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. Author manuscript; available in PMC: 2023 Aug 8.
Published in final edited form as: J Youth Adolesc. 2021 Jan 21;50(6):1068–1080. doi: 10.1007/s10964-020-01376-x

Life Course Transitions and Educational Trajectories: Examining Adolescents who Fall off Track Academically

Aprile D Benner 1, Shanting Chen 1, Rashmita S Mistry 2, Yishan Shen 3
PMCID: PMC10409603  NIHMSID: NIHMS1908725  PMID: 33475926

Abstract

Educational interventions typically center on youth displaying early academic risk, potentially overlooking those falling off track academically later in their educational careers. The current study investigated the extent to which life course transitions experienced during adolescence were linked to falling off-track academically in high school. Data from the National Longitudinal Study of Adolescent to Adult Health (N = 4284; 53% female; Mage = 14.88) documented that 1516 students displayed no educational risk in early high school, yet 14% did not pursue 4-year college by age 24. Analyses revealed the unique life course transitions predictive of falling off-track academically (i.e., sexual intercourse, alcohol use, family transitions, residential mobility). The study’s findings highlight important intervention avenues to promote adolescents’ continued educational persistence.

Keywords: Educational attainment, Educational risk, Risky behavior, Family instability, Mobility, Life course transitions

Introduction

Despite its proven utility, a college degree remains elusive or against considerable odds for many of America’s youth (McFarland et al. 2019; Shapiro et al. 2017). Overwhelming evidence supports the positive payoff to both individuals and society of postsecondary education. Better educated individuals are more likely to marry and less likely to divorce as compared with their less well educated counterparts (Isen and Stevenson 2010), and they are also less likely to be incarcerated, experience better overall health, and enjoy a longer life expectancy (Hayward et al. 2015; Lochner 2020). Postsecondary education is also positively associated with lower levels of public assistance, greater lifetime earnings, and social mobility (Friedman 2005; Haskins 2009), and a college degree has been shown to afford higher levels of job protection during economic downturns (Leonhardt 2009). Variability in who attends college juxtaposed against college as a gateway to occupational and economic security makes identifying the factors that derail young people’ s educational prospects imperative. The current study focuses on the educational pathways of students who fall off track academically—that is, those who exhibit no educational risk early in high school yet do not make it past high school. Extant research on educational achievement and attainment generally has favored a focus on identifying normative developmental patterns or factors that promote or hinder at-risk students’ educational success, with less attention paid to elucidating why some students facing little to no educational risk fail to negotiate successful educational pathways (Feinstein and Peck 2008). Greater understanding of the processes that account for the differential outcomes of youth can inform intervention, prevention, and outreach efforts to ensure educational success for all youth.

Theoretical Frameworks

This study draws on tenets from two theoretical perspectives—cumulative risk (Sameroff et al. 1987, 1993) and life course theory (Elder et al. 2003)—to guide understanding of the consequences of adolescents’ educational risk and factors that elucidate relations between limited early educational risk and later educational attainment.

Cumulative risk perspectives

Cumulative risk approaches recognize that risk factors rarely operate in isolation and it is the experience of co-occurring and overlapping risk factors that are most detrimental to children and adolescents’ health and well-being. One of the most widely utilized approaches is the computation of a cumulative risk index, as originally proposed by Sameroff and colleagues (1987), that captures meaningful differences in children’s risk-related experiences, based on a large number of individual factors, in a single score. Given the co-occurrence of many risk factors, cumulative risk approaches also negate the problem of multicollinearity inherent in multiple regression approaches to assessing risk and child outcome associations (Burchinal et al. 2000). Abundant evidence documents the adverse effects of the accumulation of risk factors on adolescents’ health and well-being across developmental domains (e.g., Sharma et al. 2019; Zhang et al. 2019).

In the current study, the measure of educational risk encompasses adolescents’ school-related experiences across multiple domains, including academic performance, school engagement, and educational expectations. Academic performance is one of the most consistent and robust predictors of later educational attainment. Educational performance in elementary school can set students on differential academic trajectories (Duncan et al. 2007; Entwisle et al. 2005), but secondary school experiences are also critical for students’ educational careers. Adolescents who earn poorer grades in secondary school are more likely to fall behind in credit accrual and perform more poorly on standardized achievement tests, all of which are tied to higher dropout rates and a reduced likelihood of attending or completing college (Franklin and Trouard 2016; Stearns et al. 2007). In the current study, academic performance is captured via three measures: grade point average, course failure (directly tied to credit accrual), and retention in grade.

School engagement is similarly tied to adolescents’ ultimate educational success. Fredricks et al. (2004) conceptualize school engagement across three dimensions: behavioral (e.g., compliance, participation in school and extracurricular activities), affective (i.e., socioemotional interest in school), and cognitive (e.g., learning motivation, self-regulation strategies). Indicators of these three dimensions of school engagement are included in the current study, as previous research suggests that each is linked to adolescents’ dropout likelihood (Henry et al. 2012; Janosz et al. 2008) and college attendance (Lawson and Masyn 2015).

As with academic performance and engagement, numerous studies have examined the links between the educational expectations students hold for themselves and future educational success. Although previous research has documented an expectation-performance gap, particularly for racial minority youth (Reynolds et al. 2014), adolescents’ own educational expectations are linked not only to academic performance in secondary school (Domina et al. 2011) but also their college enrollment (Wood et al. 2011) and ultimate educational attainment (Sommerfeld 2016).

Although prior research has shown academic performance, school engagement, and educational expectations to be independently predictive of adolescents’ educational progress beyond high school, few studies have attended to the interrelated nature of educational risks and sought to examine its cumulative impact on later attainment as done here. Yet, scholars point out that educational progress, particularly during the adolescent years, more accurately reflects a combination of cognitive abilities, academic performance, and motivational processes (McLoyd et al. 2009), something which is carefully attended to in the current study.

Life course theory

Life course theory focuses on the dynamic interplay between persons, processes, contexts, and time that determines stability and change in developmental trajectories that unfold as individuals age (Elder et al. 2003). Central to this perspective, human development is characterized by a series of life course transitions, both normative and non-normative, that can serve as turning points in individuals’ lives. In some cases, these turning points can be disruptive, deflecting developmental trajectories that were previously characterized by positive adjustment. The current study seeks to understand exactly how common life course transitions that are frequently experienced in adolescence can derail students’ educational pathways.

