Subjective Well-Being in Urban Adolescents

http://yas.sagepub.com/Youth & Society

http://yas.sagepub.com/content/43/2/609The online version of this article can be found at:

DOI: 10.1177/0044118X09353517

2011 43: 609 originally published online 22 December 2009Youth SocietyBena Vacek and Laura Dick Coyle

Melissa L. Morgan, Elizabeth M. Vera, Rufus R. Gonzales, Wendy Conner, KimIndividual, and Community Influences

Subjective Well-Being in Urban Adolescents: Interpersonal,

Published by:

http://www.sagepublications.com

can be found at:Youth & SocietyAdditional services and information for

http://yas.sagepub.com/cgi/alertsEmail Alerts:

http://yas.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://yas.sagepub.com/content/43/2/609.refs.htmlCitations:

What is This?

- Dec 22, 2009 OnlineFirst Version of Record

- May 27, 2011Version of Record >>

at Alexandru Ioan Cuza on May 26, 2014yas.sagepub.comDownloaded from at Alexandru Ioan Cuza on May 26, 2014yas.sagepub.comDownloaded from

http://yas.sagepub.com/

http://yas.sagepub.com/content/43/2/609

http://www.sagepublications.com

http://yas.sagepub.com/cgi/alerts

http://yas.sagepub.com/subscriptions

http://www.sagepub.com/journalsReprints.nav

http://www.sagepub.com/journalsPermissions.nav

http://yas.sagepub.com/content/43/2/609.refs.html

http://yas.sagepub.com/content/43/2/609.full.pdf

http://yas.sagepub.com/content/early/2009/12/22/0044118X09353517.full.pdf

http://online.sagepub.com/site/sphelp/vorhelp.xhtml



Youth & Society43(2) 609 –634

© 2011 SAGE PublicationsReprints and permission: http://www. sagepub.com/journalsPermissions.nav

DOI: 10.1177/0044118X09353517http://yas.sagepub.com

Subjective Well-Being in Urban Adolescents: Interpersonal, Individual, and Community Influences

Melissa L. Morgan1, Elizabeth M. Vera2, Rufus R. Gonzales3, Wendy Conner2,Kim Bena Vacek2 and Laura Dick Coyle2

Abstract

This study examined the relationship between subjective well-being criteria (negative affect, positive affect, and subjective well-being) and individual, family, friend, school, and neighborhood predictor variables in 159 ethnically diverse, urban adolescents. Results indicated that negative affect was significantly predicted by family variables, positive affect was significantly predicted by individual, school, and friend variables, and satisfaction with life was significantly predicted by individual and family variables. Limitations, directions for future research, and clinical implications of these findings are discussed.

Keywords

subjective well-being, adolescence, youth

Recently, mainstream psychology has begun to shift its attention toward wellness and positive psychology (Seligman & Csikszentmihalyi, 2000). Pro-ponents of a strengths-based perspective call for practitioners to focus on enhancing quality of life and promoting mental health in their clients, rather than solely treating pathology (Smith, 2006). One construct in the area of wellness to which significant scholarly contributions have been made is

1University of California, Santa Barbara2Loyola University, Chicago3DePaul University, Chicago

at Alexandru Ioan Cuza on May 26, 2014yas.sagepub.comDownloaded from


610 Youth & Society 43(2)

subjective well-being (SWB). SWB entails the evaluation of one’s own life satisfaction (Robbins & Kliewer, 2000). Diener’s (e.g., Diener, Suh, Lucas, & Smith, 1999) tripartite model of SWB consists of both cognitive and affective components. Cognitive evaluations of SWB are assessed through one’s global judgment of life satisfaction. Affective components (i.e., predominant moods and emotions) are assessed by evaluations of the frequency with which one experiences pleasant and unpleasant emotions. Thus, the model defines SWB as consisting of three interrelated factors: global life satisfaction, positive affect, and negative affect (Lightsey, 1996; Robbins & Kliewer, 2000).

The majority of research in the SWB field concerns potential predictors of life satisfaction (Diener, 2000), which, according to Lent (2004), tend to fall into one of three categories: demographic variables, personality/dispositional variables, and acquirable skill sets or environmental variables. Many studies have explored personality variables conceptually related to SWB such as self-esteem, optimism, hardiness, and agreeableness (Diener et al., 1999; Lightsey, 1996). Demographic factors such as income level, education, and marital status have also been explored extensively (Oishi, Diener, Lucas, & Suh, 1999), but it has generally been found that such variables tend to explain the least amount of variance in SWB as a group (Lent, 2004). Despite these specific findings, however, it has been suggested that the criteria on which life satisfaction is assessed may be culturally dependent.

For example, Diener and his colleagues, in a variety of studies (e.g., Diener et al., 1999; Oishi et al., 1999), have found that in collectivist cul-tures, family well-being is a stronger predictor of life satisfaction than are individual variables (e.g., self-esteem) whereas in individualistic cultures, the opposite is true. The majority of this research has used cross-national comparisons, however, and far less is known about the extent to which con-textual influences on SWB may exist among diverse populations within the United States (Edwards & Lopez, 2006).

SWB in Children and AdolescentsPrevious SWB research with children and adolescent populations has found correlations between SWB and social support, intelligence, parenting style, gang involvement, and global self-concept (e.g., Ben-Zur, 2003; Henry, 2001). In general, however, SWB research has focused primarily on college-aged and adult populations. In particular, there is a dearth of research on SWB in urban adolescent populations that are arguably more vulnerable to environ-mental risk factors that may compromise well-being (McCullough, Huebner, & Laughlin, 2000). Therefore, study of SWB in the potentially more



Morgan et al. 611

vulnerable adolescent (as opposed to adult) population may be particularly helpful in pinpointing risk factors and mechanisms which may promote SWB, data which goes beyond the extant literature.

Although little is known about the successes and psychological wellness of adolescents, urban youth have been the focus of many studies which docu-ment accounts of their vulnerabilities and lack of opportunities (Marsella, 1998). It has been argued that the plethora of struggles is due to the toxic environments in which many urban adolescents, particularly youths of color, reside. These environments increase their risk for a variety of mental health issues (Garbarino, 2001). For example, over the past decade, researchers have documented disturbing rates of teenage pregnancy (Coley, Kuta, & Chase-Lansdale, 2000); affective and behavioral disorders (Salguero & McCusker, 1996); and school dropout (Harvard Civil Rights Project, 2004) of urban adolescents. Other research has found that urban environment and poverty are related to the development of a restricted and pessimistic future orientation (McCabe & Barnett, 2000), which is a predictor of decreased SWB. Thus, to develop interventions that enhance SWB for this population, it is important to determine which factors appear to be most relevant to SWB in urban youth of color, since theories based on majority populations may not be applicable (Sue & Constantine, 2003).

