Children and Youth Services Review 58 (2015) 227–235Conten.docx

Children and Youth Services Review 58 (2015) 227–235
Contents lists available at ScienceDirect
Children and Youth Services Review
journal homepage: www.elsevier.com/locate/childyouth
Functioning patterns among older adolescents in foster care:
Results
from a cluster analysis
Svetlana Shpiegel a,⁎, Kerrie Ocasio b
a Robert D. McCormick Center for Child Advocacy and Policy,
Montclair State University, 1 Normal Ave., Dickson Hall, Room
370, Montclair, NJ 07043, USA
b Institute for Families, School of Social Work, Rutgers
University, 55 Commercial Ave., New Brunswick, NJ 08901,
USA
⁎ Corresponding author.
E-mail addresses: [email protected] (S. Sh
[email protected] (K. Ocasio).
http://dx.doi.org/10.1016/j.childyouth.2015.09.024
0190-7409/© 2015 Elsevier Ltd. All rights reserved.
a b s t r a c t
a r t i c l e i n f o
Article history:
Received 9 June 2015
Received in revised form 28 September 2015
Accepted 28 September 2015

Keywords:
Foster care
Adolescents
Cluster analysis
National Youth in Transition Database
Aging-out
Older adolescents in foster care represent a heterogeneous
population, though such heterogeneity is often
underemphasized in research and practice. This study employed
a cluster analysis to identify subpopulations
in a large, national sample of 17-year-old youth based on the
following indicators: educational attainment, con-
nection to a supportive adult, adolescent parenthood,
homelessness, substance abuse referral and incarceration.
Data from the National Youth in Transition Database (NYTD)
and Adoption and Foster Care Analysis and
Reporting System (AFCARS) were used in the analysis. Results
revealed five subpopulations defined by specific
strengths, vulnerabilities and child welfare experiences. The
largest group identified (39%) functioned successful-
ly in all domains, whereas an additional group (15%) exhibited
consistent maladaptation. The remaining groups
evidenced variable adaptation patterns, with strengths in some
domains and challenges in others. Entry to foster
care for reasons other than child's problem behaviors, and
placement in stable, family-based settings were asso-
ciated with belonging to the most adaptive group. Findings
emphasize heterogeneity among older adolescents in
foster care, and call for better design and targeting of child
welfare services and programs as appropriate to the
needs of specific subgroups.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Every year, about 25,000 youth emancipate from foster care in

the
United States after reaching the age of maturity (U.S.
Department of
Health and Human Services, 2014). This period comes with the
expecta-
tion that the youth are able to negotiate adult responsibilities
and be-
come self-sufficient (Keller, Cusick, & Courtney, 2007).
Mastering such
tasks, however, may be challenging for adolescents who
abruptly tran-
sition out of foster care and into young adulthood (Lemon,
Hines, &
Merdinger, 2005). Unlike counterparts in the general
population, foster
youth must negotiate this transition suddenly and with limited
or no
support from family members (Collins, Spencer & Ward, 2010;
Keller
et al., 2007; Stott, 2013). In addition, many are underprepared
for as-
suming adult roles in terms of educational completion, job
readiness
and basic skills needed for independent living (Courtney, 2009;
Keller
et al., 2007; Stott, 2013).
Given such disadvantage, it is not surprising that foster youth
tend to struggle as they transition to independence (Courtney,
2009; Stott, 2013). Nearly 50% fail to obtain a high school
diploma
piegel),
by the age of 18, only 30% enroll in higher education
institutions
and less than 10% complete a four-year degree (Brandford &
English, 2004; Stott & Gustavsson, 2010; Yates & Grey, 2012).

Many
experience unemployment, underemployment and homelessness,
and receive need-based government assistance (Courtney, 2009;
Dworsky & Courtney, 2009; Hughes et al., 2008; Naccarato,
Brophy,
& Courtney, 2010; Stott & Gustavsson, 2010). In addition,
foster
youth exhibit higher rates of mental illness, substance abuse,
teen
pregnancy and criminal justice involvement compared to peers
in
the general population (e.g. Hughes et al., 2008; McMillen et
al.,
2005; Narendorf & McMillen, 2010; Svoboda, Shaw, Barth, &
Bright,
2012).
Nevertheless, not all youth exhibit dysfunctional outcomes
during this vulnerable time period. Some demonstrate relatively
uncompromised, or “resilient”, functioning as they leave the
child
welfare system and begin to live on their own (e.g. Daining &
DePanfilis, 2007; Hass & Graydon, 2009; Hines, Merdinger, &
Wyatt, 2005; Jones, 2012; Samuels & Pryce, 2008). Others
function
successfully in domains such as education and employment, but
struggle with mental health difficulties, low self-esteem and
com-
promised peer relationships (e.g. Keller et al., 2007; Yates &
Grey,
2012). Overall, available evidence suggests that different
subgroups
may exist within this population, calling for a “nuanced”
approach
to research, policy and practice (Courtney, Hook, & Lee, 2012).