Many of the transitions targeted in the current study relate to the initiation of risky behaviors, which often has its etiology in adolescence (Schulenberg et al. 2018). In particular, this focus centers on initiation of substance use (i.e., alcohol, marijuana, tobacco) and sexual intercourse. These risky health behaviors can cooccur (Huang et al. 2012), placing adolescents at particular developmental risk, as all are related to poorer educational prospects. More normative in nature, adolescence is also the typical developmental period when young people initiate romantic ties (Collins et al. 2008). The implications of these romantic ties for adolescents’ educational well-being are less investigated, with evidence suggesting that they can confer benefits when the partner is achievement oriented (Giordano et al. 2008) but can be detrimental if initiated early (Neemann et al. 1995).

Nonnormative transitions, or those that occur unpredictably or unexpectedly, are also considered. Family transitions, including the exit or entry of a parent, are common in the lives of adolescents, and such family instability can have detrimental effects on young people’s educational success (Lee and McLanahan 2015; Cavanagh and Fomby 2011). Likewise, residential and school mobility, particularly when occurring multiple times, are linked to poorer achievement and lower educational attainment (Metzger et al. 2015; Reynolds et al. 2009).

Current Study

In the current study, early educational risk during 9th and 10th grades is examined in conjunction with later postsecondary educational attainment, and whether life course transitions experienced in adolescence account, at least in part, for derailed academic trajectories is examined. The focus on the early high school years is purposeful, driven by the tenets of life course theory—the transition from middle to high school necessitates a shift in schools, a proximal context of adolescents’ development, which in turn can disrupt existing relations and interactions and spur the formation of new relationships with teachers and peers (Benner 2011). Moreover, it is a time when young people experience a number of normative and non-normative life course transitions that could contribute to derailed academic trajectories.

Two primary research questions are examined using nationally-representative data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). First, how do patterns of educational risk at the beginning of high school map onto subsequent educational attainment (i.e., attending or graduating from a 4-year college) during early adulthood? Here, the particular focus is in identifying those young people who fall off track educationally—that is, those who display no early educational risk yet fail to attain a college degree. As much of the extant literature focuses on either normative achievement trajectories or factors promoting educational resilience for those displaying early risk, this focus allows for identification of an often neglected group of students whose achievement trajectories are disrupted prior to the critical transition from high school to college.

Second, the study investigates the extent to which life course transitions experienced during adolescence are linked to falling off-track educationally across the transition from adolescence into young adulthood. Given the consistent evidence linking early risky behavior initiation to poorer life course outcomes, it is hypothesized that these types of transitions will be particularly likely to deflect young people’s educational trajectories. Moreover, informed by life course theory’s tenet of accumulating disadvantages and cumulative risk perspectives’ conceptualization of risk, it was expected that adolescents experiencing a larger number of transitions would be more likely to experience disrupted educational trajectories, even after controlling for life course transitions experienced prior to the deflection.

Method

Sample

Data for the current study are drawn from Add Health, a nationally representative study of adolescents initially in grades 7–12 in 1994. The sample was identified using a multistage, stratified, school-based, cluster sampling design—high schools were selected based on their region, urbanicity, school type (public vs. private), racial composition, and size (Harris et al. 2009). All high schools that did not serve 7th and 8th grades were then matched to a feeder middle school based on the number of students moving through the feeder pattern. In-School Surveys, which represent a census of the 132 sample schools, were collected during the 1994–95 school year (N = 90,118). The In-School Survey was intended to create a sampling frame for subsequent data collection, including identification of respondents for planned oversamples. A nationally representative sample (N = 20,745) drawn from the In-School Survey served as the core sample for the In-Home Interview, with Wave I beginning shortly after the In-School Survey and Wave II beginning 1 year after Wave I. Wave III data were collected in 2001–2002, approximately 5 years after the Wave II data collection. More than 15,000 Wave I respondents participated in the Wave III survey.

In the current study, inclusion in the analytic sample was based on three criteria. First, because of the study’s particular interest in educational risk at the beginning of high school, only adolescents who were in 9th or 10th grades in Wave I were included. Second, because of the central focus on early educational risk, the analytic sample included only adolescents who had at least one valid risk measure in each education domain (academic performance, engagement, and educational expectations). Finally, due to the interest in linking early educational risk to educational attainment, only those individuals who also participated in the Wave III In-Home Interview were included. Applying these filters resulted in a final sample of 4284 adolescents in 105 schools. Table 1 provides demographic characteristics for the analytic sample.

Table 1.

Descriptive statistics for central study variables

Total sample
(N = 4284)		 No risk group
(N = 1516)		 Falling off-track
(N = 215)		 Staying on-track
(N = 883)		 Stay on-track
sensitivity
analysis sample
(N = 1301)
Variable % M SD % M SD % M SD % M SD % M SD
Educational risk
 Grade point average 2.74 0.77 3.30 0.51 3.03 0.48 3.43 0.48 3.35 0.50
 Course failure 0.53 0.87 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
 Retention in grade 19.4 0.0 0.0 0.0 0.0
 School engagement 2.76 0.92 3.16 0.58 3.13 0.56 3.17 0.58 3.17 0.58
 Extracurricular participation 1.46 1.20 2.08 1.05 1.81 0.93 2.19 1.05 2.13 1.07
 Truancy 0.53 1.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
 Educational expectations 4.15 1.11 4.71 0.56 4.39 0.75 4.83 0.42 4.76 0.50
 Cumulative risk index 1.50 1.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Transitions between Waves I and II
 Relationship initiation 14.9 14.5 16.0 12.6 14.3
 Sex initiation 14.2 12.7 19.5 10.1 11.6
 Alcohol initiation 13.6 14.6 19.5 12.6 13.8
 Marijuana initiation 9.1 7.3 10.0 6.2 6.9
 Tobacco initiation 7.6 7.9 10.5 7.0 7.4
 Exit of a parent 6.2 3.9 6.6 2.8 3.5
 Entry of a parent 6.9 4.4 9.9 3.0 3.6
 School transition 9.4 7.2 10.6 5.8 6.7
 Residential mobility 7.7 5.0 8.7 3.4 4.4
  Number of earlier transitions 1.70 1.46 1.31 1.33 1.74 1.46 1.16 1.26 1.24 1.29
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Measures

Data for the current study were drawn from the In-School Survey as well as the Wave I through III In-Home Interviews. Descriptive statistics for all study variables and covariates appear in Tables 1, 2.

Table 2.