Perceived Social SupportExisting studies have shown that external psychological resources such as social support play an important role in adolescent well-being (Edwards & Lopez, 2006). For example, Edwards and Lopez found that for Latino middle and high school students, perceived family support was the strongest predictor of life satisfaction. This study is consistent with other research on non-European adolescents, such as Asian youth, strongly identifying with and having attachment to both nuclear and extended family (Sue & Sue, 1999). Since family functions as a natural support system throughout the lifetime in these cultures, examining social support in relationship to SWB is crucial. Peer, family, and school environments have also been found to have different effects on adolescent perception of social support (Caldwell, Silverman, Lefforge, & Silver, 2004). For example, Suldo and Huber (2004) found that support from parents was the strongest predictor of life satisfaction in a large sample of adolescents, although the relationship decreased with increased age of the adolescent. Similarly, other studies have found that negative perceptions of support from school, peers, and parents have been related to a variety of negative outcomes in adolescents (Farrington, Loeber, Elliott, Hawkins, &




Kandel, 1990). Therefore it seems important to separately investigate social support coming from these different environments.

Self-EsteemFor several decades, the literature has shown that self-esteem is correlated with happiness (Wilson, 1967), and self-satisfaction has been shown to be the highest predictor of life satisfaction (Campbell, Converse, & Rodgers, 1976). More recently, self-esteem has been established as a significant contributing factor to SWB (Diener, 1984) in Western samples (Lucas et al., 1996). Not only has self-esteem been shown to be a direct influence on SWB, but also to indirectly influence other factors, such as exercise, which have a direct influ-ence on SWB (Elavsky et al., 2005). In adolescent populations, self-esteem and SWB in adolescents, peer, family, and school contexts have been found to be differentially related to adolescents’ view of the self (Caldwell et al., 2004). For example, feeling good about oneself in the context of school (i.e., school self-esteem) has been found to be related to overall self-esteem, but this relationship is moderated by race, whereas feeling good about oneself in one’s family (i.e., family self-esteem) has been found to be more predictive of general self-esteem, regardless of race (Hare, 1979).

Neighborhood ConditionsTrue to developmental frameworks emphasizing the context in which an individual exists (Bronfenbrenner, 1979), research has begun to show that an accumulation of systemic factors, rather than only individual factors, affect SWB (Brooks-Gunn, Klebanov, & Liaw, 1994; Rutter, 1989). Accordingly, researchers have begun to look at external psychological resources that are theoretically more distal as a part of the overall contribution to SWB (Meyers & Miller, 2004). One external aspect which has been studied is the effect of neighborhood environment.

Neighborhood environment literature dates back to sociologists’ study of social disorganization theory as a model for understanding delinquency behaviors among youth. This theory posits that maintenance of public order in a neighborhood is correlated with factors such as poverty, residential insta-bility, and ethnic heterogeneity (Leventhal & Brooks-Gunn, 2000). Many studies have investigated various aspects of neighborhoods to determine their impact (Meyers & Miller, 2004). Neighborhood aspects such as availability of parks and libraries, presence of role models, negative behavior of peers in the neighborhood, competition among neighbors for scarce resources, and



Morgan et al. 613

neighbors’ evaluation of themselves relative to others have all been linked to behaviors of individual residents (Jencks & Mayer, 1990).

Such neighborhood variables have been correlated with multiple specific outcomes, for example, teen pregnancy rates (Hogan & Kitagawa, 1985); IQ, verbal ability, and behavior problems (Klebanov, Brooks-Gunn, Chase-Lansdale, & Gordon, 1997); physical health and child injury (Durkin, Davidson, Kuhn, O’Connor, & Barlow, 1994); adolescents’ overall mental health (Leventhal & Brooks-Gunn, 2000); and psychological well-being and school problems (Meyers & Miller, 2004). Some results have varied by gender, as in Ceballo, McLoyd, and Toyokawa’s (2004) study of African American adolescents, where it was found that neighborhood factors affected educational values among girls but not boys. For all residents, residing in a low-income neighborhood has been related to poor neighborhood cohesion and community disengagement, which can lead to decreased individual resil-ience (Brodsky, 1996). Neighborhood factors in general have yet to be studied extensively in adolescent populations as correlates of SWB.

Sense of CommunitySense of community is a related construct thought to be comprised of four dimensions: (a) needs fulfillment, or the belief that the community will meet an individual’s needs; (b) membership, or the feeling of belonging to a neigh-borhood; (c) influence, or the perception that one can make a difference in the neighborhood; and (d) emotional connection, or the feeling of shared his-tory and place among neighbors (McMillan & Chavis, 1986).

Correlates of sense of community found in the existing literature include political participation, local action (Chavis & Wandersman, 1990), and SWB (Davidson & Cotter, 1991). In a study in Italy, Prezza and Constantini (1998) found that these correlations are even stronger when looking at a smaller community, even if that community is a part of a larger urban area. Thus, feelings about a specific neighborhood may affect SWB even more signifi-cantly. It is yet to be established how sense of community impacts SWB for adolescents in the United States.

Studies have shown that residents’ perception of their community as disad-vantaged leads to many negative consequences. Among these consequences are that residents form fewer relationships with neighbors and community values are less upheld (Kowaleski-Jones, 2000), resident life variables such as the completion of high school, career plans, and family responsibility are also negatively affected (Ge, Conger, & Elder, 2001), and residents may perceive that they have poor chances of controlling their lives or succeeding (Wilson,




1991). In such environments, adolescents specifically are less likely to find positive role models and/or are more likely to emulate behaviors that may negatively affect their lives (i.e., early sexual activities, early pregnancies; Wickrama, Conger, Wallace, & Elder, 1999). Meyers and Miller (2004) found that when parents perceive their families as living in high-risk neighborhoods, their children were significantly more likely to have psychological difficulties and school problems, regardless of gender, ethnicity, or area of the country in which they lived. Thus, sense of community may be particularly important to understanding SWB in adolescents. To date, little research has been conducted on how adolescents’ sense of community might affect their SWB.

Purpose of the Present StudyThe current study examined the relationships between hope, optimism, self-esteem, social support, neighborhood perceptions, and subjective well-being in ethnically diverse, urban adolescents. Predictor variables were selected from among each of the identified categories of SWB factors (Lent, 2004): demographic (gender), personality (self-esteem, optimism), and acquirable skills/resources (hope, social support, neighborhood conditions, sense of support). It was hypothesized that, in accordance with Bronfenbrenner’s eco-logical model (1979), more distal variables such as neighborhood and community may have less of an impact on SWB than more proximal indi-vidual variables such as hope or self-esteem. However, because proponents of ecological models of human development (e.g., Bronfenbrenner, 1979; Garbarino, 2001) believe that even the most distal factors “trickle down” and affect the psychological functioning of children and families, it was impor-tant for the current examination of SWB in urban adolescents to be as comprehensive as possible. Given that the majority of extant literature has focused primarily on proximal individual predictors of SWB, this study was designed to examine the relative importance of such factors in the context of a more systemic conceptualization.

Research QuestionsThe following research questions were addressed: (a) Are there signifi-cant relationships between self-esteem, social support, hope, optimism, and neighborhood as predictors of subjective well-being (as measured by positive affect, negative affect, and life satisfaction)? (b) Do individual, family, friend, school, and neighborhood variables significantly predict positive affect? (c) Do individual, family, friend, school, and neighborhood variables significantly



Morgan et al. 615

predict negative affect? (d) Do individual, family, friend, school, and neigh-borhood variables significantly predict life satisfaction?

