Family instability during early and middle adolescence

Journal of Applied Developmental Psychology 29 (2008) 380–392

Contents lists available at ScienceDirect

Journal of Applied Developmental Psychology

Family instability during early and middle adolescence

Lyscha A. Marcynyszyn⁎, Gary W. Evans, John EckenrodeDepartment of Human Development, Cornell University, USA

a r t i c l e i n f o

⁎ Corresponding author. Department of Human DevE-mail address: [email protected] (L.A. Marcynysz

0193-3973/$ – see front matter © 2008 Elsevier Inc. Adoi:10.1016/j.appdev.2008.06.001

a b s t r a c t

Available online 11 July 2008
Two studies investigated associations between family instability (changes in parents' intimatepartners,workhours, residence, children's schools) and adolescent adjustment. In Study 1 (N = 141,M age = 15.23 years), instability was associated with increased caregiver-reported externalizingand internalizing behaviors (including youth-reported cigarette use), reduced teacher-reportedfrustration tolerance, social skills, task orientation, and lower academic grades. Logistic regressionresults for instability exposure showed an increased risk for school suspensions, Person in Need ofSupervision status, binge drinking, and marijuana use. In Study 2 (N = 225,M age = 13.37 years),instability was linked to adolescent-reported externalizing and internalizing behaviors, teacher-reported disruptions, and lower English and math grades. Key sociodemographic factors andnegative life eventswere statistically controlled in regression analyses. Results indicate that amoretheoretically coherent measure of family instability, which is distinct from negative life events,may prove valuable in understanding the potentially adverse influence of instability on youth.
© 2008 Elsevier Inc. All rights reserved.

Keywords:Family instabilityAdjustmentAdolescenceInternalizing behaviorsExternalizing behaviorsSchool gradesSocial skills

1. Introduction

Across America, instability in the home and school lives of children has become the norm. Nearly half of all American childrenmoved to a different home at least once between 1995 and 2000 (Franklin, 2003). During the early 1990s, one in six U.S. thirdgraders attended three or more different schools since beginning first grade (U.S. General Accounting Office, 1994). Not includingnormative transitions between elementary, middle, and high schools, 40% of 12- to 17-year-olds in a national sample had changedschools at least once (Lugaila, 2003). Despite a static divorce rate over the past two decades, children increasingly experiencediscontinuity in family structure as primary caregivers shift in and out of marital-like relationships (Bumpass & Lu, 2000). Workingmothers who are young, low-income, single, and non-White are most likely to have unpredictable work hours that change weekly(Golden, 2001; Han, 2005). To understand the cumulative effects of these changes on children, we examined the effects ofinstability within home, school, and neighborhood contexts on adolescent outcomes in two longitudinal samples of rural andsemi-rural youth in upstate New York. Instability was defined as changes in (a) children's residences, (b) children's schools,(c) parents' intimate partners, and (d) parents' work hours.

Bronfenbrenner (1979) described proximal processes, exchanges of energy between the developing person and theenvironment, as the engines of human development. To be effective, such processes should occur on a regular basis and overextended periods of time. During childhood and early adolescence, proximal processes occur within three key ecological contexts:home, school, and neighborhood (Bronfenbrenner, 1979; Bronfenbrenner & Evans, 2000).

Regular, predictable, and supportive exchanges with adults and peers in these settings promote competent development.Alternatively, disruptions to the continuity and predictability of these key contexts threaten competent development(Bronfenbrenner, 1979; Bronfenbrenner & Evans, 2000; Bronfenbrenner &Morris, 1998). As such, family instability is distinguishedfrom a broader class of life events that do not fundamentally threaten the coherence of key developmental contexts (although it is

elopment, Cornell University, MVR Hall, Ithaca, NY 14853-4401, USA. Tel.: +1 607 351 2219.yn).

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381L.A. Marcynyszyn et al. / Journal of Applied Developmental Psychology 29 (2008) 380–392

acknowledged that some of these life events are potentially stressful and require readjustments on the part of the child). Likewise,although instability may be correlated with poverty and other measures of socioeconomic status (e.g., parental education), it is nota proxy for these risk factors. Indeed, instability might mediate the effects of poverty on child outcomes (Evans, 2004).

To date, only a handful of studies have examined the influence of instability (Ackerman, Brown, & Izard, 2003; Ackerman, Kogos,Youngstrom, Schoff, & Izard, 1999; Forman & Davies, 2003; Herrenkohl, Herrenkohl, & Egolf, 2003; Milan, Pinderhughes, & theConduct Problems Prevention Research Group [CPPRG], 2006; Moore, Vandivere, & Ehrle, 2000; Simmons, Burgeson, Carlton-Ford,& Blyth, 1987). As definitions of instability vary somewhat across these studies, it is difficult to achieve clarity regarding the uniqueinfluence of instability on child adjustment. One of our objectives in the present paper is to provide greater specificity to theinstability measure relative to other indicators of adversity such as negative life events, illness, or poverty. To do so, we focus onconstruct of development— What is instability? How does it differ from other constructs? — and tests of its unique effects. Whilework on explanatory factors is also needed, such questions are premature until greater clarity in the content of the construct isachieved. The present study helps to clarify instability because the heterogeneous economic backgrounds of the sample children inStudy 2 enabled us to disentangle correlates of instability from those of income — a contribution that only two other studies havemade (Milan et al., 2006; Moore et al., 2000) and only one in the context of adolescence (Moore et al., 2000). We also provide amulti-informant assessment of adolescent outcomes across two different samples.

Ackerman et al. (Ackerman et al., 1999; Ackerman, Brown, & Izard, 2003) defined instability in terms of the number of residencesand intimate adult relationships involving a child's primary caregiver, the number of families withwhom the child lived, the numberof serious illnesses the child experienced during the past five years and, finally, the number of negative life events, such as changes inparental employment or deaths of relatives, the child experienced over the most recent six months. Findings from their 1999 studyrevealed a significant positive relationship between family instability and externalizing behaviors among racially diverse, urban five-year-olds, and between family instability and internalizing behaviors among seven-year-olds.Moreover, relations between instabilityand maladjustment were stronger for temperamentally difficult children and among children who had pre-existing problembehaviors.

Milan et al. (2006) examined family instability on child externalizing and internalizing problems over a 6-year elementaryschool period as a part of the Fast Track investigation. In their study, family instability was defined as a count of seven types ofevents during the past year: residential moves, family member deaths, divorces or separations from cohabitating partners,remarriage/reconciliation/entries into homes of new partners, temporary parent–child separations, sibling births or new children'sentries into the home, and parental job losses or changes in employment status. Cumulative family instability predicted children'sexternalizing and internalizing behavior trajectories during elementary school. After controls for early maladjustment anddemographic factors were included in the model, timing effects were observed for levels of externalizing behaviors among thirdgraders, such that stronger relations were detected for early experiences of family instability during kindergarten compared withcumulative family instability experiences in first through third grades.

