Patterns of time use among low-income urban minority adolescents and associations with academic outcomes and problem behaviors.

J Youth Adolescence (2015) 44:1208–1225 DOI 10.1007/s10964-015-0294-0

EMPIRICAL RESEARCH

Patterns of Time Use Among Low-Income Urban Minority Adolescents and Associations with Academic Outcomes and Problem Behaviors Sharon Wolf1 • J. Lawrence Aber2 • Pamela A. Morris2

Received: 22 April 2015 / Accepted: 25 April 2015 / Published online: 5 May 2015 Ó Springer Science+Business Media New York 2015

Abstract Time budgets represent key opportunities for developmental support and contribute to an understanding of achievement gaps and adjustment across populations of youth. This study assessed the connection between out-ofschool time use patterns and academic performance outcomes, academic motivations and goals, and problem behaviors for 504 low-income urban African American and Latino adolescents (54 % female; M = 16.6 years). Time use patterns were measured across eight activity types using cluster analysis. Four groups of adolescents were identified, based on their different profiles of time use: (1) Academic: those with most time in academic activities; (2) Social: those with most time in social activities; (3) Maintenance/work: those with most time in maintenance and work activities; and (4) TV/computer: those with most time in TV or computer activities. Time use patterns were meaningfully associated with variation in outcomes in this population. Adolescents in the Academic cluster had the highest levels of adjustment across all domains; adolescents in the Social cluster had the lowest academic performance and highest problem behaviors; and adolescents in the TV/computer cluster had the lowest levels of intrinsic motivation. Females were more likely to be in the Academic cluster, and less likely to be in the other three clusters compared to males. No differences by race or

& Sharon Wolf [email protected] 1

Institute for Research on Poverty, University of WisconsinMadison, 1180 Observatory Drive, Madison, WI 53706, USA

2

Department of Applied Psychology, Steinhardt School of Culture, Education and Human Development, New York University, 246 Greene Street, New York, NY 10003, USA

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gender were found in assessing the relationship between time use and outcomes. The study’s results indicate that time use patterns are meaningfully associated with withingroup variation in adjustment for low-income minority adolescents, and that shared contexts may shape time use more than individual differences in race/ethnicity for this population. Keywords Time use Minority Low-income Socialization

Introduction While the Black-White and Hispanic-White achievement gaps have been narrowing since 1999, gaps continue to persistent and remain large (Reardon et al. 2014). Researchers have suggested that understanding achievement gaps requires moving beyond a narrow focus on grades and test scores only, as well as moving beyond between group comparisons to examine processes that explain within group variation (Garcia Coll et al. 1996; Wong and Rowley 2001). Out-of-school time budgets offer tangible representations of the types of opportunities for developmental support—i.e., how much time is spent in activities that positively contribute to growth and how much time is spent in idle or even harmful activities (e.g., Lareau 2003; Mahoney et al. 2005)—and may partially explain both between and within group variation in outcomes. Decades of research have examined adolescents’ discretionary time use as it relates to academic achievement, substance use and psychological well-being (Feldman and Matjasko 2005; Farb and Matjasko 2012). Assessing how time use relates to a range of outcomes for low-income minority adolescents specifically—including academic performance,

J Youth Adolescence (2015) 44:1208–1225

academic motivations and goals, and problem behaviors— would provide a more holistic view of academic success and well-being and contribute to knowledge of achievement gaps. Low-income minority youth—of which African Americans and Latinos are the largest subgroups in the United States—face particular challenges to development compared to suburban White middle- and upper-income adolescents. They experience higher rates of poverty and living in unsafe neighborhoods, inadequate housing, attendance in less resourced schools, and single-parent households (Evans 2004; Patterson 1991). In addition, minority adolescents have lower rates of school engagement (Johnson et al. 2001; Sirin and Sirin 2004) and lower levels of academic achievement across a number of indicators (e.g., Reardon et al. 2014). Less resourced schools and communities may offer different opportunities for time use than more resourced ones. And indeed, differences in the rates of time use between this group and White middle-class adolescents are documented across several activity types (e.g., Bouffard et al. 2006; Larson et al. 2001; Pederson and Seidman 2005; Theokas and Bloch 2006; Rideout et al. 2010). Furthermore, very few studies have documented that the relationship between time use and outcomes differs for African American and White adolescents (Fredericks and Eccles 2008; Lleras 2008). And, even fewer studies have assessed variation in discretionary time use and its associations with adaptive and maladaptive outcomes within the population of low-income minority youth. Such research is necessary to further current understanding of developmental disparities between minorities and whites. We follow calls from researchers to study within group variation in assessing ethnic minority adolescent outcomes (Garcia Coll et al. 1996; Dotterer et al. 2007; Wong and Rowley 2001). The goals of this study are to: (1) assess how a sample of low-income, urban African American and Latino adolescents structure their discretionary out-ofschool time; (2) examine if and how time use patterns are associated with academic performance outcomes, academic motivations and goals, and problem behaviors within this population; and (3) assess if time use patterns and their relationship to outcomes are moderated by race and gender. Following more recent developments in adolescent time use research (Farb and Matjasko 2012), we conceptualize and measure time use in a person-centered analysis that accounts for activity ‘‘portfolios’’, allowing us to measure patterns across individual time budgets. We use data from a sample of low-income urban minority adolescents from an evaluation of Opportunity NYC-Family Rewards, a conditional cash transfer intervention (Morris et al. 2012; Riccio et al. 2010) evaluated by MDRC, a nonprofit, nonpartisan education and social policy research organization.

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Theoretical Framework Social Control Theory and Routine Activity Theory Social control theory and routine activity theory help to articulate the pathways through which activity time use may affect adolescent outcomes. Typically applied to describing crime and deviant behavior (e.g., Osgood and Anderson 2004), social control theory posits that individuals are driven toward deviant and risky behaviors when connection to societal conventions (e.g., family, education) is weak or broken (Hirschi 1969). Rather than focusing on individual characteristics or motivations behind delinquent acts, this perspective focuses on the daily opportunities (i.e., time use) to participate in delinquent acts. It is the social norms, or social ‘‘controls,’’ provided by the contexts in which adolescents spend time that prevent individuals from engaging in crime and other deviant behaviors. Routine activity theorists have expanded this notion more broadly to encompass positive development (e.g., Barnes et al. 2007). Adolescents who are connected to societal conventions, such as high engagement in organized extracurricular activities, would not only show reductions in delinquent activities but also increased prosocial behaviors, such as improved academic motivation and performance. Similarly, the positive youth development perspective (Larson 2000) considers children as resources that need to be cultivated. The way children spend their time contributes to this cultivation and development (Lareau 2003). Thus, involvement in prosocial activities would protect against such behaviors, while engagement in unstructured activities would reinforce disconnection to societal norms and positively predict delinquent behaviors. Activities as Microsystems for Adolescent Development According to Bronfenbrenner’s bioecological model of human development, multiple interrelated systems affect child development. While biology is a primary force in individual development, it is the individual’s interactions with and between the surrounding systems—particularly those in which daily interactions occur such as family, school and community environment (microsystems)—that promote or stunt growth (Bronfenbrenner and Morris 2008). Race/ethnicity, urbanicity and socioeconomic status all shape adolescents’ social contexts in many ways. For example, at the individual level, family ethnicity influences many aspects of the home environment and, subsequently, many aspects of child development (e.g., Hill and Craft 2003; Umana-Taylor et al. 2004). In addition, urbanicity and socioeconomic disadvantage shape the microsystems in which low-income urban minority youth develop, including by shaping the extracurricular activities