MethodParticipants

Participants in the study were 159 English-speaking, culturally diverse, 7th and 8th graders at an urban elementary school in a large, Midwestern city. Fifty-six percent of the students were male and 42.1% were female. They had a mean age of 13.41 years (SD = 0.8, range = 12 to 16 years). The majority of the stu-dents self-identified as Latino/a (57.9%), with 17.6% identifying as Asian, 5.7% as Black, 5% as Mixed race or ethnicity, 3.1% as White, 2.5% as Other, and 0.6% as Native American, and 7.5% did not report their race or ethnicity.

According to the public state records, the study’s sample roughly reflects the demographic profile of the school as a whole (68.4% Hispanics, 10.5% Black, 9.7% White, 11.3% Asian American, and less than 1% Native Ameri-can). Eighty-seven percent of the students enrolled in the school are from low-income family (i.e., students who come from families whose incomes qualified for free breakfast and lunch programs in school).

ProcedureParticipants were recruited for the study through their school in combination with a school-based outreach program aimed at enhancing decision-making skills, career aspirations, and identity exploration. The program was designed to support the students’ transition and adjustment through middle school years and into high school, which is a prime time for specific risk situations to appear for many urban students (e.g., school dropout, gang involvement). The out-reach program is run through a collaboration between the researchers and the 7th- and 8th-grade teachers and school administration. Data are collected through this program to tailor the focus of the outreach (e.g., anxiety about entering high school might inform programming around this topic for a future outreach session). The data gathered for the current study were collected prior to the onset of any interventions.

All of the students in the 7th and 8th grades of the school were eligible for participation in the outreach program and research component, and all pres-ent at school on the day of data collection chose to participate; therefore, the sample is representative of the entire 7th- and 8th-grade population at the school. Prior to the beginning of the program, parents and/or guardians of




the participants were sent letters describing the outreach program and research component and asked to sign a written consent for their child to participate in both components. Participant assents were worded similarly. None of the parental consents prevented children from participating in either component of the program. None of the assents indicated a refusal to partici-pate in the study or the program.

The participants responded to the research survey approximately 1 week prior to their participation in the outreach program in their home room classes, during the school day, as was requested by the school administrators and teachers. This was consistent with the structure of all of the subsequent outreach program activities, which were also conducted during the school day to maximize attendance. Average survey administration took 30 minutes. Surveys were read aloud for students to control for varying reading abilities. Research team members were present to answer any questions that partici-pants had during the survey administration.

InstrumentsDemographic questionnaire. The demographic questionnaire included ques-

tions on the students’ gender, age, and race.

Criterion VariablesPositive and Negative Affect Scale (PANAS). The PANAS is a 20-item scale

that measures positive and negative affect. Its two subscales, one for posi-tive affect and one for negative affect, are each composed of 10 items (Watson, Clark, & Tellegen, 1988). The PANAS can be administered with different instructions which reflect different time frames (e.g., state vs. trait versions). In the present study, participants were asked to indicate the degree to which each of the items was generally experienced (reflecting the trait version). Respondents are asked to indicate how often they feel certain emo-tions (e.g., “interested” or “stressed”) on a 5-point scale from never to all the time. Total scores for each subscale range from 10 to 50, with higher scores reflecting higher levels of trait PANAS positive affect (PA) or PANAS nega-tive affect (NA).

The PANAS has been used in previous studies with samples of adoles-cents in the United States (Crocker, 1997). Test–retest reliability coefficients for a 7-month period (Lonigan, Phillips, & Hooe, 2003) were generally con-sistent to those found in adult samples, with coefficients ranging from .64 to



Morgan et al. 617

.77 for the PA scale and .53 to .66 for the NA scale. In the current study, the internal reliability estimate was .83 for the PA scale and .81 for the NA scale.

The Satisfaction with Life Scale (SLS). The SLS is a 5-item general measure of satisfaction with the quality of one’s life (e.g., I feel my life is close to perfect; If I could live my life over, I would change nothing; Diener, Emmons, Larsen, & Griffin, 1985). Individuals were asked to respond to the items using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Total scores are computed by summing the responses, resulting in possible scores ranging from 5 to 35 with higher scores indicating greater life satisfac-tion. Internal consistency estimates for scores on this measure were initially reported as .87, with test–retest reliability estimates (2-month interval) of .82 (Diener et al., 1985). Numerous studies since the initial study have reported reliability estimates which consistently range from .80 to .89, including scores from individuals of various ages and ethnicities (Pavot & Diener, 1993). In the current study, the internal reliability estimate was .82.

Predictor VariablesChildren’s Hope Scale (CHS). The CHS is a 6-item self-report measure

developed to examine one’s ability to obtain future goals (e.g., believing that one knows how to get what one wants in life; Snyder et al., 1997). Scores range from 6 to 36, with higher scores indicating greater hopefulness. The CHS was originally administered to several populations of children ranging in age from 7 to 17, including a sample of White and Latino public school children. Results for the sample in the original study indicated an internal reliability estimate of .86. In further studies, internal consistency scores ranged from .72 to .86, and 1 month test–retest reliabilities ranged from .71 to .73 (Snyder et al., 1997). Results indicated an internal reliability estimate of .86 for the current sample.

Hare Area-Specific Self-Esteem Scale. The Hare is a 10-item index measuring respondents’ perceptions of their worth and importance among their peers, their family, and in the school environment as well as giving a general com-posite score for self-esteem (Hare, 1979). Scores range from 10 to 40 with higher scores indicating greater composite self-esteem. Each item consists of a statement with which the participant can strongly disagree, disagree, agree, or strongly agree. A sample item is “My parents are proud of the kind of person I am.” Past reliability estimates of the original Hare scale in studies for African American adolescents were .53 for the peer subscale, .60 to for the home subscale, and .61 for the school subscale (Spencer, Dupree, Swanson, Phillips, & Cunningham, 1996). In validity studies, three separate constructs




for peer, home, and school self-esteem have been found (Shoemaker, 1980). Results of the present study indicated an internal reliability estimate for general self-esteem of .75, .74 for the family subscale, .64 for the peer sub-scale, and .68 for the school subscale. The Hare is the only multidimensional scale of self-esteem that exists in the literature, and as the authors of the current study had used the Hare in previous research and found the reliabil-ity estimates to be better than those found in past research, it was determined that the scale was appropriate for the current investigation.

Vaux Social Support Record (VSSR). The VSSR is an adaptation of Vaux’s (1986) Social Support Appraisals (SSA) 23-item scale that was designed to assess the degree to which a person feels cared for, respected, and involved (Vaux, 1986). The VSSR is a nine-item questionnaire consisting of three sub-scales that measure social support (a respondent’s perceptions of the availability of emotional advice, guidance, and practical support) in family, peer, and school contexts. Each of the items is evaluated on a 3-point scale (not at all, some, a lot) with scores ranging from 0 to 2 (and total scores rang-ing from 0 to 18), with higher scores representing higher perceptions of support and lower scores representing lower perceptions of social support. Good internal consistency estimates for the total, family, and peer scales were demonstrated by Vaux (1986) with older adolescent samples (mean a = .90, .80, and .84, respectively) and community samples (mean a = .90, .81, and .84, respectively). The development sample used for this measure included students in Grades 1 through 6. The internal reliability estimate for the current sample was .78.