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228 S. Shpiegel, K. Ocasio / Children and Youth Services
Review 58 (2015) 227–235
1.1. A person-oriented approach to understanding the
functioning of foster
youth
Existing studies on the functioning of foster youth typically
used a
variable-oriented approach to analysis, which examines how
certain
predictor variables relate to outcomes in specific domains
(Keller
et al., 2007; Yates & Grey, 2012). While this approach is useful
for iden-
tifying the correlates of individual outcomes, it fails to capture
the mul-
tidimensional nature of youths' adaptation (Keller et al., 2007).
As noted
by Courtney et al. (2012), “an alternative to the variable-
oriented ap-
proach…is the person-oriented approach, which assumes that
develop-
ment cannot be understood by examining single factors in
isolation
from their relationships with other interacting factors” (p. 410).
Person-oriented methods aim to detect meaningful subgroups in
a

given population who share similar characteristics and
experiences in
multiple domains. Identifying subgroups of foster youth
characterized
by specific strengths and vulnerabilities offers important
implications
for practice, including better design and targeting of child
welfare
services and programs (Courtney et al., 2012).
Several existing studies employed person-oriented methods to
ex-
amine the functioning of older youth in foster care. In a study
by
Keller et al. (2007), data from 17 and 18-year-olds residing in
three
Midwestern states were used to identify four subpopulations.
The larg-
est group identified, “distressed and disconnected”, represented
about
43% of the sample and included youth with high rates of
behavioral
problems and non-optimal employment and education outcomes.
The
second largest group, “competent and connected”, represented
about
38% of the sample and included youth with positive education
and em-
ployment experiences and no significant problem behaviors. The
last
two groups, “struggling but staying” and “hindered and
homebound”,
presented variable adaptation patterns, with strengths in some
domains
and challenges in others. The authors concluded that identifying
sub-

groups who share similar characteristics holds promise for
improving
service delivery to this population.
In another study, Yates and Grey (2012) identified four profiles
of functioning among emancipated foster youth in California
between the ages of 17 and 21. The outcome domains included
educational and vocational competence, civic engagement,
inter-
personal relationships, self-esteem and mental health. The
largest
group identified (47%) presented a “resilient” profile, fairing
rea-
sonably well in all domains. Two other groups exhibited
“discor-
dant” patterns of adjustment, where some youth demonstrated
psychological health despite functional difficulties (“internally
resilient”, 30%), while others presented emotional problems
despite apparent functional competence (“externally resilient”,
6.5%). An additional group exhibited a “maladaptive” profile
(16.5%) characterized by problematic functioning in all
domains.
Several other studies also employed person-oriented methods to
ex-
plore the outcomes of current and former foster youth (e.g.
Courtney
et al., 2012; Yampolskya, Sharrock, Armstrong, Strozier, &
Swanke,
2014). Most identified unique subpopulations requiring
different levels
of support and supervision on the part of child welfare officials.
Taken
together, existing evidence suggests that foster youth are a
heteroge-
neous population, pointing to the need for developing

intervention
strategies tailored specifically to different subgroups. Utilizing
a
“nuanced” approach to service delivery may enhance youths'
motiva-
tion and engagement in services and facilitate long-term
competence
(Courtney et al., 2012).
1.2. Factors influencing group membership
Although person-oriented studies consistently identified mean-
ingful subgroups among older adolescents in foster care, they
failed
to identify a reliable set of factors differentiating between well-
functioning youth and their more challenged peers. For
instance,
Keller et al. (2007) noted that members of the least adaptive
group
in their sample reported increased child maltreatment, residence
in
non-family care arrangements and considerable placement
instabil-
ity. In contrast, Yates and Grey (2012) failed to detect similar
differ-
ences, noting that their subgroups were comparable with respect
to
child welfare experiences. The contribution of demographic
factors
was similarly inconsistent, with Keller et al. (2007) pointing to
fe-
male gender and African–American race as related to more
adaptive
profiles, but Yates and Grey (2012) finding no significant
differences.
Research is needed to understand how various subgroups differ