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adolescents can access and the way activities are experienced (e.g., Barnes et al. 2007; Lleras 2008). Some have posited that adolescents growing up in low socioeconomic conditions have limited opportunities to develop positive attachments with social institutions for a number of reasons (e.g., lack of access to extracurricular activities; neighborhood disorganization; poor quality educational institutions; Leventhal and Brooks-Gunn 2000). With these frameworks in mind, we consider the range of activity time use patterns as microsystems that can be potentially protective, neutral, or harmful in fostering positive development specifically for low-income urban minority adolescents. Academic Outcomes, Risky Behaviors, and Time Use Behaviors related to academic attitudes and performance, as well as risky behavior, are interrelated and likely to form over the course of adolescence, setting up individuals for future risk or success. For example, achievement has consistently been linked to self-directed and intrinsically motivated learning (Pekrun et al. 2002; Zimmerman and Schunk 2013). Students who are engaged in school are more likely to succeed academically and less likely to drop out of school (Finn and Rock 1997; Mahoney 2000; Sirin and Sirin 2004). Furthermore, while there is an extensive literature linking substance use and academic failure, studies also show that adolescents with high levels of achievement motivation and engagement are also less likely to report engaging in substance use (Bryant and Zimmerman 2002; Voelkl and Frone 2000). Time use plays an important role in understanding the interrelatedness of adolescent development. A large body of research connects time use and adolescent outcomes, focusing primarily on psychological well-being, academic performance and problem behaviors. Evidence suggests\that adolescents who are involved in structured activities have higher levels of positive adjustment (i.e., Bohnert and Garber 2007; Mahoney et al. 2009). A review of the literature on extracurricular activities (Farb and Matjasko 2012) concluded that some activities (school clubs, prosocial activities, sports), but not all, are positively associated with academic achievement (e.g., Darling 2005; Dumais 2008, 2009), academic expectations (e.g., Dumais 2009), school engagement and self-esteem (e.g., Barnett 2007; Dotterer et al. 2007; Fredricks and Eccles 2006), and negatively related to substance use (e.g., Darling 2005; Bohnert and Garber 2007). In contrast, time spent in unsupervised activities, in paid work, and watching TV have been associated with poorer academic performance (e.g., Cooper et al. 1999) and antisocial behaviors (e.g., Mahoney et al. 2004; Mahoney et al. 2001). For urban African

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American adolescents specifically, one study found that unstructured leisure activities were associated with higher levels of delinquency only if adolescents resided in more dangerous neighborhoods (Bohnert et al. 2009). Differences in the Relationship Between Time Use and Outcomes by Race/Ethnicity and Gender Minority youth are often underrepresented in structured activities and overrepresented in unstructured leisure activities (Bohnert et al. 2008; Bouffard et al. 2006), and thus examining relationships between adjustment and a range of unstructured discretionary time activities is particularly important for this group of adolescents to understand the relationship between time use and outcomes. Surprisingly, only a handful of studies has examined race (specifically African Americans and Whites) and gender as moderators of the relationships between time use and outcomes (Farb and Matjasko 2012). Notably, no studies have assessed differences across racial minority groups (e.g., between African American and Latinos). Lleras (2008) found that compared to Whites, African Americans who engaged in fine arts extracurriculars in tenth grade had higher educational attainment and earnings 10 years later, which was interpreted to suggest that fine arts might foster better cognitive and behavioral skills among racial minorities. In addition, eighth grade participation in school clubs was related to 11th grade GPA for Whites but not minority students (Fredericks and Eccles 2008), and participation in school clubs was related to lower internalizing behavior in African American adolescents but not White adolescents (Fredericks and Eccles 2006). Furthermore, time spent with peers was associated with higher rates of substance use for Whites but not African Americans (Barnes et al. 2007), while participation in school sports in 11th grade was related to lower substance use for Whites but increased alcohol use for African Americans (Frederick and Eccles 2008). Gender-related patterns of time use vary greatly across adolescence. Generally, adolescent females spend more time on household tasks and chores than males (Passmore and French 2001; Jago et al. 2005). Males typically spend more time engaged in TV/electronics and organized sports, and girls more time in personal care and in unstructured activities, including phone with friends (Jago et al. 2005). In assessing differences in the relationship between time use and outcomes by gender, the findings are mixed. Fredericks and Eccles (2006) found that participation in organized sports and school clubs was related to more years of schooling more strongly for girls than boys. In addition, Fredericks and Eccles (2008) found that eighth grade participation in school clubs was related to 11th grade school value for males, but not females. Finally,


increased unstructured time with peers had a stronger positive relationship with sexual activity for males than for females (Barnes et al. 2007). When considering sports participation specifically, participation has been associated with less risky behaviors for boys (Fredericks and Eccles 2006; McHale et al. 2005), and increased risky behaviors for girls (Fredericks and Eccles 2006; Hoffman 2006). To our knowledge, only one study to date has examined gender as a moderator of adjustment outcomes related to activity involvement for urban minority (African American) youth (Bohnert et al. 2009). No differences were found. A Person-Centered Approach to Assessing Time Use Early time use studies focused on the associations of time devoted to one activity domain, for example organized sports or work, with adolescent outcomes in isolation from everything else adolescents do after school. Yet activity participation is a complex phenomenon and the role of each activity is best understood in the context of the other activities in which adolescents engage (i.e., adolescents’ broader social ecology). Increasingly, adolescent time use research has moved to assessing activity patterns or portfolios, using person-oriented approaches (e.g., Bartko and Eccles 2003; Feldman and Matjasko 2007; Linver et al. 2009; Nelson and Gastic 2009; Shanahan and Flaherty 2001; Zarrett et al. 2009). Person-oriented analyses approach time use holistically, assessing patterns across multiple activity types and grouping adolescents on the basis of their shared participation across activities, compared to variable-oriented analyses that assess average rates of participation in one activity type at a time. Ferrar et al. (2013) reviewed 19 studies from the last 7 years that assessed ‘‘clusters’’ of adolescent time use using a person-oriented analysis as they related to physical activity and health outcomes. They identified 29 clusters of time use across the studies with samples from fourteen countries ranging from 9 to 18 years of age. Thirteen clusters were dominated by a single primary activity, such as work, school, or TV, and fourteen were defined by two co-occurring primary activities, such as sports and art. Across the studies, Ferrar et al. identified that Black and Hispanic adolescents, as well as low-income adolescents, were more likely to engage in time use patterns that were dominated by TV and ‘‘screen time’’ activities, a finding that is consistent with time use studies assessing media use as a single activity (Rideout et al. 2010). Higher income adolescents, as well as females, were more likely to engage is time use patterns dominated by school-based organized academic activities, and these adolescents had higher rates of prosocial behavior. Minority status adolescents were more likely to engage in time use patterns dominated by unorganized academic activities (i.e., ‘‘study’’ activities).

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The Ferrar et al. (2013) review provides an assessment of the types of patterns that exist across a range of adolescent populations as they relate to health and physical activity and provide a basis of comparison for future studies that assess different outcomes and specific subgroups of adolescents. Notably, the review did not consider differences between subgroups of minority adolescents, but only between minority and White adolescents.

The Present Study In this study, we use cluster analysis to assess patterns of time use for low-income urban minority adolescents. The identified clusters are partially validated by assessing how different activity patterns are associated with who adolescents report spending time with. For example, we test whether adolescents who report spending time predominantly in social activities also report spending more time with peers. We assess the relationship between cluster membership with a set of academic performance, academic motivations and goals, and problem behavior outcomes, and consider the moderating role of race/ethnicity and gender. Assessing variation across racial/ethnic minority groups helps to disentangle the relative influences of family and culture from other microsystemic disadvantage (e.g., school and neighborhood) on time use during adolescence, allowing for the development of more culturally-informed interventions targeting adolescent time use and risk reduction.