Forman and Davies (2003) defined instability as the number of times disruptive events occurred in five family domains over themost recent five years: (a) changes in residence, (b) changes in the primary or secondary caregiver, (c) transitions in romanticrelationships of the primary caregiver, (d) job and income loss, and (e) death or serious illness of a close family member. Theirresearch with a predominantly White, suburban sample of early adolescents showed that instability contributed to adolescentmaladjustment, and that this association was mediated by parenting difficulties and adolescents' perceptions of family insecurity.

Moore et al. (2000) also developed a measure of instability, which they called turbulence. In their study of 22,729 6- to 17-year-olds from the 1997 National Survey of America's Families, 6% of children nationwide experienced turbulence, defined as two ormore of six possible changes in residence, school, parental employment, or health within the past year. For children below thepoverty line, this number doubled to 13%.

Simmons et al. (1987) examined the effects of recent cumulative change acrossmultiple contexts for 447 urban,White sixth andseventh graders. Cumulative change was defined by changes in schooling, residence, or family structure brought about by parentaldeath, divorce, or remarriage, or pubertal development and early dating behavior. Overall, neither boys' nor girls' self-esteemwasinfluenced by individual factors. In contrast, as cumulative transitions increased for boys, GPA and extracurricular participation inseventh grade decreased significantly even after sixth-grade levels of these outcomes were statistically controlled. For girls, theeffects of transitions on GPA were non-linear, such that after one transition each additional transition brought lower GPAs forseventh-grade girls. These findings underscore that it is not simply the addition of discrete stressors that contributes to declines inacademic performance, but rather the difficulty children experience when coping with transitions in several areas of their lives.Herrenkohl et al. (2003) also found that caregiver and residential instability predicted adolescents' problem behaviors over andabove socioeconomic background and childhood maltreatment experiences.

We have defined instability more broadly, viewing it as comprisingmore than simply residential moves and partner changes, inorder to capture structural changes within the micro-contexts of childhood and adolescence. We offer a more concise definitionthan some prior studies, however, by excluding life events (e.g., child's illness, death of a grandparent) that do not typically alter thecoherence and predictability of immediate ecological contexts where most proximal processes are developed and sustained. Inaddition to these conceptual changes, we extend prior work on instability to a different sample of youth. As indicated above, mostprior work on chaos and children and youth development has focused on urban, predominantly African American samples. Hereinwe examine instability among predominantly White youth living in small towns and rural areas.

The present studies had two aims. The first aim was to refine the instability construct. Although our instability constructdovetails with those of previous research, it is focused more precisely on structural changes in children's microsystems. Weestimated the effects of instability net of socioeconomic influences, negative life events, and other forms of childhood adversity. To

382 L.A. Marcynyszyn et al. / Journal of Applied Developmental Psychology 29 (2008) 380–392

our knowledge this is the first paper on family instability to disaggregate structural changes in home, school, and neighborhoodcontexts from a more general class of negative life events.

The second aimwas to replicate and extend previous research on the effects of family instability on child outcomes. We presenttwo studies with similar measures of instability and similar outcomes. Prior research has tended to focus almost exclusively onyounger children from urban low-income or ethnicminority samples or conversely early adolescents frommore affluent, suburbanfamilies. The present studies contribute to the literature by examining the effects of instability beyond the developmental periodsof childhood and early adolescence with families that span a wider range of income levels and who reside in rural America. Ourreview of the literature indicates that this is only the second paper on instability to exhibit such socioeconomic variability withinan adolescent sample. Moreover, we included a broader range of outcomes than those associated with externalizing andinternalizing behaviors and relied upon multiple methods to index adolescent adjustment.

2. Study 1: Elmira

2.1. Method

2.1.1. Participants

2.1.1.1. Time 1. The Nurse–Family Partnership (NFP) study was conducted in and around Elmira, a small city in central New YorkStatewith a population of approximately 40,000. See Olds et al. (1997,1998) for details on the NFP. Pregnant womenwere recruitedfor the NFP from doctor's offices and a health clinic between 1978 and 1980. Of the 500 eligible women who were invited toparticipate in the study, 400 enrolled. There were no differences in age, education, or marital status of the women who chose toparticipate and those who declined. There was a difference by ethnicity. Eighty percent of eligible White women compared with96% of eligible African American women agreed to take part in the study. The four hundred first-time mothers were randomlyassigned to a nurse visitation program or a control group. Because the intervention had several effects on maternal and childoutcomes (Olds et al., 1997, 1998) and because we wanted to match these data to another community sample, this study includedonly 184 control group families. Of these 184 women, 90% of the mother/child dyads eligible for follow-up completed assessmentswhen the mother's first-born child (study child) was 15 years old. Some families were not eligible for follow-up because either themother or the study child was no longer living or the study child was adopted. Of the eligible families, 12 families could not belocated, and 12 families declined to participate. The final Elmira sample included 141 parent–adolescent dyads.

2.1.1.2. Time 2. At Time 2, 16 adolescents no longer resided with their biological mothers. Of these youth, 10 had frequent contactwith their mothers or had only recently left home (4 were in the custody of other relatives, 2 were in detention centers, 2 were inresidential care, and 2 were in foster care). Given the recent separation in mother–child living arrangements, maternal reports ofinstability were used for these youth. In contrast, the remaining 6 adolescents had spent the majority of their lives separated fromtheir biological mothers. For these youth, maternal and custodial parent recordswere combined based on the custody transfer dateto create the instability index.

Attrition analyses suggest that the rates of completed assessments at the Time 2 adolescent follow-upwere unrelated to baselinedemographic characteristics. The mean yearly household income was $23,388 (SD = $15,674). Forty-seven percent of the primarycaregivers had graduated from high school or earned a GED, 36% had completed at least some college, and 17% had less than a highschool education. Fifty-two percent of caregivers weremarried. Almost all of the adolescents were 15 years old (M = 182.79months,SD = 4.00). Fifty percent of the youthweremale. Seventy-eight percent of the youthwereWhite, 8%were bothAfrican American andWhite, 6% were African American, 2% were either Latino or Native American, and 6% were of other ethnic backgrounds.

2.1.2. ProcedureAt Time 2, interviews were conducted with adolescents and their primary caregivers (predominantly mothers). When

geographic distances precluded in-person interviews, phone interviews were administered. Caregivers were interviewed at homeand adolescents were interviewed at their respective schools. Mothers completed a life-history calendar that was designed to helpthem recall work histories, residential mobility, and housing arrangements since the study child's birth. Adolescents responded tointerviewer questions and self-administered questionnaires. Caregivers and adolescents were paid for their participation. Finally,math teachers completed surveys on the adolescents' classroom behaviors.

2.1.3. MeasuresReports from the primary caregiver who was most knowledgeable about the study child's life history and current adjustment

were used. Maternal reports of family instability were used for nearly all cases. The outcome measures were based on schoolrecords and adolescent, parent, and teacher reports.