from
one another so that appropriate services can be provided to
mitigate
risks and facilitate competent functioning. Demographic factors
and
reasons for out-of-home placement need further investigation,
as
these represent pre-existing risks which may relate to variations
in youths' functioning (e.g. Lee, Courtney, & Tajima, 2014;
Stott,
2013). The contribution of post-removal factors – especially
place-
ment type and stability – should also be explored, as these are
more amenable to intervention and can increase or mitigate
existing
risks (Newton, Litrownik, & Landsverk, 2000; Ryan & Testa,
2005;
Rubin, O'Reilly, Luan, & Localio, 2007; Stott, 2013). Prior
research re-
vealed that residence in stable, family-based settings is
associated
with better functioning, regardless of youths' pre-exiting
conditions
(e.g. Barber & Delfabbro, 2003; Newton et al., 2000; Rubin et
al.,
2007).
1.3. The present study
The present study employed cluster analysis as a person-
oriented method to identify distinctive profiles of functioning in
a
large, national sample of 17-year-old foster youth. Prior studies
were based on relatively small samples confined to one or few
states; therefore, utilizing a national sample is an important
next

step for the field. By limiting our study to 17-year-olds, we
aimed
to assess youths' functioning while they still had several years
be-
fore formal emancipation. As most states currently allow youth
to
remain in foster care until the age of 21, examining their
function-
ing at 17 provides a period of time for intervention to remediate
risk and facilitate competent functioning.
This study included six outcome indicators relevant to youths'
prospects for a successful transition to adulthood: educational
at-
tainment, connection with a supportive adult, teen parenthood,
and a history of homelessness, substance abuse referral and
incar-
ceration. The domains selected considered the developmental
tasks most relevant to this age group. Indicators related to
employ-
ment, independent living and economic self-sufficiency were
ex-
cluded, as they did not apply to the majority of 17-year-olds
still
under the care and supervision of child welfare agencies. In
con-
trast, educational attainment, connections to adults and
avoidance
of risky behaviors were deemed developmentally appropriate as
markers of successful adaptation at this age. Following the
identifi-
cation of the clusters, we compared the obtained subgroups on
var-
ious child welfare factors which may relate to variations in
youths'
functioning.

The specific goals of the present study were to:
(1) Identify unique profiles of functioning in a large, national
sample
of 17-year-olds based on the outcome domains described above
(i.e. education, connection to adult, childbirth, homelessness,
substance abuse referral and incarceration).
(2) Examine whether the obtained clusters relate in meaningful
ways to youths' pre-removal factors, including gender, race/eth-
nicity, number of removal episodes, age at the most recent
removal and reasons for out-of-home placement.
(3) Examine whether the obtained clusters relate in meaningful
ways to youths' post-removal factors, including length of
the current foster care episode and placement type and
stability.
229S. Shpiegel, K. Ocasio / Children and Youth Services
Review 58 (2015) 227–235
2. Methods
2.1. Dataset and procedure
This research is based on a secondary analysis of data from the
National Youth in Transition Database (NYTD). Created by the
John H. Chafee Foster Care Independence Program (CFCIP),
NYTD
is designed to (1) track various services provided through
CFCIP;
and (2) collect certain outcome measures to assess the effective-
ness of the program. All 50 states, the District of Columbia and
Puerto Rico are required to submit information to NYTD during

the designated reporting periods (NDACAN, 2014).
The present investigation focused solely on the outcome
component
of NYTD, which included information on all youth who were in
foster
care at age 17, examining their educational, vocational, and
general
well-being indicators during the period of transition to
adulthood.
States are required to collect three phases of outcome data for
every co-
hort of youth – a baseline survey during the year in which they
turn 17,
and two follow-up surveys when they turn 19 and 21.
The present study analyzed baseline data from the first cohort
of
youth established in federal fiscal year (FY) 2011 (N = 15,601).
All
youth who reached their 17th birthday in FY2011, and were in
foster
care within a 45-day period beginning on their birthday, were
eligible
to complete the outcome survey. States could choose to
administer
the survey in person, via the Internet or over the phone,
provided that
it was administered to the youth directly. Youth participation
was vol-
untary, with freedom to refuse without adverse consequences, or
to de-
cline to answer specific survey questions. Those youth who at
least
partially completed the survey during the designated 45-day
window

