and SLEs are measured at each age, we present descriptivestatistics for these variables consolidated into five age groups for the sake ofconcision. For both gender subgroups depressive symptoms show a patternof moderate increase across the younger ages, peaking at ages 15–17 anddeclining relatively sharply from ages 18–27. There is a gender gap, withfemales having noticeably higher values. Furthermore, the depressivesymptom means suggest a narrowing of the gender gap over time. TheSLEs repeated measures show similarities to the depressive symptomsprofile. On average, respondents start at relatively low levels of SLEs in theearly teens, increase until ages 15–17 and then decline in young adulthood.On average, males show elevated levels of SLEs compared to females,particularly at younger ages. Values on SES variables and social support aregenerally comparable across gender in this sample.

Growth Curve Models of Depressive Symptoms

To model trajectories of depressive symptoms, we begin by examining aseries of unconditional trajectories by gender to identify the correctfunctional form of the growth curve. In preliminary analyses we comparedvarious specifications including a simple linear model, and two polynomial

Table 2. Descriptive Statistics.

Variable Male Female

Mean (%) Std. Dev. Mean (%) Std. Dev.

CES-D 11-14 4.370 3.345 5.506 4.274

CES-D 15-17 5.122 3.757 6.467 4.518

CES-D 18-20 4.959 3.838 5.715 4.428

CES-D 21-23 4.130 3.705 4.751 4.347

CES-D 23-28 4.047 3.744 4.436 3.935

SLEs 11-14 2.154 2.433 1.435 1.860

SLEs 15-17 2.662 3.032 1.884 2.217

SLEs 18-20 2.314 2.748 1.566 1.773

SLEs 21-23 1.684 1.844 1.460 1.554

SLEs 23-28 1.691 1.769 1.310 1.409

White 0.678 – 0.666 –

Black 0.153 – 0.171 –

Asian 0.042 – 0.035 –

Hispanic 0.128 – 0.128 –

Household income 52.452 50.016 53.442 52.635

Mother’s education 5.655 2.376 5.625 2.394

Father’s education 5.696 2.243 5.650 2.265

Perceived social support 31.770 4.392 32.023 4.393


(i.e., quadratic and cubic) functions. These analyses showed that thequadratic model with random intercept and slope fit the data well andrepresented a superior balance of accuracy and parsimony. The results ofthe quadratic age-based growth curve are shown in Tables 3 (for males) and4 (for females). As shown in model 1 of Tables 3 and 4, this quadratic modelfit the data well with all fixed effects strongly significant. Thus, depressivesymptoms in early life are well modeled as a curvilinear trajectory withvalues rising early in the trajectory, before declining in the mid and latersections. The mean trajectory is higher for females than males, with thedifference primarily in intercept (b0 ¼ 5.739 and 4.271, respectively).However, as shown in Fig. 1, there is some evidence of convergence asfemale’s depressive symptom levels begin declining earlier and more steeplythan males. While the results suggest narrowing of the gender gap in youngadulthood, given that coefficient t-tests indicate that the gender differencesin the age and age2 are not significantly different, we are hesitant toconclude they indicate convergence. Significant random effects for eachgender indicate considerable variance around this mean trajectory, withgreater variability in trajectory shapes among females than among males.

Table 3. Trajectories of CES-D for Males Predicted by SES, SLE,Perceived Social Support, and Family Structure (N ¼ 4,992).

Model 1 Model 2 Model 3 Model 4 Model 5

Fixed Effects

Intercept 4.271�� 3.962�� 4.035�� 4.270�� 4.331��

Age 0.289�� 0.296�� 0.301�� 0.252�� 0.205��

Age2 �0.026�� 0.026�� 0.027�� 0.023�� 0.021��

White – – –

Black 0.762�� 0.699�� 0.527�� 0.679��

Asian 0.882�� 1.003�� 1.036�� 0.981��

Hispanic 1.045�� 0.668�� 0.500�� 0.577��

Log household income �0.002� �0.002 �0.001

Mother’s education �0.078�� 0.056�� 0.061��

Father’s education �0.134��

�0.122��

�0.116��

Stressful life events 0.697��

0.513��

Perceived social support �0.242��

Random Effects

Level 1 residual 2.625�� 2.633�� 2.642�� 2.643�� 2.647��

Level 2 intercept 3.391�� 3.325�� 3.240�� 3.021�� 2.609��

Level 2 age 0.304��

0.299��

0.294��

0.280��

0.272��

Corr (intercept, age) �0.697 �0.695 �0.691 �0.680 �0.648

Log likelihood �30,461.3 �30,414.8 �30,358.8 �30,142.7 �29,828.8

Note: Bold letters indicate a statistically significant (po.05) difference between coefficient for

compared groups (either male and female, or White and each minority group) according to a

t-test (one-tailed), z ¼ bx � by=ffiffiffiffiffiffis2bx

qþ s2by where bx and by are the coefficients and s2bx and s2by

are the squared standard error of the coefficients for group 1 and 2, respectively (Clogg et al.,