2.1.3.1. Family instability. The family instability index was based on four classes of disruptive microsystem changes since the studychild's birth. The index was defined by the total number of (a) primary caregivers' intimate partners (M = 1.67, SD = 1.00),(b) primary caregivers' work hour changes (M = 5.25, SD = 3.37), (c) residence changes (M = 5.74, SD = 4.03), and (d) school transfers(M = 3.35, SD = 2.27) (see Table 1). School transfer datawere derived from school recordswhile the remaining events were based oncaregiver reports. For the partner change variable, a partner was defined as an individual who spent at least four nights a week in

Table 1Means, standard deviations, ranges, and sample sizes for the variables that comprise the lifetime instability construct in Study 1 (Elmira sample)

Variable M SD Range N

Parent's partner changes 1.67 1.08 0–5 141Parent's work hour changes 5.25 3.37 0–15 140Residence changes 5.74 4.03 0–21 140School transfers 3.35 2.27 0–11 134


the home for at least one month. The work hour change variable was constructed to measure changes of 5 h or more per weekincluding terminations for all jobs that caregivers held for at least one month. While we intended to investigate unscheduledschool transfers, the Elmira data did not specify school type (i.e., elementary, middle, or high school) so we were unable todistinguish between the scheduled and the potentially more disruptive school transfers. Thus, all school changes were counted.

Confirmatory factor analyses with oblique rotations showed that these four variables loaded on one factor (α = .62). Individualloadings for partner changes, work hour changes, residential mobility, and school transfers were .70, .54, .73, and .76 respectively.Although the instability index correlated with variables typically associated with poverty, such as (a) socioeconomic status at Time1 (Hollingshead, 1976), (b) income-to-needs ratios at Time 2, (c) lifetime AFDC use, and (d) caregiver report of current economichardship (Lempers, Clark-Lempers, & Simons, 1989), rs(136–140) = −.15, −.25, .27, and .05, ps = .07, .00, .27 and ns, respectively,these latter variables loaded uniquely on different factors and their inclusion reduced the instability alpha to .14.

Likewise, when individual negative life events that occurred over the past 15 years (family member hospitalization, parent/sibling/close friend death, natural disaster) were included in factor analyses with the individual instability variables, the eventsloaded uniquely on other factors, suggesting that our instability construct is distinct from both economic and negative life eventvariables. To understand the cumulative influence of instability, each of the four indicator variables were transformed into z-scoresand then aggregated to form a composite measure of family instability. Aggregation in this manner fit our a priori hypothesis aboutthe covariation of change across these familial, residential, and school contexts and reduced potential redundancy in the analyses(each of the individual instability indicators was associated with at least two of the outcome measures).

2.1.3.2. Child, caregiver, and family covariates. Caregiver education and family income were used as covariates in all regressionanalyses. Caregiver education assessed at Time 2 was coded as continuous data (years of school), with GED attainment scored as 12.Time 2 household income from all sources was converted into income-to-needs ratios in order to account for family size and annualfluctuations in the economy. Ratios were calculated by dividing current total family income by a federal poverty line, specific to theadult–child household composition for a given interview year (1994, 1995, or 1996). A value of one signifies that a family is at thepoverty line, whereas values below or above one correspond to incomes below or above the poverty line. Adolescent's racialminority status (0 = White; 1 = not White) was also used as a covariate in all regression analyses. The negative life events measurewas based the on caregiver's responses to a series of questions, adapted from the National Comorbidity Survey (Kessler, 1990–1992),about stressful life events that occurred to them since the study childwas born. Questions about deaths (parent/sibling/close friend),natural disasters, and hospitalizations were aggregated to form a negative life events index (M = 1.50, SD = 1.00, range = 0–5).

2.1.3.3. Externalizing and internalizing behaviors. Caregiver ratings on the Child Behavior Checklist (CBCL; Achenbach, 1991) andadolescent self-report on the Youth Self Report (YSR; Achenbach, 1991) were used to assess the incidence of adolescents'externalizing and internalizing behavior problems during the past six months. The CBCL and YSR have demonstrated validity asinstruments for evaluating problem behaviors among youth (Brown & Achenbach, 1993). Caregivers rated each CBCL item on athree-point Likert-type scale (0 = not true; 1 = somewhat or sometimes true; 2 = very true or often true). The CBCL externalizing scale(α = .93) consists of 20 questions on aggression (e.g., cruelty, bullying, meanness) and 13 questions on delinquency (e.g., truancy)possible score range = 0 to 66. The internalizing scale (α = .90) is composed of three subscales: 14 items on anxiety/depression (e.g.,unhappy, sad), 9 items on social withdrawal (e.g., would rather be alone thanwith others), and 9 items on somatic behaviors (e.g.,feels dizzy) possible score range = 0 to 62. Following Achenbach's (1991) scoring procedures, one item, which is used on both theanxiety/depression and withdrawal subscales was included only once in the construction of the internalizing scale.

Overall, the YSR is congruent with the CBCL in content and rating protocol. The YSR externalizing scale (α = .89) consists of a 19-item aggression subscale and an 11-item delinquency subscale. The internalizing scale (α = .87) includes three subscales: 16questions on anxiety/depression, 7 questions on social withdrawal, and 9 questions on somatic behaviors. YSR scores for theexternalizing and internalizing scales can range from 0 to 60 and 0 to 62, respectively. Raw, untransformed scores were utilized inour analyses given the approximately normal distributions of the scales.

2.1.3.4. Classroom behaviors. Current math teachers assessed competent functioning by using the Teacher–Child Rating Scale (T-CRS; Hightower et al., 1986). While both math and English teachers completed the T-CRS, response rates were consistently higheramongmath teachers and thus their reports were used. Each itemwas rated on a five-point scale with high scores reflecting betteradjustment. The T-CRS taps frustration tolerance (e.g., accepts things not going his/her way), peer social skills (e.g., is friendlytoward peers), and task orientation (e.g., functions well even with distractions). Each subscale included five items (possible scorerange = 5 to 25) with corresponding sample Cronbach's alphas of .94, .95, and .95, respectively. For the frustration tolerance, socialskill, and task orientation subscales, complete data were available for 70%, 67%, and 72% of the sample, respectively. Analyses were

Table 2Means, standard deviations, sample sizes, and correlations among Study 1 (Elmira sample) variables

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

1. InstabilityParent report2. Externalizing .39⁎⁎3. Internalizing .41⁎⁎ .71⁎⁎4. Suspensions .26⁎⁎ .47⁎⁎ .20⁎

Adol. report5. Externalizing .17⁎ .39⁎⁎ .26⁎⁎ .28⁎⁎6. Internalizing .15+ .20⁎⁎ .30⁎⁎ .02 .53⁎⁎7. Cigarette use .30⁎⁎ .25⁎⁎ .24⁎ .13 .19⁎ .038. Binge drink .21⁎ .30⁎⁎ .16+ .25⁎⁎ .40⁎⁎ .37⁎⁎ .24⁎⁎9. Marijuana use .28⁎⁎ .25⁎⁎ .14 .30⁎⁎ .36⁎⁎ .26⁎⁎ .49⁎⁎ .49⁎⁎10. PINS status .21⁎ .43⁎⁎ .37⁎⁎ .30⁎⁎ .15+ .12 .41⁎⁎ .07 .23⁎11. Conviction .04 .20⁎ −.00 .27⁎⁎ .13 .04 .31⁎⁎ .27⁎⁎ .27⁎⁎ .35⁎⁎