Life Orientation Test (LOT). The LOT measures optimism or one’s expecta-tions about the potential to experience positive things in life (e.g., looking on the bright side of things, expecting good things to come out of bad situations; Scheier & Carver, 1985). Scores range from 0 to 32. The LOT includes four negative and four positive items, such as “I always look on the bright side of things” and “If something can go wrong for me it will.” Scores range from 0 to 32, and participants respond to a 5-point Likert-type scale with options ranging from 0 (strongly agree) to 4 (strongly disagree). Scheier and Carver reported an internal consistency estimate of .76 and a test–retest correlation of .79 over a 4-week period for college students (Scheier & Carver, 1985). The LOT has since been used in studies with seventh- and eight-grade adolescents, with Cronbach’s alphas of .78 and .76 reported (Mahon, Yarcheski & Yarcheski, 2004). The internal reliability estimate for the current sample was .53.

Sense of Community Index (SCI). The SCI measures psychological sense of community or feeling that members have of belonging and being important to each other (Long & Perkins, 2003). Responses are given on a 3-point



Morgan et al. 619

Likert-type scale (1 = not at all true to 3 = always true) to 12 items. The initial internal reliability estimate was .80 for the entire scale (Perkins, Florin, Rich, Wandersman, & Chavis, 1990). The current study yielded a reliability estimate of .63 for sense of community in school and .71 for sense of com-munity in neighborhood.

SCI scores have been found to be positively related to several variables, including length of residence, satisfaction and informal social control, politi-cal participation and community involvement (Hughey, Speer, & Peterson, 1999), and well-being. It has been found to be applicable with adolescents (Pretty, Conroy, Dugay, Fowler, & Williams, 1996).

Block Booster Environmental Inventory (BBEI). The Perceived Crime/Delinquency Problems and Perceived Incivilities subscales of the Perception of Block Problems Scale of the BBEI were used to measure perceived neigh-borhood problems (Perkins et al., 1990). The Perceived Crime/Delinquency Problems subscale is a 7-item instrument in which respondents indicate the degree to which they perceive crime and delinquency as problematic in their neighborhood. The Perceived Incivilities subscale is a 6-item instrument in which participants rate the degree to which they perceive incivilities in their neighborhood as problematic. Both subscales have choices from 1 (a serious problem on my block) to 3 (no problem at all on my block). Total scores for the Perceived Incivilities subscale range from 6 to 18. For both subscales, higher mean values represent greater levels of perceived problems in one’s neighborhood conditions. A coefficient alpha of .78 for the Perceived Crime/ Delinquency Problems subscale and .65 for Perceived Incivilities subscale were reported in the initial study (Perkins et al., 1990). In the present study, Cronbach’s alpha was .84 for Perceived Crime/Delinquency and .64 for Inci-vilities subscales.

ResultsThe means and standard deviations for all variables, namely, individual (hope and life orientation), family (self-esteem and social support), school (social support, school self-esteem, and sense of community in school) friends (self-esteem with friends, social support with friends) and neighborhood (sense of community in neighborhood and neighborhood conditions), are displayed in Table 1. To address the first research question and determine the relationships between the variables, Pearson correlation coefficients were calculated (also presented in Table 1). With regard to SWB criterion variables, positive affect was positively correlated with satisfaction with life (r = .26 p < .01) but not correlated with negative affect. Satisfaction with life was negatively



Tabl

e 1.

Mea

ns, S

tand

ard

Dev

iatio

ns, a

nd C

orre

latio

ns a

mon

g Var

iabl

es

PA

N

A

Hop

e LO

T

Har

e H

FE

HH

E H

SE

VSS

R

VSS

V

FS

VPS

SL

S SC

IS

SCIN

N

BC

PA

N

A

.135

H

ope

.521

** -.

024

LO

T

.316

** -.

186*

.4

30**

H

are

.325

** -.

367*

* .5

21**

.3

70**

HFE

.1

95*

-.24

3**

.278

**

.243

**

.696

**

HH

E .2

87**

-.

340*

* .4

48**

.3

33**

.8

25**

.2

82**

HSE

.3

55**

-.

207*

.5

20**

.3

45**

.6

67**

.4

34**

.3

82**

V

SSR

.4

08**

.0

65

.430

**

.208

* .2

69**

.1

75*

.206

* .3

36**

VSS

.1

76*

.120

.2

29**

.0

35

.063

.1

23

-.01

7 .1

34

.712

**

VFS

.2

94**

-.

197*

.3

65**

.2

96**

.3

72**

.1

23

.395

**

.354

**

.708

**

.241

**

V

PS

.401

**

.168

* .3

36**

.1

37

.144

.1

12

.071

.2

31**

.7

74**

.3

29**

.3

52**

SL

S .2

64**

-.

330*

* .4

81**

.4

80**

.4

91**

.3

50**

.4

33**

.4

22**

.4

01**

.2

00*

.438

**

.226

**

SC

I S

.440

**

.001

.3

57**

.2

27**

.2

46**

.2

18*

.167

.2

84**

.5

69**

.3

53**

.4

16**

.4

76**

.2

62**

SC

I N

.323

**

.064

.3

26**

.1

95*

.259

**

.188

* .2

45**

.2

63**

.4

29**

.2

37**

.3

30**

.3

46**

.1

56

.308

**

N

BC

.025

.1

72*

.019

.0

10

-.14

6 -.

031

-.22

6**

.030

-.

151

-.08

8 -.

155

-.06

8 -.

025

-.08

3 -.

156

M

32.9

8 25

.96

23.1

9 20

.83

28.3

9 8.

86

13.2

2 8.

94

11.5

3 3.

20

4.44

3.

83

23.8

1 15

.00

13.1

5 21

.82

SD

6.80

5.

99

5.70

3.

46

4.29

1.

82

2.65

1.

72

3.58

1.

61

1.60

1.

69

6.73

2.

14

2.43

5.

78

Not

e: P

A =

PA

NA

S Po

sitiv

e A

ffect

; NA

= P

AN

AS

Neg

ativ

e A

ffect

; Hop

e =

Chi

ldre

n’s

Hop

e Sc

ale;

LO

T =

Life

Ori

enta

tion

Test

; Har

e =

Har

e A

rea-

Spe-

cific

Sel

f-Est

eem

Sch

edul

e; H

FE =

Har

e Fr

iend

s; H

HE

= H

are

Hom

e; H

SE =

Har

e Sc

hool

; VSS

R =

Vau

x So

cial

Sup

port

Rec

ord;

SLS

= S

atis

fact

ion

with

Li

fe S

cale

; SC

I = S

ense

of C

omm

unity

Inde

x; N

BC =

Nei

ghbo

rhoo

d Bl

ock

Con

ditio

ns S

cale

; VSS

= V

aux

Scho

ol S

uppo

rt; V

FS =

Vau

x Fa

mily

Sup

port

; V

PS =

Vau

x Pe

er S

uppo

rt; S

CIS

= S

ense

of C

omm

unity

Inde

x -

Scho

ol; S

CIN

= S

ense

of C

omm

unity

Inde

x –

Nei

ghbo

rhoo

d*p

< .0

5. *

*p <

.01.