1995).�po.05. ��po.01.


Next, as shown in model 2 of Tables 3 and 4, we introduce a battery ofdummy variables to assess racial differences. Results indicate that allminority groups have significantly higher levels of depressive symptomsthan the White reference group for both males and females. For bothgenders, Asians and Hispanics show the highest levels of depressivesymptoms, with Blacks falling between these groups and Whites. Theinclusion of race/ethnicity resulted in a significant improvement in model fitfor both genders as measured by LRTs.

Gender Differences in the Effects of SES, SLEs, and Social SupportHaving identified a well-fitting model of depressive symptom trajectories,we then move to examine gender differences in the effects of SES, SLEs, and

Table 4. Trajectories of CES-D for Females Predicted by SES, SLE,Perceived Social Support, and Family Structure (N ¼ 4,950).

Model 1 Model 2 Model 3 Model 4 Model 5

Fixed Effects

Intercept 5.739�� 5.459�� 5.540�� 5.987�� 5.931��

Age 0.250�� 0.250�� 0.251�� 0.167�� 0.162��

Age2 �0.030�� 0.030�� 0.030�� 0.026�� 0.026��

White – – –

Black 0.642�� 0.527�� 0.299� 0.219

Asian 1.158�� 1.394�� 1.465�� 1.235��

Hispanic 1.040�� 0.686�� 0.629�� 0.551��

Log household income �0.002 �0.001 0.000

Mother’s education �0.138�� 0.104�� 0.080��

Father’s education �0.068��

�0.064�

�0.069��

Stressful life events 1.383��

1.095��

Perceived social support �0.313��

Random Effects

Level 1 residual 3.086�� 3.088�� 3.096�� 3.109�� 3.111��

Level 2 intercept 4.316��

4.280��

4.210��

3.774��

3.160��

Level 2 age 0.369��

0.368��

0.364��

0.338��

0.330��

Corr (intercept, age) �0.755 �0.756 �0.755 �0.752 �0.718

Log likelihood �34,104.7 �34,071.5 �34,030.2 �33,654.8 �33,244.7

Note: Bold letters indicate a statistically significant (po.05) difference between coefficient for

compared groups (either male and female, or White and each minority group) according to a

t-test (one-tailed), z ¼ bx � by=ffiffiffiffiffiffis2bx

qþ s2by where bx and by are the coefficients and s2bx and s2by are

the squared standard error of the coefficients for group 1 and 2, respectively (Clogg et al., 1995).�po.05. ��po.01.


social support. As shown in model 3 of Tables 3 and 4, childhood SES isoperationalized as three variables – household income, mother’s education,and father’s education. While the effects of household income show little orno effects, both mother’s and father’s education exert strong, significantlynegative effects on depressive symptom levels. There are also noteworthygendered patterns to these SES effects, with father’s education showingstronger effects among males. The effects of age and age2 remain significantand continued to indicate a curvilinear, inverted U-shaped trajectory. LRTsindicate that the inclusion of childhood SES significantly improves model fitfor both genders.

Model 4 investigates gender differentials in the effects of SLEs ondepression trajectories. As shown in Tables 3 and 4, the effects of SLEs are

34

56

7D

epre

ssiv

e sy

mpt

oms

11 16 21 26

Age

Males Females

Fig. 1. Unconditional Trajectories of Depressive Symptoms by Gender.


large, positive, and highly significant for both males and females. There isalso considerable gender difference in the effect of SLEs, illustrated inFig. 2, with the effect for females approximately twice as large as that ofmales (bSLEs ¼ 1.383 and 0.697, respectively). Thus, females show greatervulnerability to the occurrence of such life events. Further, SLEs are shownto mediate the effects of childhood SES, with the effect of mother’seducation dropping approximately 25% for both genders with the inclusionof SLEs. For both genders, the inclusion of SLEs resulted in a largeimprovement in model fit.