Teacher report12. F Tolerance −.27⁎⁎ −.35⁎⁎ −.05 −.40⁎⁎ −.28⁎ −.10 −.19+ −.23⁎ −.15 −.26⁎ −.27⁎13. Social skills −.27⁎⁎ −.20+ −.07 −.13 −.10 .05 −.15 .01 .05 −.08 −.12 .66⁎⁎14. T Orientation −.18+ −.23⁎ .02 −.30⁎⁎ −.23⁎ .03 −.16 −.16 −.08 −.21⁎ −.30⁎⁎ .79⁎⁎ .71⁎⁎

Grades 7–915. English −.19⁎ −.32⁎⁎ −.08 −.41⁎⁎ −.20⁎ .07 −.17+ −.22⁎ −.20⁎ −.17+ −.18⁎ .47⁎⁎ .39⁎⁎ .56⁎⁎16. Math −.19⁎ −.31⁎⁎ −.10 −.37⁎⁎ −.13 −.01 −.13 −.19⁎ −.30⁎⁎ −.24⁎⁎ −.05 .34⁎⁎ .32⁎⁎ .39⁎⁎ .61⁎⁎17. Science −.24⁎⁎ −.30⁎⁎ −.12 −.49⁎⁎ −.15+ .02 −.15+ −.20⁎ −.23⁎ −.19⁎ −.07 .48⁎⁎ .40⁎⁎ .50⁎⁎ .76⁎⁎ .66⁎⁎

Demographics18. A not White .13 .13 .11 .30⁎⁎ .04 .05 .11 .01 .11 .14 .02 −.09 .06 −.02 −.02 −.17⁎ −.0819. Inc-to-needs −.25⁎⁎ −.10 −.05 −.20⁎ −.01 −.04 −.11 −.05 −.01 −.13 −.10 −.03 .03 −.04 .12 −.00 .13 −.28⁎⁎20. Parent Educ. .02 −.05 −.06 −.07 −.01 .01 −.30⁎⁎ −.09 −.14 −.17+ −.06 .07 .13 .07 .17+ .10 .15 −.03 .22⁎21. Neg. life events .12 .12 .16+ .18⁎ .15+ .06 .13 −.01 −.01 .08 .01 .09 −.06 .10 −.01 .10 −.01 .06 −.06 −.08

Mean −.02 12.14 7.48 .49 13.48 11.61 1.69 .12 .32 .16 .15 16.41 16.43 14.90 2.16 2.20 2.00 .22 1.51 12.14 1.56SD 2.69 10.90 7.58 .50 8.37 7.59 3.71 .33 .47 .37 .36 5.18 4.92 5.73 .93 .96 1.00 .42 1.00 1.66 1.00n 140 134 134 141 140 140 140 141 140 140 141 98 95 102 129 129 129 141 137 132 141

Note. Adol. Report = adolescent report, PINS = Person in Need of Supervision, F Tolerance = frustration tolerance, T Orientation = task orientation, A not White = adolescent not White, Inc-to-Needs = income-to-needs, ParentEduc. = parent education, Neg. life events = negative life events.+pb .10. *pb .05. **pb .01.

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conducted to determine whether these response rates were a by-product of adolescents' school transfers. For example, a teachermay be less inclined to evaluate a student who recently transferred into her classroom. No school transfer effects were uncovered.

2.1.3.5. Academic achievement. School records were used tomeasure academic achievement. Each student's letter grades in English,math, and science during grades 7–9 were averaged over three years in order to obtain a stable index of academic performance.Grades were measured on a 5-point scale (range = 0 to 4) with higher values reflecting superior academic performance.

2.1.3.6. Criminal justice contact, substance use, and school suspensions. Alcohol and marijuana use during the past six months wereassessed using the Adolescent Risk Behavior Scale (LeBlanc & Tremblay, 1988). Prior to responding to items about their own riskbehaviors, adolescents provided self-reports of these same behaviors among their close friends. Based on adolescents' self-reported answers to questions from the National Longitudinal Survey of Youth (Baker & Mott, 1989), dichotomous variables wereconstructed to measure whether they had ever been adjudicated as a Person in Need of Supervision (PINS) for behaviors such asrepeated truancy or destruction of their parents' property (16%); whether they had ever been convicted of a crime (15%); had everengaged in binge drinking (N 5 drinks in a row; 12%); or had ever used marijuana (32%). The presence of any school suspensionswere assessed through caregiver reports (49%). These outcomes were dichotomized and scored as 0 = no and 1 = yes because theyrepresented relatively infrequent events within this sample. In order to further assess problematic health behaviors, adolescentswere also asked about the number of cigarettes they smoked daily. For this variable, the highest reported frequency of cigarettessmoked daily was 40. Because only one adolescent smoked 40 cigarettes, this value was truncated to 20, the next highest numberof cigarettes smoked daily, to reduce the likelihood that the results would be unduly affected by this value. Because these behaviorshave relatively low base rates, our findings on the relations between instability and risk should be interpreted cautiously.

2.1.4. Analysis planCorrelations and multiple regression analyses were employed to assess relations between family instability and adolescent

outcomes. A negative binomial model was used for analyses of cigarettes smoking given the variable's count distribution. In thoseinstances where data was missing, listwise deletion was used.

2.2. Study 1: Elmira results and discussion

Zero-order correlations between family instability and child adjustment are shown in Table 2. Instability was positivelycorrelated with caregivers' reports of externalizing behaviors, and internalizing behaviors, and with adolescents' reports ofexternalizing behaviors. A small positive association was detected between instability and adolescents' reports of internalizingbehaviors. Greater lifetime instability was associated with higher rates of cigarette consumption, as well as a greater likelihood ofschool suspension, PINS status, binge drinking, and marijuana use. Greater instability was significantly associated with lessfrustration tolerance, fewer social skills, and small decrements in task orientation as rated by teachers. Similarly, instability wasassociated with declines in English, math, and science grades. Family instability did not correlate significantly with the likelihoodof being convicted.

With the exception of criminal convictions, which had a low base rate (15%), we found consistent evidence of adverseassociations between instability and adolescent functioning. Two aspects of these findings are particularly noteworthy. First, themagnitude of the effects wasmoderate and tended to be greater for externalizing-type behaviors, including risky behaviors such assubstance abuse. Second, results for adolescent, parent, and teacher reports of adjustment were comparable. To rule out potentialalternative explanations for the observed associations between instability and adjustment, multiple regression analysis was

Table 3Effect of lifetime instability on adolescent outcomes in Study 1 (Elmira sample)

Outcome variable B SE B β Adj R2 ΔR2

Parent reportExternalizing 1.48 .35 .37⁎⁎⁎ .11 .13Internalizing 1.14 .24 .41⁎⁎⁎ .13 .16

Adolescent reportExternalizing .57 .30 .18+ .00 .03Internalizing .48 .28 .16+ .02 .02Cigarette use a .22 .11 – – –

Teacher reportFrustration tolerance −.78 .25 −.33⁎⁎ .08 .10Social skills −.61 .23 −.30⁎⁎ .03 .08Task orientation −.53 .26 −.22⁎ .03 .04

Academic grades (7–9)English −.07 .03 −.19+ .02 .03Math −.07 .03 −.19⁎ .06 .03Science −.09 .04 −.24⁎ .05 .06

Note. Negative life events, adolescent minority status, family income-to-needs ratios, and parent education were statistically controlled in all regression analyses.+pb .10. *pb .05. **pb .01. ***pb .001.

a Negative binomial models utilize maximum likelihood estimation unlike ordinary least squares models so there is no R2 statistic.


employed to examine the impact of family instability while statistically controlling for negative life events and keysociodemographic factors (adolescent minority status, household income-to-needs ratios, and maternal education).