Descriptive Statistics for Covariates

Total sample
(N = 4284)		 No risk group
(N = 1516)		 Falling off-track
(N = 215)		 Staying on-track
(N = 883)		 Stay on-track
sensitivity
analysis sample
(N = 1301)
Variable % M SD % M SD % M SD % M SD % M SD
Age 14.88 0.88 14.60 0.72 14.67 0.74 14.57 0.69 14.58 0.72
Race
 White 51.5 57.8 64.7 57.9 56.7
 African American 22.2 19.3 14.4 19.7 20.1
 Latinx 16.0 10.6 12.1 9.5 10.4
 Asian American 7.4 10.1 5.1 11.6 10.9
 Other 2.9 2.2 3.7 1.4 2.0
Parent education 1.98 0.84 2.22 0.81 1.76 0.76 2.40 0.77 2.30 0.79
Gender (female) 52.9 58.2 57.7 57.1 58.3
School sector (private) 8.1 13.3 3.3 16.8 14.9
School region
 West 21.6 19.7 20.5 18.4 19.6
 Midwest 25.4 28.4 31.2 29.9 27.9
 South 38.3 35.9 38.1 32.7 35.5
 Northeast 14.8 16.0 10.2 19.0 17.0
Urbanicity
 Urban 27.2 27.1 25.1 27.9 27.4
 Rural 19.6 18.4 23.7 16.8 17.5
 Suburban 53.2 54.5 51.2 55.4 55.0
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Educational risk

Educational risk was operationalized as a multi-dimensional construct. Seven separate indicators of risk were included under three broad categories—academic performance, school engagement, and educational expectations—all of which were measured in the Wave I In-Home Interview when students were in 9th or 10th grade, with the exception of extracurricular involvement, which was measured in the Wave I In-School survey. Three measures of students’ academic performance were included (i.e., self-reported grades, number of failed courses, retention in grade), three measures of school engagement (i.e., academic engagement, extracurricular involvement, truancy), and one measure of educational expectations.

Self-reported grades

Adolescents reported their grades in the four core-content areas (i.e., English, mathematics, social studies, science) using ratings ranging from 1 (D/F) to 4 (A). Grades were averaged across subjects and converted to a standard four-point composite grade point average (GPA). Previous research with the Add Health data has shown a strong correlation between self-reported and transcript-reported grades (r = 0.88; see Langenkamp 2009).

Number of failed courses

The number of core-content courses for which the student received a D or F were summed to create a count of courses failed.

Retention in grade

Retention in grade was determined with one student self-report item: “Have you ever repeated a grade or been held back a grade?”

Academic engagement

Students were asked two questions regarding their engagement in academics: how often they had problems “paying attention in school” and “getting your homework done.” Items were rated on a 5-point scale ranging from 0 (never) to 4 (everyday). The items were reverse coded and averaged, with higher mean scores denoting greater engagement in academics (r = 0.71).

Extracurricular involvement

To assess extracurricular involvement, students reported on whether they participated in sports teams (11 possible teams), academic clubs (10 clubs), performing arts (5 activities), communication and leadership clubs (4 clubs), or other clubs (2 clubs). Extracurricular activities across domains were summed to create a measure of total extracurricular participation.

Truancy

Truancy was assessed with a single item: “How many times have you skipped school for a full day without an excuse?”

Educational expectations

Students’ educational expectations were measured by one item: “On a scale of 1 to 5, where 1 is low and 5 is high, how likely is it that you will go to college?”

Educational attainment

At Wave III, participants answered a number of questions regarding their highest level of educational attainment. These included questions regarding what degrees participants had earned, current attendance in postsecondary institutions, and highest grade completed. Using these data, a variable was created to categorize educational attainment: participants who attained a high school degree or less were coded as one (1), those who graduated high school but dropped out of 2- or 4-year college were coded as two (2), those who graduated from or were currently enrolled in a 2-year college were coded as three (3), and those who graduated from or were currently enrolled in a 4-year college were coded as four (4).

Life course transitions

Nine indicators of participants’ life course transitions that occurred between Waves I and II were included. Four of these captured the initiation of risky behaviors: sexual intercourse, alcohol use, marijuana use, and tobacco use. The other transitions examined were romantic relationship initiation, family transitions (i.e., exit of a parent, entry of a parent), residential mobility, and a school transition (generally this was a nonnormative school transition). Each transition variable was dummy coded to capture whether participants experienced such transition from Wave I to II (0 = no transition, 1 = transition). A count variable was created indicating the total number of transitions experienced between Waves I and II (potential range from 0 to 9).

Control variables

All analyses controlled for student age, race (i.e., African American, Latinx, Asian American, other race/ethnicity, with White as the omitted reference group), gender, parent education (1 = high school or less, 2 = some college, 3 = college or higher), school sector (i.e., private versus public), region (i.e., West, Midwest, South, with Northeast as the omitted reference group), and urbanicity (i.e., urban, rural, with suburban as the omitted reference group), all of which were measured in the In-School Survey. The number of transitions initiated before Wave I, given that participants may have already gone through the life course transitions under study before Wave I, was also included as a covariate. These earlier transitions encompassed romantic relationship initiation, sex initiation, alcohol use initiation, marijuana use initiation, and tobacco use initiation. A count variable was created indicating the total number of earlier transitions experienced (potential range from 0 to 5).

Analysis Plan

The first step in the analytic process was to create the measure of educational risk based on theories of cumulative risk (Sameroff et al. 1987). The risk variable was initially computed as a count risk score that assessed the total number of educational risks for each student across seven total indicators. For each indicator, students received a score of one (1) if they met or exceeded the specific risk threshold; those not meeting the threshold received a score of zero (0) for the indicator. For the academic performance indicators, students received a score of one each for a GPA of 2.0 or less (C average or less), if they failed one or more classes, and if they had ever been retained in grade. For the engagement indicators, students received a score of one each if they reported engagement problems once a week or more, if they were not involved in any extracurricular activities, and if they skipped school one or more days. For educational expectations, students received a score of one if they reported low likelihood of attending college (i.e., rated their likelihood as a 1 or 2 on a 5-point scale). A sum then was created to determine the total number of educational risks for each student; because sample identification was based, in part, on the availability of early educational risk data, the missingness of individual risk factors was minimal (i.e., less than 1%). Finally, following cumulative risk theory’s conceptualization of risk thresholds (Sameroff et al. 1987), each student was categorized into one of three educational risk groups: no risk (zero risk factors), low risk (one or two risk factors), and moderate/high risk (three or more risk factors).

Crosstabulation analyses then examined how the patterns of educational risk groups mapped onto subsequent educational attainment. The crosstabulation results were used to identify those students who exhibited no early academic risk but who either fell off track academically (i.e., currently enrolled in high school, left high school without a diploma, high school degree as highest educational attainment) or who remained on track academically (i.e., currently enrolled or had attained a 4-year college degree). Probit regression using all nine life course transition indicators in a single model, controlling the total number of transitions experienced before Wave I and a host of other covariates, determined the extent to which life course transitions were linked to falling off-track academically. Sensitivity analyses examined whether the pattern of effects was similar when remaining on track was conceptualized as attendance or completion of a 2-year or 4-year college degree (rather than solely a 4-year college degree).