were included in FY2011 cohort. The national response rate for
the sur-
vey was 53%, ranging from 12% in Arizona to 100% in Rhode
Island and
Vermont. A weighting procedure was implemented to correct
potential
non-response bias. For detailed information about the NYTD
weighting
procedures, see NDACAN (2014).
To obtain information about youths' demographics and child
welfare
histories, NYTD data were combined with data from the
Adoption and
Foster Care Analysis and Reporting System (AFCARS) for
FY2011.
AFCARS is a federally mandated data collection system that
provides
case level information on all children for whom the state child
welfare
agencies have responsibility for placement and supervision, as
well as
on children who are adopted under the auspices of the state's
public
child welfare agency. Data includes demographic information
on chil-
dren and caregivers, and episode-level information, such as
removal
reasons, placement types, and number of previous placements.
All
states are required to submit information to AFCARS on a semi-
annual
basis (NDACAN, 2013a).
2.2. Sample

The final sample for the present study consisted of all youth in
the
NYTD FY2011 cohort for whom valid information on child
welfare
variables was available in AFCARS. Youth from all states were
represent-
ed in the final sample, with the exception of Connecticut, which
was
excluded due to incompatibilities in the format of the child's
unique
identifier, preventing the merge of NYTD and AFCARS
datasets. In addi-
tion, youth with missing information on any of the six
indicators used to
form the clusters were excluded, as the clustering method
employed
did not permit missing data. The final sample consisted of
14,402
youth (92% of the NYTD FY2011 cohort) – 6732 males and
6600 females.
The majority of the youth were White (N = 7421), followed by
African
Americans (N = 4141), American–Indians or Alaska Natives (N
= 260),
Asian (N = 118), Native Hawaiian or Other Pacific Islander (N
= 28)
and multiracial (N = 1700). In addition, 2230 youth,
irrespective of
race, identified as Hispanic or Latino.1
1 Unweighted count is presented here.
2.3. Measures
Three sets of variables were included in the analysis: (1)
outcome in-
dicators used to form clusters of functioning; (2) pre-removal
factors;

and (3) post-removal factors. Information about outcome
indicators
has been obtained from the NYTD dataset; information about
pre-
removal and post-removal factors has been obtained from
AFACRS. As
with other large administrative datasets, missing data were
present
for several variables, resulting in a modest decrease in sample
size for
some analyses.
2.3.1. Outcome indicators
Six outcome indicators were used to identify patterns of
functioning
among the participating youth. Each indicator was coded as (0)
absent;
or (1) present.
2.3.1.1. Current school enrollment. Current school enrollment
was de-
fined as attending high school, GED classes, post-secondary
vocational
training, or college at the time of the interview.
2.3.1.2. Connection to adult. Participants were asked if they
knew at least
one adult who they can go to for advice or guidance when there
is a
decision to make or a problem to solve, or for companionship
when
celebrating personal achievements. This could include, but was
not lim-
ited to, adult relatives, parents and foster parents; however, it
excluded

spouses, partners, boyfriends or girlfriends and current
caseworkers.
2.3.1.3. Childbirth. To determine childbirth status, participants
were
asked if they had ever given birth or fathered a child that was
born.
2.3.1.4. Homelessness. Homelessness was assessed by asking if
the youth
ever had no regular or adequate place to live, such as living in a
car, on
the street, or staying in a homeless or other temporary shelter.
2.3.1.5. Substance abuse referral. Youth were asked if they had
ever been
referred for an alcohol or drug abuse assessment or counseling,
includ-
ing either a self-referral or a referral by a social worker, school
staff, phy-
sician, mental health worker, foster parent or another adult.
2.3.1.6. Incarceration. Participants were asked if they had ever
been
confined in a jail, prison, a correctional facility, or juvenile or
community
detention facility in connection with allegedly committing a
crime
(a felony or a misdemeanor).
2.3.2. Pre-removal factors
These factors included youths' demographics and reasons for
out-
of-home placement. The demographic variables were gender,
race