In the final model we introduce perceived social support as a predictor ofearly life depression trajectories. As shown in Tables 3 and 4, the effects ofperceived social support on the trajectories are large, negative, and highlysignificant for both males and females. However, similar to SLEs, perceivedsocial support is found to exert a greater impact on depressive symptomsamong females compared to males (bsupport ¼ �0.313 and �0.242,respectively) (Fig. 3). Also notable are the mediation effects of perceivedsocial support on childhood SES and SLEs. Thus, with the inclusion ofperceived social support the effects of both SES and SLEs declined

24

68

11 16 21 26 11 16 21 26

Female

High SLEs Low SLEs

Dep

ress

ive

sym

ptom

s

Age

Note: High and Low SLEs defined as +/-1 SD from overal mean SLEs.

Male

Fig. 2. Gender Difference in the Effects of SLEs on Depressive Symptom

Trajectories.

02

46

8

11 16 21 26 11 16 21 26

Female

High Social Support Low Social Support

Dep

ress

ive

sym

ptom

s

Age

Note: High and Low social support defined as +/-1 SD from overal mean social support.

Male

Fig. 3. Gender Difference in the Effects of Social Support on Depressive Symptom

Trajectories.



considerably for both genders, particularly the effects of SLEs. Comparingthe random effect parameters from the baseline model and the final modelshows that cumulatively, race/ethnicity, childhood SES, SLEs, and socialsupport explain much of the variability in trajectory shapes. For males, 23%of the variance in trajectory intercepts (Dm0 ¼ 2.609/3.391) and 11% intrajectory slopes5 (Dmslope ¼ 0.272/0.304) is explained by these covariates.Among females, the full model explains 27% and 11% of trajectoryintercepts and slopes, respectively. Finally, for both genders, LRTs indicatethe inclusion of social support results in an improvement in model fit.

DISCUSSION

Research conducted from the stress process perspective has long indicatedsubstantial gender differences in the social etiology of depression. Whilethis literature has produced many findings of substantial practical andtheoretical utility, several fundamental questions regarding gendered varia-tion in the influence of the stress process on depression remain unanswered.Using Add Health, the largest nationally representative longitudinal sampleof U.S. adolescents and young adults, this study addresses limitationsin current knowledge regarding the course of gender disparity in early lifedepressive symptoms and in the role of stress, social support, and SESin explaining these gender differences. Employing mixed model growthcurves, we examine gender differences in the effects of childhood SES andSLEs on trajectories of depressive symptoms across adolescence and youngadulthood.

Several major findings emerge from this analysis. First, depressivesymptom trajectories are curvilinear for both gender groups, withtrajectories first rising across most of adolescence, then declining in youngadulthood. Second, although the basic shape of the trajectories is similaracross gender, there are substantial differences – most notably, femalesevidence higher levels across the entire period examined (ages 11–28). Alsonotable is the fact that females appear to be relatively precocious in theirdevelopment, peaking and declining at earlier ages than their male peers.Third, there is evidence of racial/ethnic disparity for both genders, with allminority groups having higher levels of symptoms than Whites. Fourth,although the overall explanatory power of SES is comparable for malesand females, the influence of the individual indicators varied, with father’seducation more important among males. Fifth, gender differences inexposure are evident, with males experiencing more SLEs. Finally, gender


differences in sensitivity to SLEs and social support were also found, withfemales showing greater sensitivity to both factors.

Our analyses show that trajectories of depressive symptoms are curvi-linear across adolescence and young adulthood and that major genderdifferences exist across this period. Supporting other literature indicatingthat depression levels peak in mid to late adolescence (Wight et al., 2004;Ge et al., 2006), we found trajectory apices to generally occur around ages16–17. These findings highlight the difficulties associated with adolescenceand the beneficial effects of many of the events associated with the transitionto adulthood, such as achieving independence, establishing stable relation-ships, and an increasing sense of control (Mirowsky & Ross, 1992;Schieman, Van Gundy, & Taylor, 2001).