Analyses were conducted in SPSS and Mplus v 8.2. The current dataset included some missing data, and Mplus handles missingness through full-information maximum likelihood (FIML). FIML is a preferred method for generalizing results to the population and using all available data (Enders 2010). Models were estimated using the CLUSTER function, which is designed to address violations to independence assumptions related to the multilevel nature of the data (e.g., the clustering of students within schools) and estimate robust standard errors.

Results

Patterns of Educational Risk and Subsequent Educational Attainment

In examining the individual risk indicators (see Fig. 1), course failure risk (34% of students) and truancy (27%) were most common, whereas educational expectations risk was least frequent (9%). A sum then was created to determine the total number of educational risks for each student. In total, 35% of students exhibited no educational risk, and an additional 24% had only one risk factor. Of students displaying multiple risk factors, 17% exhibited two risk factors, 12% three, 8% four, 3% five, 1% six, and less than 1% exhibited all seven risk factors. In categorizing students into educational risk groups, 35% exhibited no risk (zero risk factors; N = 1516), 40% exhibited low risk (one or two risk factors; N = 1724), and 24% exhibited moderate to high risk (three or more risk factors; N = 1044).

Fig. 1.

Fig. 1

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Percent of students who are at risk for each educational measure

For the study sample, crosstabulation analyses revealed that 69% of individuals who displayed high risk at the beginning of high school fell off track academically by Wave III (conceptualized as attending or completing 4-year college), whereas approximately 40% of those with low risk and 14% of those with no risk at the beginning of high school fell off track (see “Appendix”). The current study, however, was specifically interested in the subpopulation of students displaying no early risk at the beginning of high school (N = 1516) and their patterns of subsequent educational attainment. Of these students, 883 stayed on-track educationally, either currently attending or having completed a 4-year college degree by Wave III. In contrast, 215 fell off-track academically, with their highest educational level at Wave III being a high school diploma or less. The remainder (n = 418) attended or completed 2-year college and were considered in the sensitivity analyses.

Linking Life Course Transitions to Falling Off-track Academically

For students who displayed no early risk, several life course transitions experienced between Waves I and II were associated with falling off-track academically by Wave III (left portion of Table 3). Specifically, when adolescents who displayed no early academic risks at the beginning of high school initiated sexual intercourse between Waves I and II, they were more likely to fall off-track versus stay on-track. Similarly, students who initiated alcohol use were more likely to fall off-track educationally. Turning to transitions in family structure, students who experienced either the exit or entry of a new parent between Waves I and II were more likely to fall off-track academically by Wave III. Additionally, students who transitioned to a new neighborhood between Waves I and II were more likely to fall off-track academically rather than remain on-track. In contrast, romantic relationship initiation, initiation of marijuana or tobacco use, and school mobility were not related to whether or not students fell off-track educationally. In addition, the more life course transitions that students experienced before Wave I, the greater their likelihood of falling off-track educationally by Wave III. These relations were net the effects of all covariates and all transition markers.

Table 3.

Predictors of falling off-track for students exhibiting no educational risk in early high school

Off-track versus on-track
(4-year College)
 Off-track versus on-track
(2- or 4-year College)
b SE B p b SE B p
Transitions between Waves I and II
 Relationship initiation 0.25 0.13 0.08 0.06 0.15 0.12 0.05 0.22
 Sex initiation 0.27 0.13 0.08 0.04 0.20 0.12 0.06 0.09
 Alcohol initiation 0.27 0.13 0.09 0.04 0.21 0.12 0.07 0.07
 Marijuana initiation 0.07 0.15 0.02 0.66 0.01 0.15 0.00 0.97
 Tobacco initiation 0.19 0.17 0.05 0.27 0.20 0.16 0.05 0.20
 Exit of a parent 0.34 0.16 0.06 0.04 0.29 0.16 0.05 0.07
 Entry of a parent 0.59 0.18 0.11 0.00 0.52 0.16 0.10 0.00
 School transition 0.32 0.17 0.07 0.06 0.30 0.16 0.07 0.06
 Residential mobility 0.39 0.15 0.07 0.01 0.25 0.15 0.05 0.11
 Number of earlier transitions 0.16 0.04 0.19 0.00 0.14 0.03 0.17 0.00
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Significant effects are bolded

Sensitivity Analyses—Redefining What it Means to Succeed

The primary analyses used potentially strict criteria for defining staying on-track, namely completing or currently attending a 4-year college. Evidence consistently identifies an advantage in terms of occupational and economic life prospects for those completing a 4-year (i.e., bachelor’s) degree (Chan 2016). There is, however, some evidence that the receipt of a 2-year associate’s degree also confers advantages, albeit to a more limited degree and moreso for certain populations (i.e., women; Alfonso et al. 2005; McMahon and Walter 2009). As such, sensitivity analyses redefined the educational attainment cut-off criteria as completion or current attendance at either a 2-year or 4-year college. Using this threshold, the sample size of the comparison group (i.e., those who displayed no early risk and who remained on track academically) increased from 883 to 1301 individuals. Analyses then examined whether the life course transition indicators under study distinguished those who fell off-track academically (n = 215) versus those who remained on-track educationally using this less restrictive criterion. As seen in the right portion of Table 3, students who experienced family transitions between Waves I and II were more likely to fall off-track at Wave III, as were those who experienced more life course transitions prior to Wave I. There were no effects of romantic relationship initiation; initiation of sex, marijuana, tobacco use, or alcohol; or school or residential mobility.

Discussion

An exceptionally large number of American youth attend college immediately after high school, although receipt of a bachelor’s degree remains elusive for many (McFarland et al. 2019). Extensive attention has been placed on the intraindividual characteristics and interpersonal processes that promote postsecondary attendance and success (Entwisle et al. 2005; Harackiewicz et al. 2002), and a similarly expansive literature has identified the early educational and interpersonal risks that heighten students’ likelihood of school dropout (Archambault et al. 2009; Rumberger and Rotermund 2012). Missing from this body of research, however, is attention to a not insubstantial number of American high school students who fall off track academically after initially displaying no signs of educational risk—these students were the central focus of the current study.