These results indicate that greater family instability was associated with higher levels of caregiver-reported externalizing andinternalizing behaviors and number of cigarettes smoked daily (see Table 3). Two nonsignificant trends were detected betweeninstability and adolescent-reported externalizing and internalizing behaviors. Identical to the significant relations observedbetween instability and caregiver report of adolescent outcomes, teachers' ratings suggest that greater instability was linked tolower frustration tolerance, reductions in on-task behavior, and fewer social skills. Similarly, school records revealed that childrenwho experienced greater instability earned significantly lower academic grades in English, math, and science.

As shown in Table 4, regression results for the school suspension, criminal justice, and substance use outcomes indicate thatchildren exposed to greater instability were also at increased risk for school suspensions, PINS status, binge drinking, andmarijuana use. Instability was unrelated to the likelihood of being convicted, within the Elmira sample. Interactions were notdetected between instability and child sex or age.

Reports frommultiple informants supported the conclusion that instability had adverse consequences on several developmentaloutcomes. For example, net of statistical controls for negative life events, minority status, income, and maternal education,instability accounted for 11–13% of the variance in parent-reported externalizing and internalizing behaviors compared with 2–8%of the variance in teacher-reported adjustment and academic achievement. While the amount of unique variance explained wassmall, proportions explained were nearly identical to prior research on family instability with younger children (cf. Ackerman et al.,1999). The strength of associations was attenuated somewhat by statistical controls, particularly for youth report of externalizingand internalizing behaviors. Taken together, these findings reveal a pattern whereby family instability was consistently associatedwith problematic outcomes during adolescence.

3. Study 2: Southern Tier

3.1. Method

3.1.1. Time 1 participantsFor this study, 339 families from five, rural upstate New York counties were recruited to participate in parent–child home

interviews (see Evans and English, 2002). The sample was selected to be representative of both low- and middle-income groups.However, low-income families were oversampled because these data are part of a larger research program on rural poverty.Families were recruited from public school systems, New York State Cooperative Extension programs, and anti-poverty programs(e.g., Head Start). As in the Elmira study, only one child per household participated in the Southern Tier study. At Time 1, theaverage income-to-needs ratio for the sample was 1.66 (SD = 1.09) and just over half (56%) of the caregivers were married orcohabitating. Of the mothers with college experience, 33% of the sample had a two-year college degree or higher and 31% hadattended some college. On average the study children were 9 years old (SD = 1.15) at Time 1. Ninety-two percent of the studychildren were White, reflecting rural, upstate New York.

3.1.2. Time 2 participantsThree to four years later, interviews were conducted with 66% (n = 225) of the families that had been visited at Time 1. Analyses of

participant attrition indicated differences between followed and non-followed families for maternal education and income.Specifically, childrenwho participated at both Times 1 and 2 came fromhouseholdswith higher income-to-needs ratios, t(337) = 3.09,p b .01 (1.79 vs. 1.41), and had more highly educated mothers, t(330) = 2.17, p b .05 (2.30 vs. 1.96), compared with those childrenwhoparticipated only at Time 1. These differences indicate that selective attrition occurred such that families facing greater socioeconomicrisk factorswere less likely toparticipate at Time2.Unfortunately, itwasnotpossible todeterminewhether followedandnon-followedfamilies differed by partner changes or school transfers because these instability factors were assessed only at Time 2.

The Southern Tier study utilized 225 parent–adolescent dyads who participated in the Time 2 follow-up study. Of the parentsinterviewed at Time 2, 92% were mothers and the remaining caregivers consisted of fathers, grandparents, and one aunt. Themeanyearly income-to-needs ratio was 2.34 (SD = 1.44). Three percent of the primary caregivers had less than a high school education,23% graduated from high school or attained a GED, 49% completed some college, 13% completed 4-year college degrees, and theremaining 12% completed Master's degrees or higher. Sixty-eight percent of caregivers were married or cohabitating. On average,

Table 4Effect of lifetime instability on adolescent risk behaviors in Study 1 (Elmira sample)

Outcome variable Wald χ2 B SE Odds ratio

Suspension 3.87 .16+ .08 1.17Binge drink 5.18 .22⁎ .10 1.25Marijuana use 9.84 .27⁎⁎ .09 1.31PINS status a 4.52 .20⁎ .09 1.22Conviction .01 −.01 .10 0.99

Note. Negative life events, adolescent minority status, family income-to-needs ratios, and parent education were statistically controlled in all regression analyses.+pb .10. *pb .05. **pb .01.

a PINS = Person in Need of Supervision.


adolescents were 13 years old (M = 160.40 months, SD = 11.94). Fifty-two percent of the adolescents were male and a breakdownby ethnicity showed that 92% of the youth were White, 4% were African American, 3% were of other ethnic backgrounds and theremaining 1% was Asian.

3.1.3. ProcedureThe data were collected through home interviews at Times 1 and 2. Primary caregivers (usually mothers) and their children

were interviewed separately on each occasion. The primary caregiver's history of family instability was used in all instances.Caregivers completed work and family history calendars that were designed to help them recall work histories, residentialmobility, changes in family structure, and school transfers since the study child's birth. Following the home interview, Englishteachers were contacted by mail. Teachers were asked to complete brief behavioral evaluations and to provide letter grades inEnglish and math for the most recent marking period. Both families and teachers were paid for their participation.

3.1.4. Measures

3.1.4.1. Family instability. The family instability index for the Southern Tier study was based on the occurrence of four context-changing events since the study child's birth. As shown in Table 5, the index was defined by the number of (a) primary caregivers'partner changes (M = .95, SD = 1.34); (b) primary caregivers' work hour changes (M = 4.24, SD = 3.35); (c) residential moves (M =2.99, SD = 2.71); and (d) non-normative school transfers, which occurred over the course of children's academic careers sincekindergarten entry (M = .70, SD = 1.08). All events were based on caregivers' reports.

In contrast to the Elmira study, wewere able to isolate standard transitions from elementary tomiddle school or frommiddle tohigh school. These changeswere viewed as “normative” and thus excluded from the transfer index. For the partner change variable,a partner was defined as an individual who spent at least three nights per week in the study child's home. The work hour changevariable was constructed to capture changes of at least 5 h per week including job losses for all jobs that the primary caregiversheld. The four instability variables were converted to z-scores and then added to form a composite measure of family instability.