As expected, females were shown to have persistently higher levels ofdepressive symptoms than males. However, the suggestion of convergenceobserved between male and female depressive symptom trajectories in youngadulthood was less expected. While some other studies have indicated slightnarrowing of the gender gap in the transition to adulthood (Hankin et al.,1998; Ge et al., 2006), this is the first study to suggest such dramaticconvergence. Although these findings should only be considered suggestivegiven the lack of formal statistical gender differences in the age slopes, giventhe relative superiority of the data employed here over that used in formerexaminations, we believe this anomalous finding deserves further investiga-tion. That said, it is important to note that the trajectories presentedhere should not be extrapolated beyond the ages included in the sample.Given the overwhelming evidence of gender depression differentials acrossadulthood (e.g., Mirowsky, 1996), if any gender gap narrowing does occur,it must either stabilize or reverse at some point in early adulthood.

Considering that female depression levels peak in mid adolescence, whilemales peak afterward in late adolescence, we suggest that a likely explana-tion for the pattern observed here is that females experience comparativelyprecocious social psychological development. Thus, we expect that as thefemale decline in depression levels peters out in adulthood, the male declineis apt to continue a bit longer, resulting in reinstatement of the gendergap at older ages. In sum, our findings regarding the gender gap supportthe longstanding finding of female disadvantage in depression (Nolen-Hoeksema, 1990), but a more nuanced picture of fluctuation in the gendergap in depression across early life is revealed.

While race/ethnicity was treated as a control in these analyses, the resultsindicating a general advantage for Whites of both genders are noteworthygiven the lack of consensus on this issue in the literature. Former studies have


alternately shown adolescent Blacks (Garrison, Jackson, Marsteller,McKeown, & Addy, 1990; Gore & Aseltine, 2003), Hispanics (Twenge &Nolen-Hoeksema, 2002), Asians (Greenberger & Chen, 1996), and Whites(Dornbusch, Mount-Reynand, Ritter, Chen, & Steinberg, 1991) each toexhibit higher rates of depression compared to other racial/ethnic groups.Given the exceptional size and quality of the Add Health data analyzed here,our finding of higher levels of depressive symptoms among minorities com-pared to Whites represents significant progress in the debate on this issue.

Although empirical findings to date have been mixed, the minoritydisadvantage in early life depression observed here is generally consonantwith leading theories of racial health disparities. For instance, the structuraldisadvantage perspective on racial health disparities suggests that minoritiesface pervasive, interlocking adversity from factors including lowSES, discrimination, and neighborhood disadvantage (Vega & Rumbaut,1991; Ross, 2000; Williams & Collins, 1995). Cumulatively, this pervasivestructural disadvantage is theorized to exert a strong negative effect onpsychological well-being (Vega & Rumbaut, 1991; Williams & Collins,1995). Thus, our finding that racial/ethnic minority status was consistentlyassociated with reduced psychological well-being, even after adjustingfor childhood SES, is consistent with leading theoretical formulations(Williams, Neighbors, & Jackson, 2003). Future research should elaboratethese findings, taking advantage of the rich behavioral and psychologicaldata present in Add Health to empirically model the causes of these racialdisparities in early life depression.

Childhood SES was shown to be highly influential on depressiontrajectories for both genders. Given our exogenous conceptualization ofsocioeconomic environment as childhood SES, these findings providedstrong evidence that the direction of the effect here is SES-depression,lending support to social causation theories of depression (e.g., Link &Phelan, 1995; Mirowsky & Ross, 2003). Furthermore, there were alsosubstantial differences in the effect size and significance for the threeindicators of SES. Contrary to former research indicating income asa stronger predictor of early life depression than parental education(Gore et al., 1992), we found the opposite, with household income showingnonsignificant effects, while both father’s and mother’s education werehighly significant in all models.

These findings debunk the common assumption in health research thateducation and income are interchangeable indicators of SES (see Bravemanet al., 2005). In contrast, our results demonstrate that, though related,education and income represent different underlying concepts – schooling


means something beyond simply economic status. As Mirowsky and Ross(1998) show, in addition to its income enhancing function, educationpromotes health by learned effectiveness – increasing exposure to healthybehaviors and lifestyles, and promoting the sense of control necessary toadopt and maintain these behaviors. Further, they demonstrate that highlyeducated parents not only reap the health benefits of increased knowledgeand mastery, but they pass these benefits on to their children (Mirowsky &Ross, 1998). Thus, though lacking the data for an empirical test, we suspectthat the relative importance of parental education over income points tothe fact that, in an affluent society like contemporary America, the healthbehaviors, parenting knowledge, and sense of mastery engendered byeducation are of greater importance than material resources in promotingmental health in one’s children.