Research Questions and Hypotheses Three primary research questions and hypotheses are addressed. What are the patterns of out-of-school weekday time use for low-income urban African-American and Latino adolescents? Of adolescents who vary by activity time use patterns, are there differences in who adolescents report spending time with (e.g., family, peers) in the logically expected directions (Q1)? How are time use patterns associated with academic performance, academic motivations and goals, and risky behavior (Q2)? Are there differences in time use patterns between African-American and Latino adolescents and boys and girls? And, do the associations of time use with academic motivations and goals, performance and problem behaviors vary between African Americans and Latinos and boys and girls (Q3)? Drawing on our theoretical framework and previous research findings with other adolescent populations, we hypothesize that for the population of low-income, urban minority adolescents, whose social contexts differ from those of other adolescent populations: (a) adolescents who

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engage in more prosocial activities such as organized and academic activities will be observed to have higher levels of adjustment across all three dimensions, as such activities can foster strong ties to societal conventions and norms; (b) adolescents who engage in more involvement in passive unstructured activities at home (e.g., high rates of media use) will be observed to have lower academic performance and academic motivations and goals and lower problem behaviors, as such activities do not foster or hinder ties to societal conventions; and (c) adolescents who engage in more unstructured leisure activities away from home (e.g., socializing) will be observed to have higher levels of risky behavior, as such activities can hinder ties to societal conventions. Finally, we assess if time use associations with outcomes differ for African Americans and Latinos and for boys and girls (assuming that family culture and gender may be a moderator of time use patterns in their associations with outcomes). Because so little research has assessed within group variation in time use portfolios within this population, we consider this an exploratory, hypothesis-generating analysis.

Methods Sample The sample for this study includes 504 adolescents from the Bronx, Manhattan and Brooklyn in New York City whose mean age was 16.6 years (SD = .83). Participants were 56 % African American, 44 % Latino, and 54 % female. Nearly all adolescents attended public school; only 35 % met state learning standards for proficiency in English Language Arts (ELA) and Math. Most households were comprised of single parents (81 %) and had incomes of \130 % of the federal poverty line (86 %). The sample was part of a conditional cash transfer program evaluation, which began when adolescents were in ninth grade; participants were randomly assigned to a program or control group. There were few differences in baseline characteristics between groups except that control group parents had slightly lower education levels (Riccio et al. 2010). Sampling and Data Collection Procedures Participants were recruited from six of the most disadvantaged community districts in New York City. The initial study involved approximately 4800 families and 11,000 children. Eligible families had to have incomes at or below 130 % of the federal poverty level and have at least one child in the fourth, seventh, or ninth grade. Families were identified from school lists maintained by the NYC Department of Education and were randomly

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selected and subsequently recruited through mailings, phone calls, and home visits. Comparisons of participating families with those who did not participate suggest the sample was not more or less advantaged than the broader target population (Riccio et al. 2010, pp. 57–60). This study focused on a subset of children from the initial ninth grade cohort of the study randomly selected to participate in an embedded study (Morris et al. 2012). Of the 903 families in the larger study, a subsample of 716 adolescents (and their parent) was selected for interviewing. Information was collected on 511 adolescents, representing an effective response rate of 71 % (77 % of parents were contacted and consented their children to participate; of these, 93 % of children consented to participate and completed a survey). A survey was administered over the phone when adolescents were in the spring of 11th grade in May and June of 2010. Selection criterion established for the purposes of the survey was that the participating adult in the family either spoke English well or very well or spoke primarily English or Spanish in the home. As a result, parents in the embedded study survey samples were more likely to primarily speak English than another language in their homes than parents in the full research sample (Morris et al. 2012). In addition, school administrative records were collected from the NYC Department of Education in the spring of 10th and 11th grades to assess academic performance outcomes. Measures Activity Time Use Following the protocol of the semi-structured time-diary portion of the American Time Use Survey (www.bls.gov/ tus/), and previous time use research (Dotterer et al. 2007; McHale et al. 2004; Updegratt et al. 2006), adolescents were asked over the phone how they spent their discretionary time the previous day. Adolescents reported on all activities in which they engaged starting at 2 pm until they went to bed, and reported on how much time they spent in each activity. Questions began with ‘‘What were you doing at 2:00 PM [YESTERDAY/THURSDAY]?’’ Activities were coded by the interviewer into one of 22 pre-specified categories created by the Bureau of Labor Statistics. The interviewer asked how long the reported activity took and who with (alone, with parents, with friends, with boyfriend/girlfriend, with coworkers, with family members other than parents, with teachers, or with someone else). The next activity was prompted with ‘‘what did you do next?’’, and the same series of follow up questions were asked. This continued until adolescents reported they had gone to sleep for the night. Finally, adolescents were asked


if this was a ‘‘typical weekday’’; 83.1 % of the sample indicated so. We grouped the 22 activities into eight categories informed by prior research on the use of out-of-school time, and computed the total number of hours spent in each activity as well as the proportion of time in each activity relative to the total time reported. The eight categories were: academic (reading, school work/studying; in class/ school; academic lessons or activities); social [hanging out with friends/family; on the phone (talking and texting); having sex]; religious (pray/worship/meditate/attending church); family chores/work (doing housework/chores; taking care of children/siblings); work (working for pay); organized activities (clubs of non-academic activities/lessons/sport practice); TV/computer (watching TV, videos, etc.; computer/internet/email); maintenance (selfcare or grooming; eating or drinking; exercise; preparing meals or snack; commuting, traveling to and from school or work; nap/resting; relaxing; shopping; commuting to other places). While the maintenance category was the most heterogeneous, the included activities focused on those required to sustain or ‘‘maintain’’ daily life. This category of activities has been used in previous research on adolescent and child time use (e.g., Csikszentmihalyi and Larson 1984; Larson et al. 2001; Larson and Verma 1999). Table 1 presents descriptive statistics for each category. The Bureau of Labor Statistics has concluded that the recall diary method produces good data with low costs (Phipps and Vernon 2009). Studies assessing the reliability and validity of the recall diary method find no systematic bias associated with the method when comparing it to a random hour recorded in paper diaries or when a pager was activated (Robinson 1985; Juster 1986). However, some issues with under- and over-reporting were identified, including under reporting activities with short time frames (Robinson 1985). Notably, these studies were conducted with adult populations and not adolescents. Research assessing the reliability and validity of a recall time use diary related to physical activities showed acceptable to good reliability and acceptable validity for adolescents specifically (Booth et al. 2002). Companionship Time Use Adolescents reported on whom they were with during each activity by indicating if they were alone, with parents, friends, boyfriend/girlfriend, coworkers, family members other than parents, teachers, or someone else. Time with parents and with family members was combined to indicate time ‘‘with family.’’ Time with friends and with boyfriend/girlfriend was combined as time ‘‘with peers.’’ The total hours in each category were summed. Table 1 presents descriptive statistics for each category.