To this end, students who displayed no early signs that they were at educational risk in 9th and 10th grades were first identified. Traditionally, studies have either identified educationally vulnerable youth on the basis of such sociodemographic risk factors as ethnic minority and immigrant status or socioeconomic disadvantage or based on performance in a single domain, such as achievement tests or grades in school (Cappella and Weinstein 2001; Farkas 2003). In contrast, the current study defined educational risk more broadly—as a composite index based on adolescents’ academic performance, school engagement, and educational expectations, and not on the basis of sociodemographics. This approach is better aligned with both theoretical perspectives emphasizing the role of personal resources and agency as a driving force of development (Bronfenbrenner and Morris 2006; Elder et al. 2003) and empirical evidence demonstrating that educational success, especially during the high school years, is about more than just getting good grades in school (Eccles and Wigfield 2002; McLoyd et al. 2009). For example, in understanding which students are most likely to drop out of high school, although grades are a key predictor, research suggests that school dropout is more accurately conceptualized as a process of disengagement from school, which is tightly linked and mutually influenced by students’ prior achievement and educational aspirations (Rumberger and Rotermund 2012). Operationalizing educational risk as a multidimensional construct is a key contribution of the current study, and hopefully future research will similarly explore more expansive strategies for understanding what it means to be at educational risk during secondary school.

In determining educational risk, more than one-third of the analytic sample displayed no evidence of risk at the beginning of high school, and the academic markers of risk used in the current study—including grades and course failure, school engagement, and educational expectations—are strongly and consistently related to postsecondary attendance and completion (Buchmann and DiPrete 2006; Eccles et al. 2004). In the current study, however, 14% of students displaying no educational risk in early high school fell off track academically. These students maintained A or B averages, were engaged in school, and had high expectations for their future educational prospects early in high school, yet they were not able to persist in their education beyond secondary school. Certainly college is not the only pathway to valuable and meaningful career paths (Rose 2005), and some who attend college do not persist to a degree or may not realize the promised payoff of a bachelor’s degree, struggling instead with underemployment or economic insecurity (Froidevaux et al. 2020; Lobo and Burke-Smalley 2018). The focus of the current study, however, was centrally on those students who professed strong expectations that they would attend college, and the goal was to determine the obstacles that left these expectations unrealized. Informed by life course theory (Elder et al. 2003), the key drivers of these disrupted educational trajectories were posited to be the normative and non-normative transitions that adolescents experienced during this critical developmental juncture (National Academies of Sciences, Engineering, and Medicine Ed. 2019).

When considering who was most likely to fall off track, relative to those who persisted to attain a bachelor’s degree or higher, initiation of sex and alcohol use both served as key indicators. These were the most common life course transitions included in the current study, and in adolescence and young adulthood, these behaviors frequently cooccur (Dogan et al. 2010; Meader et al. 2016). Given that earlier initiation of these behaviors in particular (versus marijuana and tobacco use) places young people at greater risk for pregnancy and parenthood (Deardorff et al. 2005; Stone et al. 2012) as well as for the development of later substance use disorders (Stone et al. 2012), it is not surprising that each would contribute to derailed educational trajectories. Changing neighborhoods but not changing schools was also linked to falling off track academically. Prior experimental research has suggested that residential moves, even when seemingly advantageous for youth, can exert long-term negative impacts when occurring in mid- or late adolescence (Chetty et al. 2016). In contrast, the evidence is decidedly mixed in terms of effects of school mobility, suggesting that it is the accumulation of school moves that seems to be particularly detrimental rather than the experience of a single school move (Reynolds et al. 2009). The research reported here adds to these bodies of evidence by highlighting the particularly detrimental toll that neighborhood moves can take on adolescents, including those who display no academic risks at the beginning of high school.

Family transitions also distinguished those who fell off track academically. This is consistent with prior research that highlights the challenges of divorce and repartnering for adolescents’ well-being across developmental domains, including in relation to achievement and educational attainment (see review by Cavanagh and Fomby 2019); this body of research, however, suggests that the effects of family instability are heterogeneous, varying by gender, race/ethnicity, and SES, which suggests future research should seek to disaggregate the effects of such transitions on educational attainment by key demographic markers. Moreover, it is unclear whether or not family transitions precipitated other life course transitions, such as school or residential moves or transitions into risky behaviors. Certainly the potential for cascading effects, wherein family transitions then initiate a series of other life events, is possible; given that family transitions, and specifically the entry of a new parent, were impactful in distinguishing who fell off-track whether considering the benchmark as 2-year or higher or 4-year or higher postsecondary participation, this is a critical area for future study.

In contrast, life course transitions tied to risky health behaviors as well as residential and school moves were not influential when considering the benchmark of 2-year college or more. As such, it seems that these interpersonal and personal life course transitions seem to have particular consequences for pursuing a bachelor’s degree. This is likely, in part, due to the fact that these types of transitions may compromise academic standing during high school, creating challenges in meeting the more rigorous enrollment standards of 4-year colleges and universities. The open admissions process and flexibility that community colleges offer (Bryant 2001) ensure that postsecondary education is not foreclosed for students who may have encountered interpersonal or personal life course transitions.

It must also be noted that the number of life course transitions experienced prior to the assessment of academic risk (i.e., 9th or 10th grade) was the strongest predictor of falling off track, regardless of whether the comparison group were those who attained a bachelor’s degree or higher or those who attained an associate’s degree or higher. Early transitions encompassed behaviors considered risky in adolescence—initiation of substance use, sexual activity, and dating—and life course theory suggests these off-time transitions, particularly when signaling accumulating disadvantages, can derail developmental trajectories (Elder et al. 2003), evidence of which emerged in the current study for educational attainment. The detrimental effects of the accumulation of these early risky transitions is also consistent with the cumulative risk model (Sameroff et al. 1987), and taken together, the findings highlight the need to attend to both when life course transitions occur and how these transitions cooccur in adolescents’ lives.

The current study used data from a nationally representative study of adolescents (Add Health) and a theoretically-informed model to examine educational pathways for adolescents displaying no early risk. Adolescents who show no academic risk through early high school yet fail to persist educationally beyond high school are often invisible in the literature, yet they represent a not insubstantial proportion of secondary students. The current study brings attention to an oft-ignored population and highlights how life course transitions derail these students’ educational trajectories.

The study, however, is not without its limitations. The ability to capture a wide range of potential normative and nonnormative life transitions occurring in adolescence was constrained by the constructs assessed in Add Health. There are likely other key transitions that adolescents experience during high school that may make an impact to their academic trajectories. For example, employment is a key transition in the lives of many youth, but it is complicated to capture the nuances of employment’s effect on adolescents’ academic trajectories. This is in no small part due to the fact that the timing (i.e., summer versus school year), quantity (i.e., number of hours worked), quality, and pay status (i.e., working for pay versus unpaid household work) are all linked to adolescents’ academic achievement, school engagement, and postsecondary pursuits (Staff et al. 2009; Zimmer-Gembeck and Mortimer 2006). In addition, although the current study included comprehensive measures of educational risk, all of these indicators were self-reported by adolescents, which are subject to self-report biases. As such, future studies should replicate the findings reported here with other measures of students’ academic outcomes (e.g., attendance and grades from school reports, teacher-rated engagement). Likewise, given the timing of the Add Health data collections, replication with more recent cohorts of adolescents will lend further support for the intersection of life course transitions and students’ educational trajectories.