The occurrence of instability within these domains, although less common than in the more uniformly low-income Elmirasample, was still ubiquitous in the Southern Tier sample. For example, 85% of the children experienced one or more residentialmoves, 48% experienced one or more caregiver partner changes, 91% experienced one or more changes in caregivers' work hours,and 40% experienced one or more unscheduled school transfers.

Confirmatory factor analyses (CFA) demonstrated that these events loaded on one factor (α = .60). Individual loadings forpartner changes, work hour changes, residential mobility, and school transfers were .63, .67, .77, and .62 respectively. Althoughfamily income-to-needs ratios at Time 2 loaded on the instability factor, its inclusion reduced the construct alpha from .60 to .24. Tofurther demonstrate that the instability construct was not a proxy for hardship or poverty, we conducted a CFA that included theinstability variables, household income-to-needs ratios, caregiver education, and negative life events that had occurred during thepast six months (i.e., family member injury or illness; hospitalization; and parent/sibling/close relative death). As in the Elmirasample, partner changes, work hour changes, residential mobility, and school transfers uniquely loaded together on factor one(family instability), losses and medical problems uniquely loaded on factor two (negative life events), and income and caregivereducation uniquely loaded on factor three (socioeconomic status). Significant correlations were detected between the aggregateinstability construct and (a) family illness or injury, (b) a close relative's death, (c) income-to-needs ratios, and (d) parents'education rs(208–225) = .21, .15, −.40, and −.18, respectively; all ps b .05. Given that 30% of the sample experienced a family illnessor injury and 29% experienced a close relative's death, the modest correlations between instability and these events do not appearto be attributable to the low frequency of their occurrence.

3.1.4.2. Child, caregiver, and family covariates. Adolescent minority status, family income, caregiver education, and negative lifeevents at Time 2 were used as covariates in all regression models. Caregiver education was coded on a 6-point scale with highervalues reflecting more education (e.g., 0 = less than a 12th-grade education; 1 = high school diploma or GED receipt, 2 = some college,3 = two-year college degree, 4 = four-year college degree, 5 = greater than a Master's degree). Following the coding protocol used in theElmira study, household income from all sources was converted into income-to-needs ratios by dividing current total familyincome by a federal poverty line, specific to the adult–child household composition for a family's interview year (e.g., 1997–2002).A dichotomous minority status variable was constructed such that 0 = White and 1 = not White. Caregiver reports of stressful lifeevents that occurred during the past six months (i.e., family member injury or illness; hospitalization; and parent/sibling/closerelative death) were aggregated to form a negative life events index (M = 1.12, SD = 1.02, range = 0–4).

Table 5Means, standard deviations, ranges, and sample sizes for the variables that comprise the instability construct in Study 2 (Southern Tier sample)

Variable M SD Range N

Parent's partner changes .95 1.34 0–8 225Parent's work hour changes 4.24 3.35 0–14 225Residence changes 2.99 2.71 0–17 225Non-normative school transfers .70 1.08 0–5 225


3.1.4.3. Externalizing and internalizing behaviors. At Time 2, adolescents' ratings on the YSR (Achenbach, 1991) were used to assessexternalizing and internalizing behavior problems during the past six months. Sample alphas for the externalizing andinternalizing scales were .89 for each scale. Raw scores were utilized in our analyses.

3.1.4.4. Classroom behaviors. English teachers completed ratings on adolescents' disruptive and socially insecure behaviors usingthe Teacher Checklist of Social Behavior (Coie, 1990) at Time 2. Teachers rated each checklist item on a five-point Likert-type scale(1 = never true; 5 = almost always true) with high scores reflecting greater maladjustment. The disruptive (e.g., “this child makes alot of comments that are not related to what the group is doing”) and social insecurity (e.g., “this child is anxious and insecure insocial situations”) subscales each consisted of 8 items (possible score range: 8–40) and had corresponding alphas of .92 and .86. Forthe disruptive and socially insecure behavioral subscales, complete data were available for 76% and 75% of the youth, respectively.There were no differences in response rates by school transfers, lifetime residential mobility, or the aggregate family instabilityvariable. However, teacher response rates appeared to be influenced by adolescents' mobility during the past year. That is, teacherswere less likely to complete socially insecure subscale ratings for those youth who changed residences during the past year, χ2(1, N= 225) = 4.16, p b .05 and were somewhat less likely, but not significantly less likely, to complete disruptive subscale ratings forthose youth who changed residences during the past year, χ2(1, N = 225) = 3.16, p b .10.

3.1.4.5. Academic achievement. Letter grades provided by English teacherswere used to assess adolescents' academic achievement atTime 2. Grades were coded on a 5-point (0–4) scale with higher values reflecting higher grades. For the English and math grades,complete datawere available for 61% and24% of the sample, respectively. The low response rate formath gradesmaybe a function ofthe response burden placed on English teachers to obtainmath grades from students' math teachers. This “bottleneck”may explainwhymany English teachers returned questionnaireswithoutmath grades. Adolescent school transfers, lifetime residentialmobility,and family instabilitywere not related to teachers' response rates for academic grades. However, English teacherswere less likely toreport grades for those youthwho changed residences during the past year,χ2(1,N = 225) = 11.50, p b .01. English teacherswere alsoless likely to obtain math grades for those students who experienced school transfers χ2(5, N = 225) = 20.64, p b .01.

3.1.5. Analysis planFollowing the analysis plan used in Study 1, correlations and multiple regression analyses were employed to assess relations

between family instability and youth adjustment. Listwise deletion was used to handle missing data.

3.2. Study 2: Southern Tier results and discussion

Zero-order correlations between family instability and child adjustment are shown in Table 6. Instability was associated withelevated rates of adolescent-reported externalizing behaviors and internalizing behaviors. Instability was associated withincreases in disruptive classroom behaviors and marginal increases in socially insecure behaviors. Likewise, children whoexperienced greater instability received lower English grades and lower math grades.

As displayed in Table 7, results from regression analyses that controlled for negative life events, adolescents' minority status,household income-to-needs ratios, and caregiver education, showed that lifetime instability was linked to greater externalizingand internalizing behavior problems as well as increases in teacher-reported classroom disruptions. Higher levels of instabilitywere also associated with decrements in students' English and math performance. Instability independently explained 15% of the

Table 6Means, standard deviations, sample sizes, and correlations among the study variables in Study 2 (Southern Tier sample)

Variable 1 2 3 4 5 6 7 8 9 10 11

1. InstabilityAdolescent report2. Externalizing .36⁎⁎3. Internalizing .22⁎⁎ .69⁎⁎

Teacher report4. Disruption .22⁎⁎ .34⁎⁎ .18⁎5. Social insecurity .13+ .13+ .27⁎⁎ .36⁎⁎

Academic grades6. English −.30⁎⁎ −.37⁎⁎ −.19⁎ −.34⁎⁎ −.32⁎⁎7. Math −.29⁎ −.47⁎⁎ −.30⁎ −.50⁎⁎ −.24+ .78⁎⁎

Demographics8. Adolescent not White −.03 −.03 −.02 −.09 −.14+ .03 −.019. Income-to-needs −.40⁎⁎ −.26⁎⁎ −.19⁎ −.24⁎⁎ −.25⁎⁎ .40⁎⁎ .32⁎ −.15⁎10. Parent education −.18⁎ −.18⁎⁎ −.21⁎⁎ −.09 .09 .33⁎⁎ .21 −.16⁎ .50⁎⁎11. Negative life events .18⁎ .17⁎ .13+ .16⁎ .14+ −.28⁎⁎ −.09 −.02 −.18⁎ −.19⁎

Mean .00 12.41 14.04 14.67 18.02 2.58 2.72 .08 2.34 2.56 1.12Standard deviation 2.70 8.12 8.52 6.21 5.76 1.25 1.28 .26 1.43 1.38 1.02N 225 225 225 170 168 138 55 225 225 225 209

+pb .10. *pb .05. **pb .01.