620



Morgan et al. 621

correlated with negative affect (r = –.33, p < .001). Positive affect correlated with all of the predictor variables except neighborhood block conditions, sat-isfaction with life correlated with all predictor variables except sense of community and neighborhood block conditions. Negative affect negatively correlated with all self-esteem variables, and perceived social support from family, and positively correlated with social support from friends and neigh-borhood block conditions (see Table 1).

To test equality of means based on the reported ethnicity of the students, a oneway ANOVA was run. The results from the ANOVA showed significant differences among ethnic groups for the criterion variable of negative affect (F = 2.4, p = .04) and predictor variables of optimism (F = 2.28, p = .05) and self-esteem derived from friends (F = 2.81. p = .02). Tukey’s HSD tests revealed that significant differences for negative affect were between African Americans and Asian Americans (p = .05), significant differences for opti-mism were between African Americans and Latino/as (p = .02), and significant differences for self-esteem derived from friends were between African Americans and Asian Americans (p = .02). Another oneway ANOVA revealed gender differences on four of the variables: negative affect (F = 9.81, p = .002), social support in school (F = 6.79, p = .010), social support by friends (F = 18.74, p = .000) and overall social support (F = 10.33, p = .002). For each of these variables, girls had higher scores than boys.

To address the second research question, multiple hierarchical regression analyses were conducted to examine whether neighborhood variables signifi-cantly predicted SWB above and beyond other variables. Life satisfaction, positive affect, and negative affect were each examined separately. For each regression with the exception of negative affect, the individual variables of optimism and hope were entered first, family social support and self-esteem second, school social support and self-esteem third, friend social support and self-esteem fourth, and the neighborhood variables of sense of community and neighborhood conditions fifth. Gender was added as a predictor variable to the negative affect regression equation because of the gender difference noted in previous ANOVAS. The variance explained by each model was examined, and the significance of changes in explained variance with subse-quent models was determined.

To determine the adequacy of our sample size, a power analysis was cal-culated for the hierarchical multiple regression analyses for the test of unique contributions of the family variables to the SWB criteria (Cohen & Cohen, 1983). The power analysis indicated that a sample of 130 yields a power of .80 when alpha is set at .05, and the incremental contributions of the family




variables to the overall SWB criteria is assumed to be .08 based on prior research.

Predictors of Life SatisfactionIn the first hierarchical regression equation, optimism and hope accounted for 31% of the variance in life satisfaction. The addition of family variables increased the variance by 9% and raised the level of explained variance to 40%, F change(2, 107) = .00. There was no significant change in the expla-nation of variance after the addition of school variables, variance = 43%, F change(3, 102) = .28, friend variables, variance = 44%, F change(2, 100) = .23, or neighborhood variables, variance = 46%, F change(2, 98) = .19. These variables did not account for a significant amount of incremental variance in the life satisfaction variable. See Table 2 for a summary of these results.

Predictors of Positive AffectIn the second hierarchical regression equation, predictor variables of positive affect were examined. Hope and optimism accounted for 33% of the variance in positive affect. The addition of family variables in the second model was not significant, variance = 35%, F change(2, 107) = .34, but the third model, which added school variables, increased the variance by 6% and raised the level of explained variance to 41%, F change(3, 104) = .02. The addition of friend variables increased the variance by 4%, raising the level of explained variance to 45%, F change(2, 102) = .03. The fifth model, which added neighborhood variables, did not significantly account for additional variance, variance = 45%, F change(2, 100) = .41. Table 3 summarizes these results.

Predictors of Negative AffectThe third hierarchical regression equation examined predictor variables for negative affect. Gender explained 6% of the variance for negative affect, a statistically significant amount, F(1, 112)= 7.5. Hope and optimism did not account for a significant amount of the variance (3%), F change(2, 110) = 1.52, The addition of family variables was significant, adding 11%, and accounting for 19% of the variance, F change(2, 108) = 6.73. None of the remaining additions—school, variance = 24%, F change(3, 105) = .075; friends, variance = 27%, F change(2, 103), = .128; or neighborhood, variance = 31%, F change(2, 101) = .063—accounted for a significant amount of the incremental variance. See Table 4 for a summary of these results.



Morgan et al. 623

Table 2. Summary of Hierarchical Regression Analyses Predicting Life Satisfaction (N = 159)

Variable B SEB Beta

Step 1 LOT 0.74 .17 .39** Hope 0.29 .10 .26*Step 2 LOT 0.59 .16 .31** Hope 0.07 .11 .06 Hare home 0.44 .25 .18 Vaux family 1.17 .42 .27Step 3 LOT 0.56 .16 .29** Hope -0.03 .12 -.03 Hare home 0.43 .25 .17 Vaux family 1.10 .43 .25* Vaux school 0.14 .34 .03 Hare school 0.60 .34 .16 SCIS 0.13 .26 .04Step 4 LOT 0.56 .17 .30** Hope -0.06 .12 -.05 Hare home 0.43 .25 .17 Vaux family 1.05 .43 2.44* Vaux school 0.08 .34 .02 Hare school 0.47 .36 .13 SCIS -0.01 .27 -.01 Hare friends 0.32 .30 .09 Vaux friends 0.44 .34 .11Step 5 LOT .55 .17 .29** Hope -.06 .12 -.05 Hare home .53 .26 .21 Vaux family 1.10 .43 .25** Vaux school .15 .34 .04 Hare school .54 .36 .14 SCIS -.02 .27 -.01 Hare friends .29 .30 .08 Vaux friends .56 .34 .14 SCIN -.35 .23 -.12 NBC .09 .06 .08

Note: Step 1: R2 = .31, DR2 = .31; Step 2: R2 = .40, DR2 = .10; Step 3: R2 = .42, DR2 = .02; Step 4: R2 = .44, DR2 = .02; Step 5: R2 = .46, DR2 = .02.*p < .05. **p < .01.




Table 3. Summary of Hierarchical Regression Analyses Predicting PANAS Positive Affect (N = 159)

Variable B SEB Beta

Step 1 LOT .29 .16 .16 Hope .54 .10 .49**Step 2 LOT .25 .16 .14 Hope .47 .11 .43** Hare home .33 .25 .13 Vaux family .06 .40 .02Step 3 LOT .22 .16 .12 Hope .37 .12 .33** Hare home .36 .24 .15 Vaux family -.20 .40 -.05 Vaux school -.07 .33 -.02 Hare school .28 .33 .08 SCIS .75 .26 .25**Step 4 LOT .29 .16 .16 Hope .30 .12 .27** Hare home .38 .24 .15 Vaux family -.34 .40 -.08 Vaux school -.13 .32 -.03 Hare school .21 .34 .06 SCIS .53 .26 .17 Hare friends .12 .29 .04 Vaux friends .87 .33 .23*Step 5 LOT .27 .16 .15 Hope .28 .12 .26* Hare home .42 .25 .17 Vaux family -.37 .40 -.09 Vaux school -.14 .32 -.04 Hare school .14 .34 .04 SCIS .52 .26 .17* Hare friends .14 .29 .04 Vaux friends .84 .34 .22 SCIN .23 .22 .09 NBC .08 .08 .07

Note: Step 1: R2 = .33, DR2 = .33**; Step 2: R2 = .35, DR2 = .02; Step 3: R2 = .41, DR2 = .06*; Step 4: R2 = .45, DR2 = .04*; Step 5: R2 = .45, DR2 = .01.*p < .05. **p < .01.