Finally, although the current study highlights the critical nature of life course transitions for students educational trajectories, attention to the underlying content and interpersonal processes tied to these transitions is not integrated. The life course perspective places particular attention on how linked lives and the associated social ties matter for how development unfolds (Elder et al. 2003), and these interpersonal processes underlie the dynamics of why transitions are initiated and how such transitions are weathered. Delving into the aspects of linked lives that contribute to whether and how life course transitions matter for young people’s educational trajectories is an important area for future research.

Conclusion

Guided by life course and cumulative risk perspectives, the present study focuses on an often ignored population—students who displayed no early educational risk yet fell off track academically—and highlights the important roles of normative and non-normative life course transitions in derailing these students’ educational trajectories. Findings indicate that adolescents experiencing life course transitions, especially for those who initiated sexual and alcohol use behaviors and experienced family or residential transitions, were educationally vulnerable despite displaying no educational risks at the beginning of high school. Interestingly, experiencing a large number of the life course transitions prior to 9th and 10th grades did not seem to immediately derail educational trajectories but instead exacted a more long-term effect in terms of derailed educational attainment. Taken as a whole, the research presented here suggests that the methods and markers for identifying students at risk should be broadened to include both academics and the larger life course transitions common in adolescence.

Funding

The authors acknowledge grants from the William T. Grant Foundation and the National Institute for Child Health and Human Development (NICHD; K01HD087479) to the first author and from NICHD to the Population Research Center at UT Austin (P2CHD042849). Opinions reflect those of the authors and not necessarily those of the granting agencies.

Biographies

Aprile Benner is an associate professor at the University of Texas at Austin. Her major research interests focus on the development of low-income and race/ethnic minority youth, investigating how social contexts influence experiences of marginalization and discrimination, school transitions, and developmental outcomes during adolescence.

Shanting Chen is a doctoral student in the Department of Human Development and Family Sciences at the University of Texas at Austin. Her research interests center on discrimination and ethnic minority adolescents’ well-being.

Rashmita Mistry is a professor of Education and Vice Chair of Undergraduate Education at the University of California at Los Angeles. Her research examines the consequences of poverty and economic stress on child, youth and family well-being, children and adolescents’ perceptions, reasoning, and experiences of social and economic inequality, and social identity development.

Yishan Shen is an assistant professor at Texas State University. Her research interests center on how cultural and racial/ethnic processes (e.g., language brokering, acculturation, racial/ethnic socialization) interact with family socioeconomic status in influencing the psychosocial and academic outcomes of racial/ethnic minority and immigrant-origin adolescents.

Appendix

Table 4.

Crosstabulation results of falling off-track versus staying on-track by educational risk groups

No
Risk Group
 Low
Risk Group
 High
Risk Group
N % N % N %
Falling off-track 215 14% 661 38% 719 69%
Intermediary group 418 28% 555 32% 240 23%
Staying on-track 883 58% 508 29% 85 8%
Open in a new tab

Intermediary group represents students who attended or completed 2-year college, which was included for sensitivity analyses

Footnotes

Data Sharing and Declaration This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth).

Conflict of Interest The authors declare that they have no conflict of interest.

Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

Informed Consent Informed consent was obtained from all individual participants included in the study.