and ethnicity, as well as number of removal episodes and
youths'
age at most recent removal. Youth reported their gender as
either
male or female. Ethnic identity was defined as Hispanic or non-
Hispanic, and youth reported their race as White, Black/African
American, American–Indian/Alaska Native, Asian, and Native
Hawai-
ian/Other Pacific Islander. An additional category
(“multiracial”) was
created to represent youth reporting two or more racial
categories.
Furthermore, all minority race categories (including
“multiracial”)
were combined into one category labeled “non-White” for use in
some analyses. Youths' age at most recent removal was
measured
in years, and total number of removal episodes was measured
continuously.
The AFCARS dataset includes fifteen reasons for out-of-home
place-
ment: physical abuse, sexual abuse, neglect, parental alcohol
abuse,
parental drug abuse, child's alcohol abuse, child's drug abuse,
child's
disability, child's behavioral problems, parental death, parental
incar-
ceration, inability to cope, abandonment, relinquishment and
inade-
quate housing. Each reason was coded as (0) absent, or (1)
present,

Review 58 (2015) 227–235
and more than one could be recorded for each youth. For a
detailed
description of each removal category, see NDACAN (2013b).
2.3.3. Post-removal factors
Post-removal factors included duration of the current foster care
ep-
isode, number of placement settings during this episode, current
place-
ment type and the duration of the current placement. Duration
of the
current foster care episode, as well as current placement, were
mea-
sured in days. Placement type was coded as: (1) relative foster
home,
(2) non-relative foster home, (3) group home or institution, and
(4) other setting (i.e. pre-adoptive home, supervised
independent
living, trial home visit or runaway). Number of placement
settings
during the current episode was measured continuously.
2.4. Analytic strategy
Data analysis was conducted in several steps. First, a two-step
cluster
analysis was performed to organize youths' outcomes (i.e.
education,
connection to adult, childbirth, homelessness, substance abuse
referral
and incarceration) into mutually exclusive groups. The two-step
cluster
method is preferred for very large datasets, and is appropriate
for cate-

gorical variables (Fava et al., 2012; Tsai, Edens, & Rosenheck,
2011). The
number of clusters was established by the two-step algorithm,
though
we imposed a maximum of seven clusters to optimize results'
interpret-
ability and utility in subsequent analyses. The log likelihood
distance
measure was used to determine cluster membership; Bayesian
informa-
tion criterion (BIC) as well as clusters' interpretability
considerations
were used to judge the adequacy of the final solution.
Following the identification of the clusters, descriptive
bivariate
analyses (i.e. one way ANOVA tests and chi-square tests) were
used to
examine the associations between pre-removal and post-removal
fac-
tors and youths' cluster membership. Weights were incorporated
in all
analyses to produce national estimates for the full NYTD
baseline popu-
lation (i.e. all 17-year-olds in foster care). To reduce the
likelihood of
committing a Type 1 error due to multiple comparisons, the
Bonferroni
correction was applied and p value of less than .01 was used.
Analyses
were conducted with SPSS 21 Complex Samples software.
3. Results
3.1. Cluster analysis

The two-step cluster analysis produced five clusters of
functioning.
The frequencies of the domains that formed the clusters are
presented
in Table 1. The largest of the obtained clusters (39%) was
labeled resil-
ience, and characterized by positive functioning in all domains.
Youths
in this cluster were enrolled in school, had a supportive adult,
and did
not have any of the risk behaviors studied (i.e. early
parenthood, home-
lessness, substance abuse referral, or incarceration). The second
cluster
(19%), labeled substance abuse, was characterized by substance
abuse
referrals for all members, as well as incarceration histories for
some.
The third cluster (15%), labeled multiple problems, was
characterized
Table 1
Functioning indicators by cluster (N = 14,402).
Variable Resilient
(N = 5778^)
Substance abuse
(N = 2604^)
In school 100% 100%
Supportive adult 100% 100%
Has children 0% 0%
Ever homeless 0% 0%
Substance abuse referral 0% 100%
Incarceration 0% 58.1%

Note: The number in the table represents the percentage of
cases with the targeted outcome o
^Unweighted count.
Homeless. = homelessness.
by compromised functioning in all domains. Nearly half of its
members
had children, one-third did not have a supportive adult, over
30% were
not enrolled in school, 20% had been homeless, 45% had been
incarcer-
ated and about 30% reported a substance abuse referral. The
fourth clus-
ter (14%), labeled incarceration only, included youth who had
been
incarcerated; however, all were enrolled in school, had a
supportive
adult and did not have any other risks. Lastly, the smallest
cluster
(13%), labeled homelessness, was characterized by
homelessness histo-
ries for all members, as well as substance abuse and
incarceration histo-
ries for some.
3.2. Pre-removal factors
3.2.1. Demographics
In the overall sample, 51% of youths were male, 53% were
White and
20% were Hispanic. Differences by cluster are summarized in
Table 2.
Significant gender differences emerged between the groups (χ2
=
485.51, p b .001), such that multiple problems and resilience
clusters