Morgan et al. 625

Table 4. Summary of Hierarchical Regression Analysis of Predictors of PANAS negative affect (N = 159)

Variable B SEB Beta

Step 1 Gender 2.96 1.62 .25Step 2 Hope 0.01 0.11 .01 LOT -0.31 0.18 -.18Step 3 Hope 0.24 0.12 .23* LOT -0.17 0.17 -.10 Hare home -0.72 0.26 -.32** Vaux family -0.70 0.43 -.18Step 4 Hope 0.30 0.13 .30* LOT -0.08 0.17 -.05 Hare home -0.68 0.26 -.30* Vaux family -0.71 0.43 -.18 Vaux school 0.43 0.35 .11 SCIS 0.13 0.27 .05 Hare school -0.85 0.36 -.25*Step 5 Hope 0.26 0.13 .25* LOT -0.02 0.17 -.01 Hare home -0.60 0.26 -.26 Vaux family -0.87 0.44 -.22* Vaux school 0.4 0.35 .12 SCIS 0.09 0.28 .03 Hare school -0.68 0.37 -.20 Hare friends -0.52 0.31 -.16 Vaux friends 0.48 0.36 .13Step 6 Hope 0.22 0.12 .22 LOT -0.05 0.17 -.03 Hare home -0.52 0.27 -.23 Vaux family -0.92 0.43 -.23 Vaux school 0.42 0.35 .11 SCIS 0.08 0.28 .03 Hare school -0.80 0.37 -.24* Hare friends -0.51 0.31 -.16 Vaux friends 0.39 0.36 .11 NBC 0.14 0.09 .14 SCIN 0.44 0.24 .18

Note: Step 1: R2 = .06, DR2 = .06**; Step 2: R2 = .09, DR2 = .03; Step 3: R2 = .19, DR2 = .10**; Step 4: R2 = .24, DR2 = .05*; Step 5: R2 = .27, DR2 = .03; Step 6: R2 = .31, DR2 = .05*.*p < .05. **p < .01.




Overall, hierarchical regression results indicated that the individual vari-ables of hope and optimism significantly predicted life satisfaction and positive affect. Family-derived self-esteem and social support significantly predicted life satisfaction and negative affect. School-derived self-esteem, social support, and sense of community significantly predicted positive affect. Friend-derived self-esteem and social support significantly predicted positive affect, and neighborhood variables did not significantly predict any of the criterion variables for SWB.

DiscussionFindings of the current study confirmed most predictors as significant in explaining variance in the SWB variables. Individual, family, school, and friend variables were each significant in determining SWB for culturally diverse, urban adolescents. The study did not find support for neighborhood variables (i.e., sense of community, neighborhood block conditions) as sig-nificant predictors of life satisfaction, positive affect, and negative affect overall. This is in contrast to some of the literature which found that sense of community and perceived neighborhood conditions affect individual behavior and dispositions. Given that neighborhood variables are the most distal variables in relationship to the adolescent, it may be that the effect on their lives is minimal in comparison to more immediate personal, social, and support variables. It is also possible that urban adolescents may become desensitized to effects of their neighborhood. Furthermore, this finding sup-ports the idea that in urban, low-income neighborhoods, being disconnected from community (i.e., not perceiving the negativity in one’s environment) may serve a protective function (Brodsky, 1996). There was some indica-tion in the present study that this may vary by gender (i.e., boys are affected more negatively by poor neighborhood environment), but this area is in need of further research.

Family variables were significant in the prediction of overall life satisfac-tion and negative affect. This is consistent with past findings of the importance of family variables to life satisfaction in adolescent populations (Suldo & Huber, 2004) and Latino/a populations, specifically (Edwards & Lopez, 2006). The fact that the addition of variables beyond individual and family had no significant effect highlights the importance of individual resources such as hope and optimism and family above and beyond other variables. If a family is having a negative impact on the adolescent, this is likely much more salient and identifiable to the adolescent himself or herself than if the family is having a positive impact, which could be attributed to other variables such as individual variables or friends.



Morgan et al. 627

School and friend variables significantly predicted only positive affect. Variables such as school and friends may affect the adolescent’s more day-to-day mood (i.e., a positive experience at school makes him or her temporarily happy) but not necessarily affect overall life satisfaction like the more imme-diate family variables. This is consistent with existing research findings for Latino/a samples. For example, Holleran and Waller (2003) used ethno-graphic interviews of Chicano/as from a barrio in a large Southwestern city, ranging in age from 13 to 18 years old. Their data revealed a central theme of familismo (the importance of family) in the protection of adolescents and young adults from racist environments, gang activity, and high-risk behav-iors such as violence and drop-out. Narratives described the family as an oasis from hostile and unfulfilling environments as well as a source of strength. Peer support was also found to affect constructs related to SWB, such as resilience, but was not described as central (Holleran & Waller, 2003). Although the relationship needs to be clarified, it may be that the place of family in a child’s life, particularly if the child is Latino/a, provides an overall source of positive affect, whereas peer variables, although important, are not as important as the role of family in predicting SWB.

It is interesting that positive affect and negative affect had no relationship with one another in this study, which has been found to be the case in other studies (Emmon & Diener, 1985; Lent, 2004). This may imply that infrequent negative affect is not an automatic indication of frequent positive affect. In other words, adolescents who do not report feeling sad or frustrated are not necessarily happy and might still benefit from SWB promotion interventions. However, it is also important to keep in mind developmental differences that may affect the fluctuation of mood states in adolescents. For example, frequency of emotional states may be more influenced by day-to-day events that are more dynamic and inconsistent for youth of this age. Thus longitudinal research on the stability of negative and positive affect in urban adolescents of color would be very valuable.

The overall findings of this study contribute to the literature in that there have not been studies to date looking at the three components of the tripartite model of subjective well-being in relationship to individual, family, school, friend, and neighborhood variables in adolescents, as is consistent with eco-logical theories of development (Bronfenbrenner, 1979). It is important to include positive and negative affect in studies of SWB, as the current results indicate that these components may be affected more by distal variables such as school and friends, whereas overall life satisfaction may be more affected by more immediate variables such as individual and family.

Several clinical implications can be derived from this study. Pinpointing the types of variables that may impact certain types of affect allows them to




be more easily addressed in therapy. Preventive work with adolescents, focus-ing on building positive expectations for the future, goal-setting, and problem solving may help to increase overall SWB. Similarly, preventive psycho-education and cohesion-building activities for families, which would create a positive family impact on affective behaviors, could be instrumental to increasing the overall SWB of adolescents, as indicated by previous research (Larson, 2000). Friend and school environments can be addressed through peer-based programs targeting communication, relationships, and coping. Working with teachers and school officials to enhance school support for adolescents is also critical. According to the findings of this study, it is more important to focus on these aspects of an adolescent’s life rather than their broader environment.