Additionally, there were gender differences in the effects of the parentaleducation, with males showing greater sensitivity to father’s education.These results contradict earlier findings that parental education is generallymore influential on females (Gore et al., 1992). While no definitiveinterpretation is suggested by either our empirical results or extant theory,we suspect that this result may be explained by same-sex role modelingtendencies. Our interpretation here hinges on two facts: (1) children show agreater tendency to model the attitudes and behaviors of their same-sexparents (Bandura, 1977; Kohlberg, 1966); and (2) education may serveas a proxy for a variety of mechanisms promoting depression in adults(Mirowsky & Ross, 1998). Thus, we suggest that by modeling thedepression-related attitudinal and behavior tendencies of their fathers,boys tend to reflect their father’s mental health profiles more so than theirmothers. While obviously a tentative interpretation, given the fact thata similar, but not statistically significant, relationship was observed formother–daughter dyads, we see this as a plausible explanation deservingfuture study.

In line with stress process theory, the influence of childhood SES ontrajectories of depressive symptoms were shown to be partially mediatedby SLEs across the ages examined (Pearlin, 1989). We find that childhoodSES has a large, protective, direct impact on depression trajectories andalso a substantial indirect effect through reducing the likelihood of SLEoccurrence. This corroborates research positing the occurrence of stressfulevents as primary paths through which poverty reduces psychologicalwell-being (Turner & Lloyd, 1999; Turner & Butler, 2003).

As alluded to above, for both gender groups SLEs were found to promotedepressive symptoms. These effects were found to be consistent across age


and to show strong evidence of gender disparity. Regarding differentialexposure, SLEs occur less frequently among females. However, this is likelythe result of limited coverage of the SLE measurement. Due to the limitationof the available information in Add Health data, our SLE measurementmay not tap the full relevant range of events often experienced by therespondents, especially during youth adult ages. It includes a substantialportion of the events reflecting participation in delinquent or problembehaviors and/or associations with rule breaking network members. Theseevents are potential ‘‘self-generated’’ stressors rather than ‘‘fateful’’ stressors(see Turner & Wheaton, 1995). This could potentially favor the types ofstressors experienced by males as shown from their increasing representationamong males at older ages.

Regarding gender differences in sensitivity to stress, though occurring lessfrequently among females, the SLEs measured here were shown to havetwice the impact on females compared to males. Thus, females were shownto exhibit substantially greater sensitivity to these SLEs than do males. Thisfinding is consistent with former research showing increased sensitivity tostress among females (e.g., Ge et al., 1994; Kessler, 1979). However, in linewith research showing gender differences in stress sensitivity to be disorderspecific (Aneshensel et al., 1991; Hagan & Foster, 2003), we do not see thisas evidence that females are generally more sensitive to the detrimentaleffects of stress. Instead, we interpret this finding as evidence of a genderedresponse to the stress process such that females are more likely to internalizestress and males are more likely to externalize it (see Meadows, 2007). Giventhat an explicit test of this hypothesis was beyond the scope of this article,we leave more detailed empirical investigations to future research.

Similar to the results for SLEs, social support was found to have large,significant effects for both genders, but the effect size was approximately30% larger for females. This finding fills an important gap in the literature,as no studies have yet explicitly examined gender differences in the effectsof social support. But while no empirical work has been done explicitly onthis topic, the results are consistent with theory. Specifically, given thecommon emphasis on relationships among women (Turner & Marino,1994), it is not surprising that social support is more influential ondepressive symptoms in this group.

Although these analyses offer some of the first comprehensive trajectorymodels of depressive symptoms in early life for both genders, the study isnevertheless limited in several respects. First, additional waves of datawould allow further refinement and extension of these findings. The presentinvestigation was limited to the three waves of data currently available from


the Add Health study. Further understanding of the process of depressionwould be facilitated through additional waves of data extending the ageinterval further into adulthood. Fortunately, such analyses will soon bepossible using this dataset, as the fourth wave of data collection is nowunderway for Add Health (http://www.cpc.unc.edu/projects/addhealth/design_focus/wave4). When released these data will allow the extension ofthe models presented here into the participants’ late 20’s and early 30’s.