Table 7Effect of lifetime instability on adolescent outcomes in Study 2 (Southern Tier sample)

Outcome variable B SE B β Adj R2 ΔR2

Adolescent reportExternalizing .93 .21 .31⁎⁎⁎ .15 .08Internalizing .55 .23 .17⁎ .07 .02

Teacher reportDisruption .38 .19 .17⁎ .10 .02Social insecurity .05 .17 .03 .08 .00

Academic gradesEnglish −.08 .04 −.17⁎ .22 .02Math −.16 .07 −.38⁎ .13 .10

Note. Negative life events, adolescent minority status, family income-to-needs ratios, and parent education were statistically controlled in all regression analyses.+pb .10. *pb .05. **pb .01. ***pb .001.


variance for externalizing behavior problems, 7% for internalization symptoms, 10% for classroom disruptions, 22% for Englishgrades, and 13% of the variance for math grades.

Interactions between instability and child sex were not detected. While a consistent pattern of interactions between instabilityand child age was not detected, results revealed one interaction between instability and child age for teacher-reported classroomdisruptions. Specifically, instability was associated with higher levels of disruptive behavior for younger children and unrelated todisruptive behavior for older children.

4. General discussion

Across the two datasets with conceptually comparable measures, family instability was associated with decrements insocioemotional outcomes and academic achievement during adolescence. Our results build upon and extend prior research oninstability and youth development. Consistent with other findings on correlates of instability, our work indicates that youthexperiencing greater instability performed worse scholastically (Moore et al., 2000; Simmons et al., 1987) and engaged in moreproblematic behaviors (Forman & Davies, 2003; Herrenkohl et al., 2003; Milan et al., 2006). Similarly, sex-by-instabilityinteractions on youth outcomes were not detected; this finding is consistent with the work of Forman and Davies (2003) andmostof the outcomes examined by Simmons et al. (1987). Finally, our findings show that instability is an empirically distinct conceptfrom stressful life events and poverty.

Among the strengths shared by both studies were multi-informant assessments of adolescent adjustment among infrequentlystudied populations — early and middle adolescents from heterogeneous SES backgrounds residing in predominantly ruralAmerica. These results also incorporated statistical controls for child minority status, maternal education, household income, andnegative life events.

A major contribution of the present studies is conceptual. We have refined the instability construct by focusing on changes inproximal contexts that remove youth from regular and sustained proximal processes. Irrespective of whether these changes areassociatedwith positive or negative events, instability reflects an unpredictable family environment for children. Conversely, stabilitycan provide a backdrop for the development and maintenance of consistency, predictability and structure (vis-à-vis routines) withinhouseholds, the latter being predictive of multiple forms of adjustment (Fiese et al., 2002). Moreover, competency — the ability tointeract effectivelywith elements in one's surroundings—necessitates early experiences of predictability, continuity, and coherence inorder to learn how one's actions can effect change in one's surroundings (Bronfenbrenner & Evans, 2000; White, 1959). Acrossdevelopmental periods instability may interfere with the attainment of stage-salient tasks that are central to child and adolescentdevelopment.

Our measure improved on its predecessors also by excluding a range of negative life events from the instability construct. Priordevelopmental studies (Ackerman et al., 1999, 2003; Forman & Davies, 2003; Moore et al., 2000; Simmons et al., 1987) haveincorporated life events and immediate family illnesses or deaths into their instability indices. This is problematic for severalreasons. First, as shown empirically with respect to both samples, life events do not load on the same factor as our measures ofchange in the home and school environment. In the present study samples, then, standard life event measures and instability arenot empirically equivalent. Second, making childhood illness, a health measure, part of an instability construct may cause theconstruct to overlap with mental health outcomes and other measures of adjustment. Third, residential moves, parental partnertransitions, modulations in maternal work hours, and school transfers are not necessarily negative events. Indeed, some forms ofinstability likely reflect positive events (e.g., job promotions, better work hour arrangements, parental partners, schools, orneighborhoods). Yet, as our findings indicate, changes in these domains, particularly when they accumulate, are linked to adverseoutcomes in youth. Moreover, this occurs above and beyond experiences of negative life events. Developing organisms require acertain degree of consistency and predictability in their immediate surroundings and disruptions, even when positive, can stillexact a price.

An important advantage of operationalizing instability as a confluence of risk factors is that it better captures the naturalecology of disruptions and interruptions in children's lives. Many of the individual components of instability covary. As a construct,instability has not been operationalized adequately in past research. At one extreme, as we have described above, qualitatively


distinct types of risks and stressors have been included in the construct. At the other extreme, single risk factors such as residentialmobility (Wood, Halfon, Scarlata, Newacheck & Nessim, 1993), partner instability (Ackerman, Brown, Schoff D'Eramo, & Izard,2002; Capaldi & Patterson, 1991; Mednick, Baker, & Carothers, 1990; Sandefur & Wells, 1999), and school transfers (Fenzel, 1989;Seidman, Allen, Aber, &Mitchell, 1994) have been studied in isolation. Research conducted on single risk factors removes processesfrom their natural context and thusmay inadvertently lead to the underestimation of their effects. By operationalizing instability asa confluence of risk factors, wewere able to capture disruptions and interruptions in children's lives as well as the covariance of theindividual components of instability. For example, when circumstances that compromise caregiving are studied in isolation fromone another they have limited predictive value, but in combination these events may be highly predictive of child behavior (e.g.,Burchinal, Roberts, Hooper, & Zeisel, 2000). As a cumulative risk index, the family instability construct provides the basis of aholistic approach for studying cumulative disruptive events that are likely to co-occur (e.g., Sameroff, Seifer, Baldwin, & Baldwin,1993).

One limitation shared by both studies is their correlational design. Becausewewere unable to control for baselinemeasures of childadjustment, it is possible that children's adjustment difficultiesmay have “caused” someof the family instability thatwe observed.Wecannot,moreover, rule out the possibility that unmeasured processes are driving the relationship between instability and adjustment.Fortunately these confounds, if they exist, appear to operate independently of income, minority status, and maternal education. It isalso important to acknowledge that our data do not permit us to determine the circumstances under which these changes wereoccurring. Simply put, we don't know if family instability was associated with positive or negative events.