Limitations and Areas of Future ResearchSeveral limitations should be taken into account in the interpretation of these results. First of all, the size of the sample was limited and not equally represen-tative of each race and ethnicity, or of gender. Therefore, any possibility for comparisons and/or contrasts between subgroups is quite limited. Future areas of study would include a larger sample and more equal ethnic representation, which would allow for closer examination of ethnic and gender differences. Similarly, a second limitation is that the results are not necessarily generaliz-able to urban adolescents in other settings as all participants attended the same school. Third, all data were collected by self-report, which increases the pos-sibility of different interpretations of questions by different individuals. Fourth, all of the participants did not necessarily live in the same neighborhood, and therefore any comparisons of neighborhood perceptions could not be made. A future study might focus on one specific neighborhood environment and there-fore gain more detailed and specific information about perceptions. Fifth, scores on some of the instruments did not demonstrate strong reliability (i.e., measures of optimism, sense of community in school, neighborhood incivili-ties, and peer self-esteem scores all demonstrated reliability estimates below .70), which would result in an underestimation of the true relationships that may exist between the study’s variables. Finally, the current data provided a snapshot of adolescents’ views at a given time. It would be interesting to con-duct a more longitudinal study to better gauge the consistency of adolescent’s perceptions of each of the variables over time, which may help in better under-standing how protective factors can be increased in the lives of adolescents.

More positively focused research is needed to inform professionals in the field about adolescent competencies and resources that both foster healthy



Morgan et al. 629

developmental outcomes and prevent a host of mental health problems in urban youths (Larson, 2000). Such a shift would reflect an acknowledgment that even youth who do not exhibit diagnosable problems may not necessar-ily grow up to become happy, adjusted, or productive adults (Larson, 2000).

ConclusionThe current study served to highlight the importance of more immediate aspects of an urban adolescent’s world such as family and school to SWB, in contrast to more distal variables such as neighborhood. In addition, differ-ences in predictors of each component of SWB were illuminated. This is one of few studies to examine multiple contextual predictors of SWB simultane-ously in this population. Future research is needed to more clearly understand the differences that culture may bring to bear on the understanding of subjec-tive well-being in adolescents.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding

The authors declared no financial support for the research and/or authorship of this article.

References

Ben-Zur, H. (2003). Happy adolescents: The link between subjective well-being, inter-nal resources, and parental factors. Journal of Youth & Adolescence, 32, 67-79.

Brodsky, A. E. (1996). Resilient single mothers in risky neighborhoods: Negative psy-chological sense of community. Journal of Community Psychology, 24, 347-363.

Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, MA: Harvard University Press.

Brooks-Gunn, J., Klebanov, P. K., & Liaw, F. (1994). The learning, physical, and emotional environment of the home in the context of poverty: The Infant Health and Development Program. Children and Youth Services Review, 17, 251-276.

Caldwell, R. M., Silverman, J., Lefforge, N., & Silver, N. C. (2004). Adjudicated Mexican American adolescents: The effects of familial emotional support on self-esteem, emotional well-being, and delinquency. American Journal of Family Therapy, 32, 55-69.

Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life. New York: Russell Sage.




Ceballo, R., McLoyd, V., & Toyokawa, T. (2004). The influence of neighborhood quality on adolescent’s educational values and school effort. Journal of Adoles-cent Research, 19, 716-739.

Chavis, D. M., & Wandersman, A. (1990). Sense of community in the urban environ-ment: A catalyst for participation and community development. American Journal of Community Psychology, 18, 55-82.

Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum.

Coley, R. L., Kuta, A. M., & Chase-Lansdale, P. L. (2000). An insider view: Knowl-edge and opinions of welfare from African American girls in poverty. Journal of Social Issues, 56, 707-726.

Crocker, P. R. E. (1997). A confirmatory factor analysis of the Positive and Negative Affect Schedule (PANAS) with a youth sport sample. Journal of Sports and Exer-cise Psychology, 19, 91-97.

Davidson, W.B. & Cotter, P.R. (1991). The relationship between sense of community and subjective well-being: A first look. Journal of Community Psychology, 19(3), 246-253.

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542-575.Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for

a national index. American Psychologist, 55(1), 34-43.Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with

life scale. Journal of Personality Assessment, 49, 71-75.Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being:

Three decades of progress. Psychological Bulletin, 125, 276-302.Durkin, M. S., Davidson, L. L., Kuhn, L., O’Connor, P., & Barlow, B. (1994). Low

income neighborhoods and the risk of severe pediatric injury: A small-area analy-sis in Northern Manhattan. American Journal of Public Health, 84, 587-592.

Edwards, L. M., & Lopez, S. (2006). Perceived family support, acculturation, and life satisfaction in Mexican American youth: A mixed methods exploration. Journal of Counseling Psychology, 53, 279-287.

Elavsky, S., McAuley, E., Motl, R., Konopack, J., Marquez, D., Hu, L., et al. (2005). Long-term quality of life in older adults: Efficacy, esteem, and affective influ-ences. Annals of Behavioral Medicine, 30(2), 138-145.

Emmons, R.A. & Diener, E. (1985). Personality correlates of subjective well-being. Personality and Social Psychology Bulletin, 11(1), 89-97.

Farrington, D., Loeber, R., Elliott, D. S., Hawkins, J. D., & Kandel, D. (1990). Advancing knowledge about the onset of delinquency and crime. In B. Lahey & A. Kazdin (Eds.), Advances in Clinical Child Psychology (pp. 283-342). New York: Plenum.

Garbarino, J. (2001). An ecological perspective on the effects of violence on children. Journal of Community Psychology, 29, 361-378.



Morgan et al. 631

Ge, X., Conger, R. D., & Elder, G. H. (2001). The relation between puberty and psychological distress in adolescent boys. Journal of Research on Adolescence, 11(1), 49-70.

Hare, B. (1979). Racial and socioeconomic variation in preadolescent area-specific and general self-esteem. International Journal of Intercultural Relations, 1, 31-51.

Harvard Civil Rights Project. (2004). Losing our future: How minority youth are being left behind by the graduation rate crisis. Cambridge, MA: The Civil Rights Project at Harvard University.

Henry, D. B. (2001). Classroom context and the development of aggression: The role of normative processes. In F. Columbus (Ed.), Advances in psychology research, 6 (pp. 193-227). Hauppauge, NY: Nova Science.

Hogan, D. P., & Kitagawa, E. M. (1985). The impact of social status, family struc-ture, and neighborhood on the fertility of Black adolescents. American Journal of Sociology, 90, 825-855.

Holleran, L. K., & Waller, M. A. (2003). Sources of resilience among Chicano/a youth: Forging identities in the Borderlands. Child and Adolescent Social Work Journal, 20, 335-350.

Hughey, J., Speer, P. W., & Peterson, N. A. (1999). Sense of community organizations: Structure and evidence of validity. Journal of Community Psychology, 27, 97-113.

Jencks, C., & Mayer, S. (1990). The social consequences of growing up in a poor neighborhood. In L. E. Lynn & M. F. H. McGeary (Eds.), Inner-city poverty in the United States (pp. 111-186). Washington, DC: National Academy Press.

Klebanov, P. K., Brooks-Gunn, J., Chase-Lansdale, P. L., & Gordon, R. (1997). Are neighborhood effects on young children mediated by features of the home envi-ronment? In J. Brooks-Gunn, G. J. Duncan, & J. L. Aber (Eds.). Neighborhood Poverty, Vol. 1, Contexts and Consequences for Children (pp. 146-173). New York: Russell Sage.