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Academic Performance School administrative records were collected for adolescents’ 10th and 11th grade school performance (2008–2009 and 2009–2010) from the NYC Department of Education. Outcomes included: attendance rate (% of days present; M = 80.1, SD = 22.3 in 11th grade); course credits earned (M = 8.93, SD = 5.90 in 11th grade); passing to the next grade the following year (76.9 % passed to 12th grade); and number of Regents exams passed (M = 1.34, SD = 1.30 in 11th grade). Academic Motivations and Goals Future Hopes for Academic Attainment A single item assessed how far adolescents hoped to progress in their education, with higher numbers indicating higher levels of attainment, where 1 = ‘‘some high school’’, 2 = ‘‘finish high school’’, 3 = ‘‘technical school’’, 4 = ‘‘some college’’, 5 = ‘‘finish college’’, and 6 = ‘‘graduate or professional school after college.’’ School Behavioral Engagement Adolescents reported on their engagement in classroom and school activities using a four-item scale (adapted from Furrer and Skinner 2003). Item were answered on a fivepoint scale where 1 = ‘‘never’’, 2 = ‘‘once in a while’’, 3 = ‘‘some of the time’’, 4 = ‘‘very often’’ and 5 = ‘‘all of the time.’’ Example items include ‘‘I try hard to do well in school’’ and ‘‘In class, I work as hard as I can.’’ Items were averaged (M = 4.30, SD = .61, range = 2—5; a = .75). Academic Motivation A 16-item version of the Academic Self-Regulation Questionnaire (SRQ-A; Ryan and Connell, 1989) was used to assess the degree of adolescents’ autonomy of motivation to learn. Each item had a 4-point scale where 1 = ‘‘not at all true’’, 2 = ‘‘not very true’’, 3 = ‘‘sort of true’’ and 4 = ‘‘very true.’’ A score was calculated to create the ‘‘Relative Autonomy Index’’ based on a combination of the subscales and ranged from -7 to 7 (Ryan and Connell 1989). Larger negative scores indicate less autonomous motivated (i.e., more extrinsically motivated) to learn while larger positive scores indicated that teenagers are more autonomously motivated (i.e., more intrinsically motivated) to learn (M = -.59, SD = 2.24). Representative sample items of extrinsic motivation include ‘‘I do homework because I will get in trouble if I don’t do it’’ and for intrinsic motivation include ‘‘I do homework because I enjoy doing my homework.’’

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Table 1 Means and standard deviations of time allocation in activities and with companions Hours spent in each category Full sample M (SD)

African American M (SD)

Latino M (SD)

504

281

220

Total time reported Time in academic activities

8.20 (2.80) 1.59 (1.78)

8.05 (2.60) 1.59 (1.78)

8.40 (3.13) 1.58 (1.75)

Time in social activities

1.28 (1.95)

1.23 (1.91)

1.33 (1.99)

N

t value

Girls M (SD)

Boys M (SD)

t value

273

230

1.38 -0.83

8.06 (2.95) 1.77 (1.87)

8.38 (2.71) 1.39 (1.64)

-1.28 3.30***

0.28

1.18 (1.79)

1.39 (2.12)

-1.05 -0.34

Activities

Time in work activities

0.22 (0.95)

0.21 (0.94)

0.23 (0.98)

0.43

0.20 (0.92)

0.24 (1.00)

Time in maintenance activities

2.08 (1.93)

2.04 (1.71)

2.14 (2.18)

-0.46

2.27 (2.15)

1.87 (1.59)

2.73**

Time in organized activities

0.51 (1.26)

0.59 (1.38)

0.40 (1.07)

-1.46

0.26 (1.00)

0.79 (1.43)

-4.27***

Time doing family chores

0.25 (0.90)

0.21 (0.80)

0.31 (1.02)

0.79

0.31 (0.94)

0.19 (0.85)

2.08*

Time watching TV/on the computer

2.11 (2.18)

2.01 (2.20)

2.24 (2.17)

1.29

1.83 (2.01)

2.45 (2.33)

-3.23***

-1.42

Companionship Time alone

1.40 (2.00)

1.44 (2.10)

1.36 (1.89)

-0.83

1.26 (1.92)

1.57 (2.08)

Time with family

4.25 (3.28)

4.03 (3.06)

4.51 (3.55)

1.48

4.57 (3.35)

3.88 (3.18)

Time with peers Time with coworkers

2.44 (2.58) 0.18 (0.87)

2.47 (2.56) 0.15 (0.78)

2.39 (2.60) 0.22 (0.98)

-0.86 0.82

2.12 (2.51) 0.18 (0.90)

2.80 (2.61) 0.18 (0.84)

-3.14** -0.21

Time with teacher

0.88 (1.37)

0.89 (1.41)

0.86 (1.33)

-0.68

0.87 (1.37)

0.89 (1.37)

0.34

3.59***

* p \ .05; ** p \ .01; *** p \ .001

Mastery Goal Orientation

Aggressive Behaviors

Adolescents reported on the extent to which they learn because of a desire to become proficient in a topic (adapted from Midgley et al. 2000). Items included, ‘‘It’s important to me that I thoroughly understand my class work’’ and ‘‘One of my goals is to master a lot of new skills this year’’. A five-point scale was used where higher numbers indicated higher levels of mastery goal orientation, where 1 = ‘‘not at all true’’, 2 = ‘‘a little true’’, 3 = ‘‘somewhat true’’, 4 = ‘‘mostly true’’ and 5 = ‘‘very true’’. Items were averaged (M = 4.47, SD = .59, range = 1.2–5; a = .77).

Adolescents reported on the frequency with which they engaged in nine aggressive acts intended to cause harm to others in the past seven days (Orpinas and Frankowski 2001). Items included ‘‘teased students to make them angry’’ and ‘‘threatened to hurt or hit someone.’’ A four-point response scale was used with 0 = ‘‘0 times’’, 1 = ‘‘1 time’’, 2 = ‘‘2 times, 3 = ‘‘3 times’’, and 4 = ‘‘4 times’’ (M = .16, SD = .36, range = 0–2.67; a = .70).

Problem Behaviors Delinquent Behaviors Adolescents reported on their engagement in six minor delinquent acts in ‘‘the past couple of weeks’’ (Loeber and Dishion, 1983). Sample items included ‘‘taking something from someone at school that didn’t belong to me’’ and ‘‘breaking or ruining something on purpose that belonged to the school.’’ A four-point response scale was used, with 1 = ‘‘never’’, 2 = ‘‘once or twice’’, 3 = ‘‘a few times’’ and 4 = ‘‘many times’’ (M = 1.19, SD = .27; a = .61).

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Substance Use Four items measured the frequency with which adolescents reported smoking cigarettes, drinking alcohol, using marijuana, and using hard drugs (ecstasy, cocain, crack, LSD, uppers or downers) during the past month (adapted from Gaubert et al. 2010). A four-point response scale was used, with 1 = ‘‘never’’, 2 = ‘‘only a few times’’, 3 = ‘‘1 or 2 times a week’’, 4 = ‘‘several times a week or more.’’ Scores were dichotomized to indicate whether adolescents ever used substances in the past month (24.9 % reported using any substances. While these estimates are lower than some surveys have found (SAMSHA, 2012), they are similar to other national estimates where more than one quarter of adolescents drank alcohol, approximately onefifth used an illicit drug, and almost one-eighth smoked


cigarettes; Center for Behavioral Health Statistics and Quality 2012). Peer Substance Use Three items measured how many friends used substances in the past month, including how many friends tried alcohol, marijuana, and hard drugs (Gaubert et al. 2010). A fourpoint response scale was used for all items, with 1 = ‘‘none’’, 2 = ‘‘a few’’, 3 = ‘‘some’’, and 4 = ‘‘most.’’ Items were averaged (M = 1.9, SD = .7, range = 1.0–4.0; a = .63). Covariates Covariates were used in tests of associations between time use and adolescent academic and behavioral outcomes to control for observable variables that may confound the relations of interest. Adolescent characteristics were gender, race, special education status, and eighth grade ELA and math test scores. Family characteristics were the number of children in the household, whether or not English was spoken in the home, if the family was a single- or two-parent family, an indicator for parent has a high school degree or GED, an indicator for if the parent was currently working 30 h per week or more (calculated by a parent survey at baseline), and an indicator for if there was no mother in the home. Educational characteristics included the average class size for the adolescents’ grade level in the school and a school district dummy variable. An indicator for treatment status was included. In addition, an indicator of missingness was included for each covariate (on average, only 1 % of the sample was missing on any covariate; range 0–4.1 %). Analytic Plan Descriptive statistics and t tests were used to assess the average number of hours spent in activities and with companions for the full sample and by race and gender. Next, following recent person-oriented studies in adolescent time use research (see Farb and Matjasko 2012 and Ferrar et al. 2013 for reviews), we use cluster analysis to identify and differentiate between groups of adolescents by patterns of time use. Finally, based on the results of the cluster analysis, analyses of covariance (ANCOVAs) were used to examine the relationship between patterns of time use, companionship time use, and adolescent outcomes. Differences in the relationship between time use and outcomes by race and gender were examined. All analyses were conducted in SAS, Version 9.3.