References

  1. Alfonso M, Bailey TR, & Scott M (2005). The educational outcomes of occupational sub-baccalaureate students: Evidence from the 1990s. Economics of Education Review, 24(2), 197–212. 10.1016/j.econedurev.2004.02.003. [DOI] [Google Scholar]
  2. Archambault I, Janosz M, Fallu J-S, & Pagani LS (2009). Student engagement and its relationship with early high school dropout. Journal of Adolescence, 32(3), 651–670. 10.1016/j.adolescence.2008.06.007. [DOI] [PubMed] [Google Scholar]
  3. Benner AD (2011). The transition to high school: current knowledge, future directions. Educational Psychology Review, 23(3), 299. 10.1007/s10648-011-9152-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bronfenbrenner U, & Morris PA (2006). The bioecological model of human development. In Lerner RM (Ed.), Handbook of child development: Vol. 1. Theoretical models of human development (6th ed., pp. 793–828). Wiley. 10.1002/9780470147658.chpsy0114. [DOI] [Google Scholar]
  5. Bryant AN (2001). ERIC review: Community college students recent findings and trends. Community College Review, 29(3), 77–93. 10.1177/009155210102900305. [DOI] [Google Scholar]
  6. Buchmann C, & DiPrete TA (2006). The growing female advantage in college completion: The role of family background and academic achievement. American Sociological Review, 71(4), 515–541. 10.1177/000312240607100401. [DOI] [Google Scholar]
  7. Burchinal MR, Roberts JE, Hooper S, & Zeisel SA (2000). Cumulative risk and early cognitive development: a comparison of statistical risk models. Developmental Psychology, 36, 793–807. 10.1037/0012-1649.36.6.793. [DOI] [PubMed] [Google Scholar]
  8. Cappella E, & Weinstein RS (2001). Turning around reading achievement: predictors of high school students’ academic resilience. Journal of Educational Psychology, 93(4), 758–771. 10.1037/0022-0663.93.4.758. [DOI] [Google Scholar]
  9. Cavanagh SE, & Fomby P (2011). Family instability, school context, and the academic careers of adolescents. Sociology of Education, 85, 81–97. 10.1177/0038040711427312. [DOI] [Google Scholar]
  10. Cavanagh SE, & Fomby P (2019). Family instability in the lives of American children. Annual Review of Sociology, 45(1), 493–513. 10.1146/annurev-soc-073018-022633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chan RY (2016). Understanding the purpose of higher education: an analysis of the economic and social benefits for completing a college degree. Journal of Education Policy, Planning and Administration, 6, 1–40. [Google Scholar]
  12. Chetty R, Hendren N, & Katz LF (2016). The effects of exposure to better neighborhoods on children: new evidence from the moving to opportunity experiment. American Economic Review, 106(4), 855–902. 10.1257/aer.20150572. [DOI] [PubMed] [Google Scholar]
  13. Collins WA, Welsh DP, & Furman W (2008). Adolescent romantic relationships. Annual Review of Psychology, 60, 631–652. 10.1146/annurev.psych.60.110707.163459. [DOI] [PubMed] [Google Scholar]
  14. Deardorff J, Gonzales NA, Christopher FS, Roosa MW, & Millsap RE (2005). Early puberty and adolescent pregnancy: the influence of alcohol use. Pediatrics, 116(6), 1451. 10.1542/peds.2005-0542. [DOI] [PubMed] [Google Scholar]
  15. Dogan SJ, Stockdale GD, Widaman KF, & Conger RD (2010). Developmental relations and patterns of change between alcohol use and number of sexual partners from adolescence through adulthood. Developmental Psychology, 46(6), 1747–1759. 10.1037/a0019655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Domina T, Conley A, & Farkas G (2011). The Link between educational expectations and effort in the college-for-all era. Sociology of Education, 84, 93–112. 10.1177/1941406411401808. [DOI] [Google Scholar]
  17. Duncan GJ, Dowsett CJ, Claessens A, Magnuson K, Huston AC, Klebanov P, Pagani LS, Feinstein L, Engel M, Brooks-Gunn J, Sexton H, Duckworth K, & Japel C (2007). School readiness and later achievement. Developmental Psychology, 43, 1428–1446. 10.1037/0012-1649.43.6.1428. [DOI] [PubMed] [Google Scholar]
  18. Eccles JS, Vida MN, & Barber B (2004). The relation of early adolescents’ college plans and both academic ability and task-value beliefs to subsequent college enrollment. The Journal of Early Adolescence, 24(1), 63–77. 10.1177/0272431603260919. [DOI] [Google Scholar]
  19. Eccles JS, & Wigfield A (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. 10.1146/annurev.psych.53.100901.135153. [DOI] [PubMed] [Google Scholar]
  20. Elder GH, Johnson MK, & Crosnoe R (2003). The emergence and development of life course theory. In Mortimer JT & Shanahan MJ (Eds.), Handbook of the Life Course (pp. 3–19). USA: Springer. 10.1007/978-0-306-48247-2_1. [DOI] [Google Scholar]
  21. Enders C. (2010). Applied missing data analysis. New York, NY: Guilford. [Google Scholar]
  22. Entwisle DR, Alexander KL, & Olson LS (2005). First grade and educational attainment by age 22: a new story. American Journal of Sociology, 110, 1458–1502. 10.1086/428444. [DOI] [Google Scholar]
  23. Farkas. (2003). Racial disparities and discrimination in education: what do we know, how do we know it, and what do we need to know? Teachers College Record, 105, 1119–1146. [Google Scholar]
  24. Feinstein L, & Peck SC (2008). Unexpected pathways through education: Why do some students not succeed in school and what helps others beat the odds? Journal of Social Issues, 64, 1–20. 10.1111/j.1540-4560.2008.00545.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Franklin BJ, & Trouard SB (2016). Comparing dropout predictors for two state-level panels using Grade 6 and Grade 8 data. The Journal of Educational Research, 109(6), 631–639. 10.1080/00220671.2015.1016601. [DOI] [Google Scholar]
  26. Fredricks JA, Blumenfeld PC, & Paris AH (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. 10.3102/00346543074001059. [DOI] [Google Scholar]
  27. Friedman TL (2005). The world is flat: A brief history of the twenty-first century. New York, NY: Farrar, Straus and Giroux. [Google Scholar]
  28. Froidevaux A, Koopmann J, Wang M, & Bamberger P (2020). Is student loan debt good or bad for full-time employment upon graduation from college? Journal of Applied Psychology, 105 (11), 1246–1261. 10.1037/apl0000487. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Giordano PC, Phelps KD, Manning WD, & Longmore MA (2008). Adolescent academic achievement and romantic relationships. Social Science Research, 37, 37–54. 10.1016/j.ssresearch.2007.06.004. [DOI] [Google Scholar]
  30. Harackiewicz JM, Barron KE, Tauer JM, & Elliot AJ (2002). Predicting success in college: a longitudinal study of achievement goals and ability measures as predictors of interest and performance from freshman year through graduation. Journal of Educational Psychology, 94(3), 562–575. 10.1037/0022-0663.94.3.562. [DOI] [Google Scholar]
  31. Harris KM, Halpern CT, Whitsel E, Hussey J, Tabor J, Entzel P, & Udry JR (2009). The national longitudinal study of adolescent to adult health: research design. http://www.Cpc.Unc.Edu/Projects/Addhealth/Design. [Google Scholar]
  32. Haskins R. (2009). Promoting economic mobility by increasing postsecondary education. Economic Mobility Project. 10.1177/003804070808100304. [DOI] [Google Scholar]
  33. Hayward MD, Hummer RA, & Sasson I (2015). Trends and group differences in the association between educational attainment and U.S. adult mortality: implications for understanding education’s causal influence. Special Issue: Educational Attainment and Adult Health: Contextualizing Causality, 127, 8–18. 10.1016/j.socscimed.2014.11.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Henry KL, Knight KE, & Thornberry TP (2012). School disengagement as a predictor of dropout, delinquency, and problem substance use during adolescence and early adulthood. Journal of Youth and Adolescence, 41, 156–166. 10.1007/s10964-011-9665-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Huang DYC, Lanza HI, Murphy DA, & Hser Y-I (2012). Parallel development of risk behaviors in adolescence: potential pathways to co-occurrence. International Journal of Behavioral Development, 36, 247–257. 10.1177/0165025412442870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Isen A, & Stevenson B (2010). Women’s education and family behavior: trends in marriage, divorce and fertility. National Bureau of Economic Research. 10.3386/w15725. [DOI] [Google Scholar]