were characterized by increased number of females, whereas
incarcera-
tion only and substance abuse clusters were characterized by
increased
number of males. Significant differences by race (χ2 = 128.34,
p b .001) and ethnicity (χ2 = 32.13, p b .01) were also noted,
such
that substance abuse and homelessness clusters were
characterized by in-
creased number of Whites, whereas multiple problems cluster
was char-
acterized by increased number of non-Whites and Hispanics.
On average, participants were about 13.5 years-old during the
latest
removal episode, though differences by cluster were noted (F =
109.29,
p b .001). Youths in resilience cluster were somewhat younger
during
the latest removal (M = 12.73), while those in substance abuse
cluster
were older (M = 14.40). The average number of removal
episodes for
the overall sample was 1.49. Cluster differences were
significant (F =
5.26, p b .01), however, they were relatively small.
3.2.2. Reasons for removal
Cluster differences in removal reasons are summarized in Table
3.
The most commonly reported reason for removal in the overall
sample
was neglect (41%), followed by a child's behavioral problem
(38%), and
caretaker's inability to cope (23%). Cluster differences were

significant
for the majority of reasons, with the exception of parental
alcohol
abuse, imprisonment, relinquishment and death. Youths in
resilience
cluster presented higher rates of physical abuse, sexual abuse
and ne-
glect, but lower rates of most child-related factors, with the
exception
of disability. Youths in the substance abuse cluster presented an
opposite
pattern, with decreased rates of child abuse and neglect but
increased
rates of child-related factors. Youths in the homelessness
cluster were
noted for relatively high rates of inadequate housing, parental
substance
abuse and parental inability to cope, while those in the
incarceration
only cluster were noted for high rates of child disabilities and
problem
behaviors. Interestingly, youths in the multiple problems cluster
present-
ed average rates of both child-related and parent-related reasons
for
out-of-home placement.
Multiple problems
(N = 2178^)
Incarceration only
(N = 1957^)
Homeless.
(N = 1885^)
65.9% 100% 100%

68.4% 100% 100%
44.6% 0% 0%
21.7% 0% 100%
31.4% 0% 38.8%
45.0% 100% 43.7%
ut of all cases in that cluster.
Table 2
Demographic characteristics by cluster^.
Demographic Resilient Substance abuse Multiple problems
Incarceration only Homeless. X2 or F
Gender 485.5**
Male 44.4% 60.2% 41.7% 69.0% 50.6%
Female 55.6% 39.8% 58.3% 31.0% 49.4%
N^^=13,332
Race 128.3**
White 52.0% 57.7% 45.4% 49.5% 61.0%
Non-white 48.0% 42.3% 54.6% 50.5% 39.0%
N^^=13,668
Ethnicity 32.1*
Non-Hispanic 81.2% 80.8% 76.4% 82.1% 77.6%
Hispanic 18.8% 19.2% 23.6% 17.9% 22.4%
N^^=12,730
Age at last removal 109.29**
M 12.7 14.4 14.1 13.8 13.9

(SE) (.05) (.06) (.07) (.07) (.07)
N^^=13,331
Number of removals 5.26*
M 1.45 1.51 1.53 1.53 1.52
(SE) (.01) (.02) (.02) (.02) (.02)
N^^=13,325
Notes: ^Weighted analysis.
^^ Unweighted count.
*p b .01; **p b .001.
Review 58 (2015) 227–235
When examining all reasons for removal, more than two-thirds
(73%) of youths in the resilience cluster were removed
exclusively
due to parent-related difficulties (i.e. physical abuse, sexual
abuse,
neglect, parental alcohol or drug use, inability to cope,
inadequate
housing, imprisonment, abandonment, relinquishment and
death),
while just 11% were removed solely due to child-related factors
(i.e. child's alcohol or drug use, disability and behavioral
problem).
In contrast, in the substance abuse and incarceration only
clusters,
42% and 35% respectively were removed exclusively due to
child-
related factors, while only about 40% were removed solely due
to
parent-related difficulties. In the multiple problems and