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Measuring Time Use Patterns Cluster analysis is a multivariate technique for grouping subjects by identifying groups or clusters of individuals that are similar across a set of observable variables (e.g., Luke et al. 1991; Magnusson 1995). For the current study, a cluster represents a group of adolescents who spent their time in similar ways across all of the eight activity types. Different clusters represent different time use patterns. Because variables with large variances have a greater effect on the resulting clusters than those with smaller variances (Milligan 1996), all variables were standardized as a proportion of total time reported on (range 0–1). Both hierarchical and iterative methods were used to identify the final solution of clusters (Milligan 1996). The first step of hierarchical clustering uses Ward’s minimumvariance clustering method, which is an agglomerative hierarchical clustering method designed to generate clusters in such a way that the variance within the clusters is minimal. The proximity between every possible pair of individuals is calculated using the squared Euclidean distance, in an N-dimensional space, where N is equal to the number of variables on which subjects are clustered; in this case N = 8. Using ward’s method, the two individuals with the smallest squared Euclidean distance are merged to form a new cluster. Cases are merged in this way until the number of requested clusters is reached. Once the number of clusters was selected based on the hierarchical solution, the iterative analysis was conducted. Results from the group centroids of the clusters from the hierarchical analysis make up the starting values for clusters in the iterative solution using a k-means algorithm to determine cluster membership (Blashfield 1976; Milligan 1996). The iterative cluster identifies the most unique cluster solution by minimizing the distance across cases within clusters and maximizing the distance between clusters (Blashfield 1976). We assessed differences in companionship time use as an additional element of time use to build confidence that our clusters are reliably measuring adolescents’ activity participation. To ensure that the resulting clusters were not driven by receipt of the intervention through which this data was collected, the cluster analysis was rerun for both the program and control group adolescents separately to test whether the solutions were similar. Associations Between Time Use and Outcomes We employed a set of ANCOVAs to examine the relationship between time use cluster membership and indicators of adolescent academic and behavioral outcomes, controlling for covariates. Planned post hoc comparisons were conducted contrasting each outcome by cluster membership. We report the eta squared value associated

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with each ANCOVA as an effect size, which indicates the percentage of variance accounted for by the variable in the model after controlling for all other variables. This is considered a more conservative estimate than the partial eta squared (Levine and Hullett 2002). Only Type III effects are reported, which correspond to the variation attributable after correcting for covariates.


coworkers (.2 h). The high rate observed in time spent with family members is consistent with other reports of time use in minority youth (Larson et al. 2001). Notably, there was substantial variability in the averages (see standard deviations in Table 1), indicating that some adolescents spent larger amounts of times in some domains and less in others, and that a cluster analysis may yield additional insights into time use patterns.

Differences by Race and Gender Assessing Time Use Patterns: Cluster Analysis First, to test for potential subgroup differences in time use patterns, a multinomial logistic regression analysis was employed to understand descriptively if and how race and gender were related to cluster membership. Such an analysis is well-suited to answer this question because the dependent variable is a nominal categorical variable (i.e., membership in one of the four clusters). In a normal logistic regression the dependent variable is dichotomous; in a multinomial logistic regression the dependent variable has multiple categories (Hutcheson and Moutinho 2008). Second, to explore if the relationship between time use patterns and academic and behavioral outcomes differed by race and gender subgroups, two 2 9 4 factorial ANCOVAs were conducted. Analyses included cluster membership and gender with a corresponding interaction term and cluster membership and race with a corresponding interaction term (as well as all covariates). A significant interaction would indicate that the relationship between cluster membership and the outcome (i.e., academic performance, academic motivations and goals, or problem behaviors) is different for African Americans compared to Latinos, and for boys compared to girls.

Results Preliminary Analyses: Average Activity Participation Rates

A final four cluster solution was chosen. The additional variance in time use patterns explained by a fifth cluster was small (6 %). In addition to not adding conceptual information to the existing clusters, we concluded it did not contribute meaningfully to explaining additional variance. Each cluster was distinguished by a large amount of time spent in one primary activity. The first second cluster (N = 115) engaged predominantly in academic activities (51 %). The second cluster (N = 137) engaged predominantly in social activities (53 %). The third cluster (N = 157) engaged predominantly in maintenance activities (41 % of their time) and also engaged in the highest levels of work and family chores and organized activities, and the fourth cluster (N = 95) predominantly in watching TV and being on the computer (57 %). The levels of the other activities were somewhat similar across the groups. The four clusters were labeled (1) Academic, (2) Social, (3) Maintenance/work, and (4) TV/Computer. See Fig. 1 for a graphic representation of the patterns of time use across activities for each cluster. Because the data used in this study was collected as part of an intervention evaluation, the cluster analysis was rerun for adolescents in the program and control group separately to test if the solutions were similar. In both conditions, the final iterative solution was substantively nearly identical to the main analysis. (Results available from the first author upon request.) Validating Time Use Patterns

As a preliminary step, we examined descriptive statistics of time use variables and differences in mean values by race and gender (see Table 1). Adolescents reported being awake for an average of 8.2 h (SD = 2.8) beginning at 2 p.m. A majority of their out-of-school time was spent on maintenance activities (2.1 h), watching TV or on the computer (2.1 h), and academic activities (1.6 h). On average, adolescents spent 1.3 h on social activities, and about 0.5 h in organized activities. Only a small portion of time was spent working for pay or doing family chores (about 15 min for each). Regarding companionship, adolescents reported spending a majority of their time with family (4.2 h), followed by peers (2.4 h), alone (1.4 h), with teachers (.9 h) and a very small amount of time with

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We examined how cluster membership was associated with who adolescents spent time with (results displayed in Table 2). With the exception of time spent alone, cluster membership was significantly correlated with all other forms of companionship including: time with coworkers, F (25 478) = 6.03, p \ .001, g2 = .035, time with family, F (25 478) = 6.37, p \ .001, g2 = .035, time spent with teachers, F (25 478) = 19.00, p \ .01, g2 = .095, and time with peers, F (25 478) = 22.79, p \ .001, g2 = .118. All of the relationships observed were in the expected directions. Specifically, adolescents in the Maintenance/work cluster spent the highest amounts of time with coworkers (M = .41 h), significantly more than those in the