  37. Janosz M, Archambault I, Morizot J, & Pagani LS (2008). School engagement trajectories and their differential predictive relations to dropout. Journal of Social Issues, 64, 21–40. 10.1111/j.1540-4560.2008.00546.x. [DOI] [Google Scholar]
  38. Langenkamp AG (2009). Following different pathways: social integration, achievement, and the transition to high school. American Journal of Education, 116, 69–97. 10.1086/605101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lawson MA, & Masyn KE (2015). Analyzing profiles, predictors, and consequences of student engagement dispositions. Journal of School Psychology, 53(1), 63–86. 10.1016/j.jsp.2014.11.004. [DOI] [PubMed] [Google Scholar]
  40. Lee D, & McLanahan S (2015). Family structure transitions and child development: Instability, selection, and population heterogeneity. American Sociological Review, 80(4), 738–763. 10.1177/0003122415592129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Leonhardt D. (2009). Job losses show breadth of recession. New York Times. http://www.nytimes.com/2009/03/04/business/04leonhardt.html. [Google Scholar]
  42. Lobo BJ, & Burke-Smalley LA (2018). An empirical investigation of the financial value of a college degree. Education Economics, 26 (1), 78–92. 10.1080/09645292.2017.1332167. [DOI] [Google Scholar]
  43. Lochner L. (2020). Education and crime. In Bradley S & Green C (Eds.), The Economics of Education (2nd ed.) (pp. 109–117). London: Academic Press. 10.1016/B978-0-12-815391-8.00009-4. [DOI] [Google Scholar]
  44. McFarland J, Hussar B, Zhang J, Wang X, Wang K, Hein S, Diliberti M, Cataldi EF, Mann FB, Barmer A, Nachazel T, Barnett M, & Purcell S (2019). The condition of education 2019 (NCES 2019-144). Washington, DC: National Center for Education Statistics. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2019144. [Google Scholar]
  45. McLoyd VC, Kaplan R, Purtell KM, Bagley E, Hardaway CR, & Smalls C (2009). Poverty and socioeconomic disadvantage in adolescence. In Lerner R & Steinberg L (Eds.), Handbook of Adolescent Psychology (3rd ed., Vol. 2, pp. 444–491). Hoboken, NJ: John Wiley. [Google Scholar]
  46. McMahon WW, & Walter W (2009). Higher learning, greater good: the private and social benefits of higher education. Baltimore: Johns Hopkins University Press. [Google Scholar]
  47. Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, Power C, White M, & Sowden AJ (2016). A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health, 16(1), 657 10.1186/s12889-016-3373-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Metzger MW, Fowler PJ, Anderson CL, & Lindsay CA (2015). Residential mobility during adolescence: Do even “upward” moves predict dropout risk? Social Science Research, 53, 218–230. 10.1016/j.ssresearch.2015.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. National Academies of Sciences, Engineering, and Medicine (Ed.) (2019). The promise of adolescence: Realizing opportunity for all youth. Washington, DC: The National Academies Press. [PubMed] [Google Scholar]
  50. Neemann J, Hubbard J, & Masten AS (1995). The changing importance of romantic relationship involvement to competence from late childhood to late adolescence. Development and Psychopathology, 7, 727–750. 10.1017/S0954579400006817. [DOI] [Google Scholar]
  51. Reynolds AJ, Chen C-C, & Herbers JE (2009). School mobility and educational success: a research synthesis and evidence on prevention. Workshop on the Impact of Mobility and Change on the Lives of Young Children, Schools, and Neighborhoods, June, 29-30. [Google Scholar]
  52. Reynolds J, Stewart M, Macdonald R, & Sischo L (2014). Have adolescents become too ambitious? High school seniors’ Educational and occupational plans, 1976 to 2000. Social Problems, 53, 186–206. 10.1525/sp.2006.53.2.186. [DOI] [Google Scholar]
  53. Rose M. (2005). The mind at work: valuing the intelligence of the American worker. New York, NY: Penguin. [Google Scholar]
  54. Rumberger RW, & Rotermund S (2012). The relationship between engagement and high school dropout. In Christenson SL, Reschly AL, & Wylie C (Eds.), Handbook of Research on Student Engagement (pp. 491–513). USA: Springer. 10.1007/978-1-4614-2018-7_24. [DOI] [Google Scholar]
  55. Sameroff AJ, Seifer R, Baldwin A, & Baldwin C (1993). Stability of intelligence from preschool to adolescence: The influence of social and family risk factors. Child Development, 64, 80–97. 10.1111/j.1467-8624.1993.tb02896.x. [DOI] [PubMed] [Google Scholar]
  56. Sameroff AJ, Seifer R, Zax M, & Barocas R (1987). Early indicators of developmental risk: Rochester longitudinal study. Schizophrenia Bulletin, 13, 383–394. 10.1093/schbul/13.3.383. [DOI] [PubMed] [Google Scholar]
  57. Schulenberg J, Maslowsky J, Maggs JL, & Zucker RA (2018). Development matters: Taking the long view on substance use during adolescence and the transition to adulthood. In Monti PM, Colby SM, & Tevyaw TO (Eds.), Brief interventions for adolescent alcohol and substance abuse (pp. 13–49). New York, NY: The Guilford Press. [Google Scholar]
  58. Shapiro D, Dundar A, Huie F, Wakhungu P, Yuan X, Nathan A, & Hwang Y (2017). A national view of student attainment rates by race and ethnicity—Fall 2010 cohort (Signature Report No. 12b). Herndon, VA: National Student Clearinghouse Research Center. [Google Scholar]
  59. Sharma S, Mustanski B, Dick D, Bolland J, & Kertes DA (2019). Protective factors buffer life stress and behavioral health outcomes among high-risk youth. Journal of Abnormal Child Psychology, 47(8), 1289–1301. 10.1007/s10802-019-00515-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sommerfeld AK (2016). Education as a collective accomplishment: How personal, peer, and parent expectations interact to promote degree attainment. Social Psychology of Education, 19(2), 345–365. 10.1007/s11218-015-9325-7. [DOI] [Google Scholar]
  61. Staff J, Messersmith EE, & Schulenberg JE (2009). Adolescents and the world of work. In Handbook of adolescent psychology: Contextual influences on adolescent development, Vol. 2, 3rd ed. (pp. 270–313). Hoboken, NJ: John Wiley & Sons, Inc. 10.1002/9780470479193.adlpsy002009. [DOI] [Google Scholar]
  62. Stearns E, Moller S, Blau J, & Potochnick S (2007). Staying back and dropping out: The relationship between grade retention and school dropout. Sociology of Education, 80, 210–240. 10.1177/003804070708000302. [DOI] [Google Scholar]
  63. Stone AL, Becker LG, Huber AM, & Catalano RF (2012). Review of risk and protective factors of substance use and problem use in emerging adulthood. Addictive Behaviors, 37(7), 747–775. 10.1016/j.addbeh.2012.02.014. [DOI] [PubMed] [Google Scholar]
  64. Wood D, Kurtz-Costes B, & Copping KE (2011). Gender differences in motivational pathways to college for middle class African American youths. Developmental Psychology, 47, 961–968. 10.1037/a0023745. [DOI] [PubMed] [Google Scholar]
  65. Zhang J, Savla J, & Cheng H-L (2019). Cumulative risk and immigrant youth’s health and educational achievement: Mediating effects of inter- and intra-familial social capital. Youth & Society, 51(6), 793–813. 10.1177/0044118X17717501. [DOI] [Google Scholar]
  66. Zimmer-Gembeck MJ, & Mortimer JT (2006). Adolescent work, vocational development, and education. Review of Educational Research, 76(4), 537–566. 10.3102/00346543076004537. [DOI] [PMC free article] [PubMed] [Google Scholar]

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