Another limitation is that our data do not allow us to examine key timing issues related to instability exposure. Recent researchby Milan et al. (2006) indicates that earlier experiences of instability during kindergarten may be more predictive of externalizingbehaviors than experiences that occur during the early elementary school years. Extrapolating from research on the timing ofpoverty, for example, instability during early childhood may be far more predictive of current youth adjustment, particularlyacademic and school completion, than more recent exposure to it (e.g., Duncan, Yeung, Brooks-Gunn, & Smith, 1998). Our findingthat instability was associated with higher levels of teacher-reported classroom disruptions for younger but not older children inthe Southern Tier study further supports this hypothesis.

Further limitations of our studies include the accuracy of the retrospective instability reports. In both studies, interviewersrecorded caregiver responses about instability on life event history calendars in six-month increments. While this approach tocollecting life course data is influenced by the challenges associated with retrospective self-report data, use of these visuallystructured instruments is widely adopted (Caspi et al., 1996) and has been shown to yield reasonable agreement (greater than 85%)between concurrent reports of weekly work hours and schooling and reports obtained five years earlier (Freedman, Thornton,Camburn, Alwin, & Young-DeMarco, 1988).

In addition, some degree of measurement error may have influenced the findings. Because the present study drew upon datafrom two pre-existing studies, there are small discrepancies in how the partner and school change variables were defined. Withrespect to the partner change variable (four nights per week vs. three nights per week), we believe a difference of one night wouldnot alter our findings. With respect to the school transfer variable, (all transfers vs. non-normative school transfers), it is possiblethat greater variability in school transfers among the Southern Tier youth may have yielded a stronger association between familyinstability and the outcome measures.

Another limitation specific to our samples is selective attrition, which resulted in a more advantaged Southern Tier sample atTime 2. In the Southern Tier sample, our findingswere further limited by problems associatedwith the collection ofmath grades. Inparticular, the potential burden placed on English teachers to obtain both English andmath gradesmay have contributed to the lowresponse rate for math grades. Because some of the highest risk children were lost, our findings may underestimate the effects ofinstability for the Southern Tier youth. Finally, findings from the Southern Tier sample are limited by the non-random nature of theacademic grade data. Given that teachers were less likely to provide English grades for childrenwho changed residences in the pastyear and less likely to provide math grades for those childrenwho experienced more school transfers, it appears that the relationsbetween instability and academic grades were possibly underestimated in this sample.

Finally, the internal consistency reliability for each of the instability constructs was not strong. However, across the two studies,the alphas were comparable at approximately .60. Considering that the constructs are comprised of four variables, we expectedinternal consistency coefficients in this range.

Several future directions are suggested by the findings of this research. With the refined instability construct introduced inthese studies, researchers are better positioned to investigate factors that may moderate instability's effects on children'sdevelopment. Broadly, a useful next step would include examining the environments of childrenwho experience instability but donot display problem behaviors. Examining whether variations in living arrangements such as the presence of extended relatives inthe home (Kowaleski-Jones & Dunifon, 2006) buffer some of instability's effects on parents and children would be particularlyfruitful.

As suggested by the interaction between instability and child age on teacher-reported disruptive behavior themoderating role ofchildren's developmental stage is also of interest. Because parents' roles in children's development change over timewith changesin the proximal and distal systems affecting development (Bronfenbrenner & Morris, 1998), the pathways by which instabilityaffects children's developmentmay vary over the course of childhood. For example, older youth,whohave greater access to cars andtelecommunication devices,mayadjust to social losses associatedwith residentialmobility or school transfers differently than theiryounger peers, whose access to these resources is comparatively limited. Moreover, considering that the data for both the Elmiraand SouthernTier sampleswere collected in themid-1990s, social losses associatedwith residential or school transitionsmayhave asmaller effect on today's youth given greater opportunities to connect with friends through cellular phone calls.


A second stepmight include examining factors thatmediate relationsbetween instability and child adjustment. Forman andDavies(2003), for example, found that adolescent appraisals of family security (e.g., “I believe thatmy family will be around to helpme in thefuture”) mediated the link between instability and adolescents' externalizing and internalizing symptoms. Thus, if youth are not giventhe opportunity to assert themselves because of the unpredictability associated with exposure to instability, they may begin to feelinadequate or they may doubt or reject their caregivers' parenting practices, and in turn they may become overly dependent onexternal systems for self-regulatory control, a response associatedwith externalizing problems. Conversely, such unpredictabilitymayinculcate a more rigid, overcontrolled self-regulatory style as evidenced by internalizing problems. In addition to appraisals of familysecurity, parental monitoring practices and responsiveness are other mediating processes worthy of examination.

The accumulation of risk factors has been linked to negative outcomes for children. Reducing the risk of maladjustment inadolescence may be possible if we can better understand the nature of the risk factors. This paper represents a first step inidentifying contextual risk factors that may be particularly relevant for adolescent functioning. Social policy can more easily andmore directly address family instability factors than discrete negative life events, and therefore furthering our efforts to understandthese factors offers considerable promise for the long-term objective of improving the well-being of young people.

Acknowledgments

Cornell University supported this research in part via a Flora Rose Fellowship from the College of Human Ecology and a SummerFellowship from the Department of Human Development, both awarded to the first author. Data collection for Study 1 wassupported by grants from the Children's Bureau, U.S. Department of Health and Human Services (90-CA-1631), PreventionResearch Branch of the National Institute of Mental Health (R01-MH49381), Assistant Secretary for Planning and Evaluation, U.S.Department of Health and Human Services (96ASPE278A), National Institute of Mental Health Senior Research Scientist Award toDavid L. Olds (1-K05-MH01382-01), and the Smith-Richardson Foundation. We thank David Olds and John Shannon for theirgenerous support of the Nurse–Family Partnership; Alise Mahr and Darlene Batroney for tracing and interviewing the families; andKimberly Sidora-Arcoleo for unparalleled assistance with data management. Data collection for Study 2 was supported by grantsfrom the John D. and Catherine T. Mac Arthur Foundation Network on Socioeconomic Status and Health, the National Institute ofChild Health and Human Development (1 F33 HD08473-01), the Cornell University Agricultural Experiment Station (Project Nos.NYS 327404, 327407), and the W. T. Grant Foundation. We thank Jana Cooperman, Kimberly English, Missy Globerman, TinaMerrilees, Chanelle Richards, Adam Rokhsar, and Amy Schreier for assistance with data collection.

Portions of these datawere presented in symposia at the biennial meetings of the Society for Research in Child Development inMinneapolis, Minnesota (April 2001) and in Atlanta, Georgia, (April 2005), and in a poster presentation at the annual meetings ofthe American Psychological Association in Toronto, Canada (August 2003).

We gratefully acknowledge Karen Grace-Martin for statistical consultation. Most of all, the authors thank the parents, youth,principals, and teachers involved.

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Family instability during early and middle adolescence