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Academic

Maintenance/Work 10.6

Academic

20.7 6.7

Religious activity

40.7

4.9

50.7

14.0

Work

0.9

Social

6.1

Family chores Organized activities

14.5

3.3

6.2

Maintenance

12.1

2.2 0.0

TV/Computer

1.6 Social 1.2 0.3

TV/Computer 0.6

6.6 17.0

19.0

14.6

0.0 4.8 1.9 2.7

15.2 52.6 2.6

56.7 1.4

Fig. 1 Proportion of time spent in activities by time use cluster

TV/computer group (M = .02 h, p \ .001) and the Social cluster (M = .04 h, p \ .01). Adolescents in the TV/computer cluster spent the most amount of time with family (M = 5.21 h), significantly more than those in the maintenance/work cluster (M = 4.08, p \ .05), the academic cluster (M = 3.44, p \ .001), and marginally significantly more than those in the social cluster (M = 4.12, p \ .10). Furthermore, adolescents in the Academic cluster spent the most time with teachers (1.67 h), significantly more than all the other clusters (M = .75 h in the TV/computer cluster, p \ .001; M = .72 h in the maintenance/work cluster, p \ .001; and M = .37 h in the social cluster, p \ .001). Finally and not surprisingly, adolescents in the Social cluster spent the most time with peers (4.13 h), significantly more than all the other groups (M = 1.40 h in the TV/computer cluster, p \ .001; M = 2.41 h in the Maintenance/work cluster, p \ .001; and M = 2.33 h in the academic cluster, p \ .001). Sensitivity to Category Definitions While most of the items that comprised the eight categories were face valid and functionally equivalent within categories, there were two exceptions. First, time spent on the

computer was categorized together with TV time. Time on the computer may be productive (for example, doing homework or research on the Internet for a school assignment), nonproductive (playing video games, watching YouTube), or social (e-mailing/chatting with friends), so the validity of the cluster structure was tested by separating these two items. The cluster analysis was run separating TV and computer activities and the four cluster solution produced fairly similar clusters to the original model. For the TV/computer oriented cluster, watching TV made up 56 % of their total time, while time on the computer was only about 6 % of the total time, suggesting that this cluster is primarily made up of teenagers watching television. Second, the maintenance category was comprised predominantly of self-care and grooming activities, eating, and napping, but also included time spent in commuting. We separated time commuting from the rest of the maintenance activities which were primarily self-care (napping, resting, eating, self-care). The four-cluster solution was similar to the original model. In the ‘‘maintenance/work’’ cluster, commuting accounted for only about 5 % of that cluster’s total time, while napping, resting, eating, and selfcare together accounted for 70 %. Indicating that most of

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Table 2 Analysis of covariance results for time use cluster membership and development outcomes Cluster mean values F

g2

Academic

Social

Maintenance/work

TV/computer

Companionship time use Hours alone

1.99

0.011

1.24

1.31

1.26

1.76

Hours with coworkers

6.03***

0.035

0.17

0.04

0.41

0.02

Hours with family

6.37***

0.035

3.44

4.12

4.08

5.21

Hours with teacher

19.00***

0.095

1.67

0.37

0.72

0.75

Hours with peers

22.79***

0.118

4.13

4.13

2.41

1.4

6.14***

0.033

86.00

74.10

77.50

82.30

Number of course credits passed

5.17**

0.025

10.56

7.51

8.60

9.00

Number of regents passed

1.19

0.006

1.50

1.30

1.20

1.40

Progressed to 12th grade (%)

1.81

0.01

70.40

54.90

61.80

60.50

How far hope to go in school/education

3.32*

0.017

5.18

4.94

4.88

4.80

School engagement

2.66*

0.015

4.43

4.19

4.28

4.32

Academic motivation (RAI index)

3.66*

0.020

-0.17

-0.50

-0.51

-1.09

Mastery goal orientation

4.73**

0.026

4.65

4.45

4.45

4.39

Academic performance Attendance rate

Academic motivations and goals

Problem behaviors in past month Any delinquent behaviors (%)

0.82

0.005

44.30

55.80

48.30

49.00

Any aggressive behaviors (%)

2.75*

0.015

23.20

26.90

16.60

13.60

Any individual substance use (%)

4.87**

0.027

23.00

40.20

20.20

21.60

No. of peers using substance

2.88*

0.018

1.73

2.04

1.92

1.92

Type III effects reported. All analyses include covariates * p \ .05, ** p \ .01, *** p \ .001

the maintenance time was time spent in self-care activities rather than commuting. Associations Between Cluster Membership and Outcomes: Analysis of Covariance Models ANCOVA analyses were used to assess how time use patterns were related to outcomes in three domains of adolescent development. Covariates were included in all models. In addition, ‘‘lagged’’ models were used to estimate academic performance outcomes in 11th grade that included each outcome in tenth grade as an additional control to better estimate of the independent relationship of time use in predicting academic performance. Differences in the association of time use with adolescent outcomes by race and gender were also examined. Cluster Membership, Academic Outcomes and Risky Behaviors Cluster membership was associated with some outcomes in each of the three domains, indicating that controlling for adolescent and family demographic characteristics, time use

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patterns were associated with certain aspects of adolescent academic and behavioral outcomes (see Table 2). Specifically, cluster membership was associated with two academic performance outcomes including attendance rate [F (25 478) = 6.14, p \ .001, g2 = .033] and number of course credits passed [F (25 478) = 5.17, p \ .01, g2 = .025]; all academic motivation and goal outcomes, including future hopes for academic attainment [F (25 478) = 3.32, p \ .05, g2 = .017], school engagement [F (25 478) = 2.66, p \ .05, g2 = .015], academic motivation [F (25 478) = 3.66, p \ .05, g2 = .020], and mastery goal orientation [F (25 478) = 4.73, p \ .01, g2 = .026]; and with three of the four problem behaviors in the past month, including aggressive behavior [F (25 478) = 2.75, p \ .05, g2 = .005], substance use [F (25 478) = 4.87, p \ .01, g2 = .027] and number of peers using substances [F (25 478) = 2.88, p \ .05, g2 = .018]. Post hoc Tukey–Kramer multiple comparisons revealed that both adolescents in the academic cluster and TV/computer cluster had significantly higher school attendance rates than those in the social cluster (M = 86 vs. 74 %, p \ .001; and M = 82 vs. 74 %, p \ .01, respectively). Adolescents in the Academic cluster also had


passed significantly more course credits than those in the Social cluster (M = 10.6 vs. 7.5, p \ .001). Regarding academic motivations and goals, adolescents in the Academic cluster had significantly higher hopes for future educational attainment than adolescents in the TV/computer group (p \ .05); higher school engagement than adolescents in the Social cluster (p \ .027); higher motivation than adolescents in the TV/computer cluster (p \ .01); and higher levels of mastery goal orientation than adolescents in the Maintenance/work cluster (p \ .05) and TV/computer cluster (p \ .01). Finally, regarding problem behaviors, there were no differences between clusters in delinquent activities. Adolescents in the Social cluster had higher rates of aggression than adolescents than the TV/computer cluster (p \ .05); higher rates of substance use than adolescents in the Academic (p \ .05), TV/computer (p \ .01), and Maintenance/work clusters (p \ .01); and had more peers using substances than adolescents in the Academic cluster (p \ .05). Data was available only for academic performance outcomes (but not other outcomes) in the year prior to the survey data collection, and thus ‘‘lagged’’ models for academic performance outcomes controlling for tenth grade performance was assessed. These models allow for a better estimation of the independent relationship of time use in predicting academic performance. Compared to the previous models, in the lagged models the relationship between cluster membership and attendance rate was smaller in magnitude but still statistically significant [F (25, 478) = 3.06, p = .028], and the relationship between cluster membership and total course credits passed was smaller in magnitude and marginally significant [F (25, 478) = 2.30, p = .076]. Race and Gender as Moderators Descriptive Statistics In assessing mean differences in average activity participation, race (African American and Latinos) and gender (boys and girls) differences were explored using independent samples t tests. There were no significant differences by race, but several differences were observed by gender (see Table 1). Girls on average spent more time than boys on academics (1.8 vs. 1.4 h; t = 3.30, p \ .001), maintenance activities (2.3 vs. 1.8 h; t = 2.73, p \ .01), and family chores (.30 vs. 18 h; t = 2.08, p \ .05). Boys spent more time than girls in organized activities (.78 h vs. 26 h; t = -4.27, p \ .001) and watching TV/on the computer (2.40 vs. 1.82 h; t = -3.23, p \ .001). There were no differences in time spent in social activities or work activities. Regarding companionship, girls reported spending more time with family than boys (4.57 vs. 3.88 h; t = 3.59,

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p \ .001), while boys reported spending more time with peers (2.80 vs. 2.12 h; t = -3.14, p \ .01). There were no differences in time spent alone, with coworkers or teachers. Differences in Time Use Patterns To test for potential subgroup differences in time use patterns, a multinomial logistic regression analysis was conducted to understand descriptively if and how race and gender was associated with cluster membership. Race was not significantly associated with cluster membership (b = 3.03, p = .386), but gender was significantly associated with cluster membership (b = 10.82, p \ .05). Posthoc comparisons revealed that boys were less likely to be in the Academic cluster and more likely to be in the Maintenance/work cluster (b = -.56, p \ .05; OR = .57), the TV/computer cluster (b = -.81, p \ .01, OR = .44), and the Social cluster (b = -.74, p \ .05, OR = .48). Differences in the Relationship Between Time Use and Outcomes Two 2 9 4 factorial ANCOVAs were conducted (for race and gender). There were no statistically significant interaction terms, indicating that the relationships between cluster membership and outcomes were not different by race or gender.