homelessness
clusters, the majority of youths (59% and 63% respectively)
were re-
moved exclusively due to parent-related difficulties, though a
sizable
portion (about 18%) was removed due to a combination of
parent-
related and child-related factors.
Table 3
Removal reasons by cluster^ (N = 13,127).
Removal reason~ Resilient Substance abuse Multip
Physical abuse 17.5% 8.2% 11.8%
Sexual abuse 10.9% 5.2% 7.2%
Neglect 48.1% 29.6% 40.8%
Parent alcohol abuse 6.1% 4.8% 4.6%
Parent drug abuse 13.7% 10.2% 12.9%
Child alcohol abuse .8% 3.9% 2.1%
Child drug abuse 1.1% 9.6% 5.1%
Child disability 4.9% 3.2% 2.9%
Child behavior 23.7% 57.5% 38.1%
Parent death 1.5% 1.1% 1.1%
Parent incarceration 4.4% 2.9% 4.2%
Caregiver coping 24.0% 17.9% 20.8%
Abandonment 8.8% 6.6% 10.7%
Relinquishment 2.2% 1.7% 1.8%
Inadequate housing 7.9% 3.4% 7.5%
Primary reason(s)
Parent only 73.2% 38.7% 58.9%
Child only 11.7% 42.0% 23.2%
Parent and child 15.1% 19.3% 17.9%

~More than one removal reason can be listed for each child.
**p b .001.
3.3. Post-removal factors
At the next step, youths' post-removal factors were examined,
in-
cluding placement type, time spent in current placement,
number of
placements during the current spell in foster care, and duration
of the
current foster care episode. In the overall sample, 38% of
youths resided
in non-relative foster homes, 36% resided in group homes or
institu-
tions, 10% lived with relatives and 16% were placed in other
settings.
The average time spent in current placement was 367 days,
whereas av-
erage duration of the current foster care episode was 1214 days.
Place-
ment instability was high for all participants, with an average of
5.42
placements since the latest removal.
Cluster differences in placement characteristics are summarized
in
Table 4. Significant differences emerged for current placement
type
(χ2 = 1027.26, p b .001), such that members of the resilience
cluster
le problems Incarceration only Homeless X2
10.1% 11.9% 156.6**
7.3% 7.0% 87.5**

32.3% 44.4% 304.5**
4.5% 6.4% 15.1
10.1% 14.0% 32.4⁎⁎
.8% 3.4% 119.4**
2.3% 5.4% 332.1**
5.8% 3.6% 34.6**
57.1% 34.3% 1117.5**
1.0% 1.3% 4.7
4.0% 4.7% 11.8
22.2% 27.7% 67.6**
8.9% 10.8% 33.0**
1.3% 1.6% 7.7
5.1% 9.7% 86.3**
1304.2**
40.5% 69.9%
35.4% 18.7%
24.1% 18.4%
Table 4
Placement characteristics by cluster ^.
Variable Resilient Substance abuse Multiple problems
Incarceration only Homeless. X2 or F
Current placement 1027.2**
Relative home 15.0% 6.1% 8.5% 5.9% 10.1%
Non-relative home 48.3% 26.0% 33.4% 26.8% 40.0%
Residential 24.2% 50.0% 39.0% 51.9% 33.3%
Other 12.6% 17.8% 19.2% 15.4% 16.6%

N^^=13,257
Number of placements 16.5**
M 5.0 5.0 5.9 6.1 5.7
(SE) (.07) (.12) (.16) (.17) (.14)
N^^=13,321
Days in placement 109.4**
M 529.3 238.1 259.9 258.2 299.8
(SE) (12.1) (8.7) (10.4) (9.9) (10.1)
N^^=13,031
Days in care 109.7**
M 1525.7 902.4 996.4 1113.1 1092.5
(SE) (21.7) (24.2) (27.9) (29.7) (26.8)
N^^=13,331
^^ Unweighted count.
Residential = group homes and/or institutions.
**p b .001.
Review 58 (2015) 227–235
were likely to reside in relative or non-relative foster homes,
while
members of the substance abuse and incarceration only clusters
were
likely to reside in group homes and institutions. Youths in the
multiple
problems and homelessness clusters resided mostly in non-