Kowaleski-Jones, L. (2000). Staying out of trouble: Community resources and prob-lem behavior among high-risk adolescents. Journal of Marriage and Family, 62, 449-464.

Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55, 170-183.

Lent, R. (2004). Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment. Journal of Counseling Psychology, 51, 482-509.

Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: The effects of neighborhood residence on child and adolescent outcomes. Psychological Bul-letin, 126, 309-337.

Lightsey, O. R. (1996). The role of psychological resources in well-being: A reply. The Counseling Psychologist, 25, 699-705.




Long, D. A., & Perkins, D. D. (2003). Confirmatory factor analysis of the Sense of Community Index and development of a brief SCI. Journal of Community Psy-chology, 31, 279-296.

Lonigan, C. J., Phillips, B. M., & Hooe, E. (2003). Relations of positive and negative affectivity to anxiety and depression in children: Evidence from a latent variable longitudinal study. Journal of Consulting and Clinical Psychology, 71, 465-481.

Mahon, N.E., Yarcheski, A. & Yarcheski, T.J. ( 2004). Social support and positive health practices in early adolescents: A test of mediating variables. Clinical Nurs-ing Research, 3(3), 216-236.

Marsella, A. J. (1998). Urbanization, mental health, and social deviancy: A review of issues and research. American Psychologist, 53, 624-634.

McCabe, K., & Barnett, D. (2000). The relationship between family factors and future orientation of urban, African American sixth graders. Journal of Child and Family Studies, 9, 491-508.

McCullough, G., Huebner, E. S., & Laughlin, J. E. (2000). Life events, self-concept, and adolescents’ positive subjective well-being. Psychology in the Schools, 37, 281-290.

McMillan, D. W., & Chavis, D. M. (1986). Sense of community: A definition and theory. Journal of Community Psychology, 14, 6-23.

Meyers, S. A., & Miller, C. (2004). Direct, moderated, and cumulative relations between neighborhood characteristics and adolescent outcomes. Adolescences, 39(153), 121-144.

Oishi, S., Diener, E., Lucas, R. E, & Suh, E. M. (1999). Cross-cultural variations in predictors of life satisfaction: Perspectives from needs and values. Personality & Social Psychology Bulletin, 25, 980-990.

Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychologi-cal Assessment, 5, 164-172.

Perkins, D. D., Florin, P., Rich, R. C., Wandersman, A., & Chavis, D. (1990). Par-ticipation and the social and physical environment of residential blocks: Crime and community context. American Journal of Community Psychology, 18, 83-115.

Pretty, G. H., Conroy, C., Dugay, J., Fowler, K., & Williams, D. (1996). Sense of community and its relevance to adolescents of all ages. Journal of Community Psychology, 24, 365-381.

Prezza, M., & Constantini, S. (1998). Sense of community and life satisfaction: Inves-tigation in three different territorial contexts. Journal of Community & Applied Social Psychology, 8, 181-194.

Robbins, S., & Kliewer, W. (2000). Advances in theory and research on subjective well-being. In S. Brown & R. Lent (Eds.), Handbook of counseling psychology (pp. 310-345). Hoboken, NJ: Wiley.



Morgan et al. 633

Rutter, M. (1989). Pathways from childhood to adult life. Journal of Child Psychol-ogy and Psychiatry and Applied Disciplines, 30, 23-51.

Salguero, C., & McCusker, W. (1996). Symptom expression in inner-city Latinas: Psychopathology or help seeking? In B. J. Leadbeater, N. Way, & Niobe (Eds.), Urban girls: Resisting stereotypes, creating identities (pp. 328-336). New York: New York University Press.

Scheier, M. F., & Carver, C. S. (1985). Optimism, coping, and health: Assessment and implications of generalized outcome expectancies. Health Psychology, 5, 219-247.

Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An intro-duction. American Psychologist, 55(1), 5-14.

Shoemaker, A. L. (1980). Construct validity of area specific self-esteem: The Hare Self-Esteem Scale. Educational and Psychological Measurement, 40, 495-501.

Smith, E. (2006). The strengths-based counseling model. The Counseling Psychologist, 34, 13-79.

Snyder, C. R., Hoza, B., Pelham, W. E., Rapoff, M., Ware, L., Danovsky, M., et al. (1997). The development and validation of the Children’s Hope Scale. Journal of Pediatric Psychology, 22, 399-421.

Spencer, M. B., Dupree, D., Swanson, D., Phillips, D., & Cunningham, M. (1996). Parental monitoring and adolescents’ sense of responsibility for their own learn-ing: An examination of sex differences. Journal of Negro Education, 65(1), 30-43.

Sue, D. W., & Constantine, M. (2003). Optimal human functioning in people of color in the United States. In B. Walsh (Ed.), Counseling psychology and optimal human functioning (pp. 151-169). Mahwah, NJ: Erlbaum.

Sue, D. W., & Sue, S. (1999). Counseling the culturally different. New York: Wiley.Suldo, S. S., & Huber, E. S. (2004). The role of life satisfaction in the relationship

between authoritative parenting dimensions and adolescent problem behavior. Social Indicators Research, 66, 165-195.

Vaux, A. (1986). Social support: Theory, research, and intervention. New York: Praeger.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personal-ity and Social Psychology, 54, 1063-1070.

Wickrama, K. A. S., Conger, R. D., Wallace, L. E., & Elder, G. H. (1999). The inter-generational transmission of health-risk behaviors: Adolescent lifestyles and gen-der moderating effects. Journal of Health and Social Behavior, 40, 258-272.

Wilson, W. (1967). Correlates of avowed happiness. Psychological Bulletin, 67, 294-306.Wilson, W. J. (1991). Studying inner-city social dislocations: The challenge of public

agenda research. American Sociological Review, 56, 1-14.




Bios

Melissa L. Morgan, PhD, is currently an assistant professor at the University ofCalifornia, Santa Barbara. She does research in the areas of subjective well-being, and cross-cultural and immigrant resilience. Her publications include chapters on preven-tion in the Oxford Handbook of Counselling Psychology and Handbook for Social Justice in Counseling Psychology as well as articles on subjective well-being inthe Cultural Diversity and Ethnic Minority Psychology Journal and The Counseling Psychologist

Elizabeth M. Vera is a Professor in the Counseling Psychology program at Loyola University Chicago. She teaches courses in adolescent development and prevention/advocacy. Dr. Vera's scholarship focuses on urban youth development, social justice issues in psychology, and prevention.

Rufus R. Gonzales is a licensed psychologist in the state of Illinois and the Training Coordinator at DePaul University Counseling Services. His clinical and research interests include students of color, LGBT identified students, and resilience.

Wendy Conner is the Mental Well Being Services Manager at PACE, an LGBT mental health charity in London. Her research interests include the psychology of oppression and mental health issues for marginalized populations. She completed her MA at Boston College in 2002.

Kim Bena Vacek is a doctoral candidate in Counseling Psychology at Loyola Univer-sity Chicago. Her research interests include prevention, resiliency, and well being in diverse adolescent populations.

Laura Dick Coyle is an advanced doctoral student in Counseling Psychology at Loyola University Chicago. Her research interests include urban youth development and the effects of stress on well-being.



Documents

Subjective Well-Being in Urban Adolescents