Another shortcoming of the current study was our partial conceptualiza-tion of the stress process. Here, we limited our modeling of the stress processto only childhood SES, SLEs, and social support. However, it has beendemonstrated that other aspects of the stress process, including chronicstressors and self esteem, are also important components of the stress–depression relationship (Pearlin, 1989). Future research could improve theanalysis presented here through a more exhaustive modeling of the stressprocess, including chronic stressors and other buffering psychologicalresources. Another potential improvement in the measurement of stresscould be achieved through disaggregating the SLEs index into variousdomains (e.g., Ge et al., 2006).

Despite these limitations, the present study improves our understandingof gender differences in early life trajectories of depressive symptoms and inthe effects of childhood SES, SLEs, and social support. The results indicatethat trajectories of depressive symptoms during adolescence and youngadulthood follow a normative curvilinear pattern with rising levels acrossmuch of adolescence and declining levels in young adulthood. However,gender differences in trajectories were evident – females had persistentlyhigher levels, but the gap showed some evidence of diminishing acrossyoung adulthood. Gendered variation in the function of the stress processwas also shown with females responding far more depressively to SLEs anddeficits in social support than their male counterparts, suggesting thatthe female disadvantage in early life depression may be a result of genderdifferences in response to adversity.

NOTES

1. Significant social selection effects have been shown in some mental healthresearch (e.g., Costello, Keeler, & Angold, 2003; Miech et al., 1999; Dohrenwendet al., 1992) and unfortunately, the effects of selection and causation are notoriouslyhard to separate in non-experimental, survey research. However, in the current studywe have largely avoided the risk of confounding social selection effects throughfocusing on parental SES during the subjects’ youth. Thus, social selection effects are


likely to be minimized as the children’s mental health is generally unlikely to have adramatic influence on their parents’ SES, particularly given that a major componentof SES – parental education, was generally determined prior to the subjects’ births.2. In mixed model growth curves, if all covariates are mean-centered the fixed

effects of age describe the overall mean trajectory shape; if covariates are not mean-centered the growth factor means represent the trajectory shape for cases with valuesof zero on all covariates – as this information is generally less substantivelyimportant, it is common practice to mean-center continuous covariates (Bollen &Curran, 2006).3. Social support was only measured at waves 1 and 2. The measure used here is

an average of the two.4. Likelihood ratio tests were used to determine the significance of the fixed and

random effects that were added to the model in each of the analysis steps. This testyields the deviance of the model which is defined as -2xloglikelihood. The deviancedifference (between 2 models) is asymptotically w2 distributed, with the number ofdegrees of freedom equal to the difference in number of estimated parametersbetween the two models. To judge the significance of parameters in the full model,each parameter was removed from the model, and a likelihood ratio test with onedegree of freedom was used to examine whether its effect was significant in this fullmodel.5. The random effect here refers only to the linear component of the age-based

slope, as the quadratic component is modeled as fixed.

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APPENDICES

Appendix A. List of Stressful Life Event Items in Each Wave.

Items Available in All Three WavesParent deathSelf attempted suicide resulting in injuryFriend attempted suicide (unsuccessful)Friend attempted suicide (with success)Relative attempted suicide (unsuccessful)Relative attempted suicide (with success)Involving in fighting or violenceUnwanted pregnancy (self or partner)Abortion, still birth, or miscarriage (self or partner)Having a child adoptedDeath of a childRomantic relationship endedGiving sex in exchange for drugs or moneyContracted an STDSkipped needed medical care due to financial constraintsJuvenile convictionAdult convictionImprisoned

Wave I and IIHaving a serious injuryExpelled from schoolRan away from homeParents received welfareNonromantic sexual relationship endedAbuse in romantic or nonromantic sexual relationship

Wave IIIReceived welfareBaby having major health problems at birthMarriage dissolutionCohabitation dissolutionDeath of a romantic partnerEviction, cutoff serviceEntering full time active military dutyDischarged from the armed forces

Note: All items are coded as 1 year before the interview.


Appendix B. Social Support Scale.

1. H
ow much do you feel that adults care about you? 2. H ow much do you feel that your teachers care about you? 3. H ow much do you feel that your parents care about you? 4. H ow much do you feel that your friends care about you? 5. H ow much do you feel that people in your family understand you? 6. H ow much do you feel that you want to leave home? (Reverse coded
for comparability)
7. H ow much do you feel that you and your family have fun together? 8. H ow much do you feel that your family pays attention to you?

stress processes and trajectories of depressive symptoms in early life: gendered development

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