Discussion The achievement gap between Black and Latino adolescents and their White peers is a longstanding concern of researcher and educators (Reardon et al. 2014). The sources of these gaps are multi-faceted, and researchers have suggested that assessing within-group predictors of outcomes is critical to understanding the experiences of minority youth (Garcia Coll et al. 1996; Wong and Rowley 2001). Previous work suggests that how youth spend their discretionary time has implications for a host of outcomes, but few studies have empirically examined the relationship between time use and adjustment for low-income minority youth specifically, and no studies have examined differences in time use and its associations with outcomes between different minority groups. Such research can shed light on the relative influences of family and culture versus shared environmental factors, and is important in understanding the processes underlying gaps in achievement and adjustment. This study examined time use patterns and its correlates in a sample of low-income urban African American and Latino adolescents to assess the factors that give rise to within group variation in youth development. Results

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suggest that the way low-income urban African-American and Latino adolescents structure their time meaningfully explains variation in academic performance, academic motivations and goals, and problem behaviors. Activity patterns were dominated by a single activity, indicating that, as a whole, this group of adolescents is exposed to a somewhat ‘‘unbalanced’’ set of opportunities to foster development in that exposure to different activities and social contexts is limited. Our analysis identified four out-ofschool time use portfolios: Academic, Social, Maintenance/work, and TV/computer. Our hypotheses were only partially supported. First, adolescents with more involvement in prosocial and productive activities (i.e., the Academic cluster) were associated with higher rates of adjustment, including higher academic motivations and goals (an indication of strong ties to social conventions). Notably, all groups had low participation rates in structured organized activities, and thus we could not assess that as a prosocial activity. Second, adolescents with more involvement in unstructured leisure activities (i.e., the TV/computer cluster) showed the lowest levels of intrinsic motivation, but levels of problem behaviors were similar to the Academic and Maintenance/work clusters. Third, adolescents with more involvement in unstructured leisure activities away from home (i.e., the Social cluster) was associated with the highest rates of problem behaviors, and lowest academic performance outcomes (an indication of weak ties to social norms). Thus, for the most part, patterns between time use and adjustment in this population are consistent with theory and prior findings with other adolescent populations. Similar time use portfolios to the first three—Academic, Social, TV/computer—were identified in a nationally representative sample of adolescents (Nelson and Gastic 2009), supporting the fact that similar profiles are demonstrated across populations. Unlike Nelson and Gastic, we identified a group dominated by ‘‘maintenance’’ activities (e.g., self-care, preparing meals, shopping) who also engaged in the highest rates paid work and family chores. Because Nelson and Gastic’s study did not collect information on maintenance and chore activities, it is unclear if this time use group is specific to low-income urban minority adolescents, though it is plausible. Research suggests that Hispanic adolescents, for example, report greater expectations regarding their duty to assist, respect, and support their families than White adolescents (Fuligni et al. 1999; Fuligni and Pederson 2002). Measuring such dimensions of time use may be important in assessing how obligations shape adolescent time use for this population. Adolescents engaged in Maintenance/work patterns had average to low rates of adjustment relative to the sample, indicating that it may be important for future studies to examine such aspects of time use in more depth to

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understand where a point of intervention may be for this group. Our findings suggest that assessing a wider range of unstructured out-of-school activities may be important for this population. Studies that only assess organized activities may be capturing only a small proportion of discretionary time for low-income and minority youth. As one of the first studies to assess between group variation in time use for African American and Latino adolescents, it is notable that we found no differences by race in how time was structured and its relationship with outcomes. These findings point to the possibility that the environmental similarities shared by the two groups, such as peer, school, and neighborhood contexts, shape time use more than any family or cultural differences. Indeed, research has shown that neighborhood safety can moderate the relationship between time in unstructured activities and both depressive symptoms and delinquency (Bohnert et al. 2009). Notably, selection criterion for participation in the study was such that parents were more likely to primarily speak English than another language at home (81.3 %) compared to the general population of Hispanics (*74 %; Ryan 2013). Thus, external validity of the Latino participants is limited and results must be interpreted in this light. Within this population, we found differences for males and females such that, in line with previous research, females were, compared to males, more likely to be engaged in academically oriented patterns of time use and less likely to be engaged in socially oriented and TV/computer oriented. Research shows that adolescent girls display more persistence, attentiveness and effort in school and more eagerness to learn than boys (Downey and Vogt Yuan 2005; Duckworth and Seligman 2006), and time use may be one explanation for (or result of) these differences. Several researchers have suggested that, compared to girls, boys are more likely to experience decreased supervision from parents, teachers and school administrators as they age into adolescence (Ensminger et al. 1996; Entwisle et al. 1994). This may partially explain why boys were found to spend more time than girls in social and unstructured activities and less time in academics. The findings speak to the recently coined ‘‘boy crisis’’ in the education system, where minority boys are losing ground to girls across multiple dimensions (Husain and Milimet 2009), indicating that finding ways to effectively shift boys’ time use patterns may be one way to promote academic performance and motivation. Controlling for child and family demographic characteristics and adolescents’ eighth grade achievement test scores, academically oriented time use patterns promoted the most positive academic and behavioral outcomes while socially oriented time use patterns were associated with worse outcomes. Notably, adolescents with TV/computer


oriented time use patterns had the lowest levels of academic motivations and goals, though this groups’ academic performance was average relative to the rest of the sample. These findings are in line with our hypotheses and with research on adolescent time use patterns, which generally finds that academically oriented adolescents have higher rates of prosocial behavior, while work oriented, TV oriented, and socially oriented adolescents have comparatively lower rates of prosocial behavior and lower school engagement (Ferrar et al. 2013). The observed relationships may be partially explained by the differences found in companionship time use. For example, socially oriented adolescents spent a disproportionately large amount of time with peers, which could explain the higher rates of problem behaviors observed (Mahoney 2000; Mahoney and Cairns 1997). Research on peer effects finds a process of ‘‘deviancy training,’’ where positive reinforcement to rule-breaking discussions lead to increased problem behaviors (Dishion et al. 1999). In fact, nearly one-third of intervention studies aimed at reducing adolescent problem behaviors actually increased such behaviors, which researchers purport to be the result of peer effects (Lipsey 1992). Notably, this group spends a similar amount of hours on average with family, but not meaningfully higher than the other groups. In fact, all time use groups spend a significant portion of time with family (ranging from 3.4 to 5.1 h in an afternoon), likely a result of the racial and ethnic makeup of the sample (e.g., Fuligni et al. 1999; Larson et al. 2001). Furthermore, there is significant variability in the averages within the four clusters, and future work may combine activity and companionship time use patterns to understand these relationships more fully. The findings of this study suggest that studying time use within particular subgroups and its implications for outcomes is a useful approach to understanding underlying achievement and development gaps (e.g., Dotterer et al. 2007). In addition, it suggests that assessing a wider range of unstructured activities, perhaps by asking adolescents about time use in an open-ended manner, would shed more light on the nature and patterns of unstructured activities in which adolescents engage. Finally, it is important that the findings be interpreted in light of the age of adolescence, which in this study was in the spring of 11th grade. Considering the observed relationships in the context of the transition from middle school to high school, or from high school to young adulthood, may reveal differences (or consistencies) in the relationships across time. Dotterer et al. (2007) found that, for a group of early adolescent African Americans, time spent doing homework fostered higher school bonding, similar to the findings here that adolescents who spent in academic activities reported higher academic motivations and goals. The consistency

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across these two sets of findings suggests that intervening earlier in adolescence may have implications for time use and academic outcomes that persist throughout high school. This study has several limitations that are important to note. The first set of limitations has to do with the measure of the study. Time use was assessed at one time point only. While most adolescents indicated this was a ‘‘typical’’ weekday, we are not able to confirm the representativeness of adolescents’ reported time use outside of this 1 day. In addition, the recall time diary format relies on participants’ memory and may not be a completely accurate assessment of time use. Methodological studies suggest that more intensive methods of measuring time use, such as Experience Sampling Method, are more reliable. Second, our ability to code activities was limited since surveyors automatically selected one of 22 categories based on the survey design of the Bureau of Labor Statistics. Understanding the exact nature of each activity—for example whether computer time was for social networking or for homework—was thus not possible. Along these lines, the ‘‘maintenance’’ activity category was heterogeneous. While the activities included were distinct in their focus on ‘‘maintaining’’ daily life, they span a wide range of activities that may distort meaningful differences in time use. Third, all measures of interest were collected concurrently. Consequentially, no attributions of causal effects of time use and adolescent outcomes can be made. Additionally, the findings should not be generalized outside of low-income urban minority adolescents in New York City. Nonetheless, many of the findings are consistent with previous studies that assessed a more diverse range of adolescents (see Ferrar et al. 2013 for a review). Future studies should further test the generalizability of these findings with more diverse samples of youth. Furthermore, our estimates of the quantity of time spent in different activities do not address the quality of adolescents’ experiences in each activity. Of course what happens in the activity contexts and the quality of experience is essential to understand the implications for development and adjustment (e.g., Mahoney and Cairns 1997; Raymore et al. 1999). Finally, while nearly all (98 %) of the sample attended public high schools, the exact time of the end of the school day is not consistent across schools in New York City. These differences may account for some of the variation observed in academic time use. Despite its limitations, this study makes valuable contributions to literature on minority adolescent development. First, this study expands knowledge of how low-income urban minority adolescents spend their discretionary time with a sample of adolescents who are representative of the poorest communities in New York City. Second, this study uses a person-centered approach that allows for an

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integrated and holistic depiction of time use and identifies four distinct groups with different patterns of time use. While past research has documented a lack of time spent on homework for low-income high school students (Steinberg and Cauffman 1995), this study shows that a subgroup of low-income urban minority adolescents spend a majority of discretionary time in academic activities (on average 4.2 h in an afternoon), while the others spend approximately 30 min to an hour on such activities. Third, the study’s findings build on and extend current knowledge of how time use relates to important developmental outcomes to this understudied sample. We find very low rates of participation in extracurricular activities for this population, highlighting the importance of assessing unstructured activities for this group of adolescents to understand their time use holistically. Finally, this study is the first to our knowledge to examine differences in time-use and its relationship to outcomes between different racial/ethnic minority groups, and to examine gender differences within a sample of exclusively low-income minority adolescents. The findings indicate that environmental similarities are stronger than familial and cultural differences in shaping time use.


in resulting in improved academic performance. Time use pattern data would allow for a more targeted intervention approach by helping to identify specific intervention approaches for different types of adolescents. The study’s findings suggest the value of program and policy strategies that target certain groups of low-income urban minority youth to spend time differently to reduce the risks associated with growing up in poverty. Acknowledgments This article summarizes some of the findings from an evaluation conducted by MDRC, with support from the William T. Grant Foundation, the New York City Center for Economic Opportunity (CEO), and the Smith Richardson Foundation. The authors give thanks to the many staff members at Seedco and the Neighborhood Partner Organizations who spent countless hours operating the program and collecting the essential data on families’ participation and experiences. We’d also like to thank the many people at MDRC who contributed to managing this study. Author Contributions J.A. conceived of the study and participated in the design and coordination of the study. P.M. conceived of the study and participated in the design of the survey instrument. S.W. conducted all statistical analyses and interpretation of the data, while all authors worked closely to interpret the results. S.W. drafted the manuscript with careful input from P.M. and J.A. All authors read and approved the final manuscript. Conflict of interest

The authors report no conflicts of interests.

Conclusion References This study highlights the importance of assessing within group variation in adolescent time use and its link to adolescent adjustment for low-income minority adolescents and represents only the beginning of possible time use research in this area. The findings suggest that this approach can help shed light on disparities in development both within this group of adolescents and between this group and White middle-class adolescents. Future research drawing on these findings will contribute to a fuller understanding of the varying opportunities that adolescents experience, and the implications for closing developmental and achievement gaps to promote success in adulthood. Many programs aim to change adolescent time use as a way to improve adjustment and development. For example, recent years have seen a rise in the number of educational cash incentive programs targeted towards low-income adolescents with the goal of increasing time spent in academic activities to ultimately improve academic performance (e.g., Bettinger 2012; Fryer 2011; Riccio et al. 2010). This study’s findings indicate that such a program may not be effective for all disadvantaged adolescents, specifically the subset that is already spending a majority of their discretionary time on academic activities. For these adolescents, a program aimed at improving the effectiveness of study habits (e.g., tutoring) may be more effective

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Sharon Wolf, Ph.D. is a National Poverty Fellow at the Institute for Research on Poverty at the University of Wisconsin-Madison, in residence at the Office of the Assistant Secretary for Planning and Evaluation (ASPE) in the Department of Health and Human Services. Her work focuses on two how poverty and inequality affects children and adolescents’ development in two primary areas: (a) family economic instability as a context for academic and behavioral development, and (b) educational contexts and academic

1225 development. Both areas of work incorporate applied research by using social interventions as an approach to risk prevention and promotion of well-being, and understanding how successful programs can be adapted from one context to another. J. Lawrence Aber, Ph.D. is Willner Family Professor in Psychology and Public Policy at the Steinhardt School of Culture, Education, and Human Development, and University Professor, New York University, where he also serves as board chair of its Institute of Human Development and Social Change. Dr. Aber is an internationally recognized expert in child development and social policy. His basic research examines the influence of poverty and violence, at the family and community levels, on the social, emotional, behavioral, cognitive and academic development of children and youth. Dr. Aber also designs and conducts rigorous evaluations of innovative programs and policies for children, youth and families, such as violence prevention, literacy development, welfare reform and comprehensive services initiatives. Pamela A. Morris, Ph.D. is Professor of Applied Psychology, and director of the Institute for Human Development and Social Change. Dr. Morris has conducted more than a decade of research working at the intersection of social policy, practice, and developmental psychology, testing promising interventions for low-income families and children. Dr. Morris’ work spans two primary areas of research: First, she has led a wealth of research on the effects of welfare and employment policies, and their subsequent effects on parents’ employment and income, on children. Second, Dr. Morris is conducting a series of studies on intervention strategies for young (0–5 year old) children. These include several large-scale cluster randomized trials she is conducting in collaboration with researchers at MDRC that address the effects on children of enhancements to preschool quality by training teachers to support children’s socialemotional, self-regulation, and math skills.

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International note: parenting, academic achievement and problem behaviour among Chinese adolescents.

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