relative foster
homes, as well as group homes and institutions.
Significant differences also emerged in the time spent in current
placement, as well as the number of placements since the latest
removal
(F = 109.47, p b 001 and F = 16.52, p b 001). Youths in
resilience cluster
spent more time in current placement (M = 529.39 days) and
had less
placement settings (M = 5.03) compared to members of the
other
groups. In contrast, youths in the substance abuse cluster spent
less
time in current placement (M = 238.16 days), and those in the
incarcer-
ation only cluster had more placement settings (M = 6.12)
compared to
others. Duration of the current foster care episode also differed
signifi-
cantly (F = 109.75, p b .001), with members of the resilience
cluster
spending more time in care (M = 1525.79 days), and members
of the
substance abuse cluster spending less time (M = 902.49 days).
The re-
maining clusters differed little in the duration and stability of
place-
ments, as well as duration of the current foster care episode.
4. Discussion
The goal of the present study was to identify patterns of
function-
ing among older adolescents in foster care, and examine the
factors

associated with such patterns. Findings revealed five
subpopulations
characterized by specific strengths, vulnerabilities and child
welfare
experiences. Each subpopulation may have different prospects
for a
successful transition to adulthood, requiring different levels of
sup-
port and supervision on the part of child welfare staff.
4.1. The “resilience” cluster
The largest cluster in the present sample (39%) was
characterized by
competent, or “resilient”, functioning in all domains.
Adolescents in this
cluster were enrolled in school, had supportive adults, and
avoided
problematic outcomes, such as homelessness, teen parenthood
and
incarceration. This pattern is consistent with several prior
studies indi-
cating that foster youth may function successfully as they
transition to
independence (e.g. Daining & DePanfilis, 2007; Jones, 2012;
Keller
et al., 2007; Yates & Grey, 2012). Person-oriented research
consistently
identified well-functioning subpopulations among these youth
(e.g.
Keller et al., 2007; Yates & Grey, 2012), and variable-oriented
studies
also corroborated such findings (e.g. Daining & DePanfilis,
2007; Jones,
2012). Overall, existing evidence suggests that resilient
functioning

may be quite common among young people leaving foster care,
supporting the view of resilience as developing “via the
operation of
normal developmental processes…rather than from exceptional
indi-
vidual capacities” (Yates & Grey, 2012, p. 476).
Members of the resilience cluster were more likely to be female,
though differences were relatively small. The observed pattern
is con-
sistent with prior studies reporting better outcomes for female
foster
youth (e.g. Daining & DePanfilis, 2007; Keller et al., 2007).
Nevertheless,
some of the indicators used in this analysis are inherently less
prevalent
among females (e.g. incarceration), which may bias the study
findings.
In fact, in studies that include internalizing, as well as
externalizing,
markers of successful adaptation, gender differences tend to be
less pro-
nounced (e.g. Yates & Grey, 2012).
Youths in the resilience cluster were younger during the latest
out-
of-home placement, and spent substantially more time in foster
care
compared to other youth. Their reasons for removal were also
some-
what different, with higher rates of caregiver-related factors
(e.g. child
maltreatment) and lower rates of child-related difficulties (e.g.
behav-
ioral problems). Many were placed in relative or non-relative
foster

homes and spent nearly twice as much time in current placement
com-
pared to members of the other groups.
Overall, findings suggest that young people who are removed in
ear-
lier stages of adolescence, for reasons other than severe
externalizing
problems, and who are placed in relatively stable, family-based
settings,
exhibit better functioning at age 17. Although these patterns are
strictly
correlational, prior research shows that children and adolescents
placed
in family-based settings fare better than those placed in
residential care
(e.g. Cusick, Courtney, Havlicek, & Hess, 2011). The stability
of place-
ments also promotes competent functioning (e.g. Stott, 2012),
and sta-
ble placements are easier to find for children without severe
emotional
and behavioral problems (e.g. Ryan & Testa, 2005; Stott, 2012).
4.2. The “substance abuse” cluster
The second largest cluster in the present sample (19%) was
charac-
terized by substance abuse referrals for all members, as well as
incarcer-
ation histories for some. Nevertheless, all youth were enrolled
in school,
had a supportive adult, and did not have histories of
homelessness or
adolescent parenthood. The emergence of this cluster is
supported by

prior research, reporting elevated rates of substance abuse
among
Review 58 (2015) 227–235
older adolescents in foster care (e.g. McDonald, Mariscal, Yan,
& Brook,
2014; Narendorf & McMillen, 2010). In addition, two person-
oriented
studies indicated that subgroups characterized by substance use
also
tended to include delinquent behaviors (Keller et al., 2007;
Yates &
Grey, 2012).
The substance abuse cluster was characterized by increased
number
of male and White youth, consistent with prior research
reporting sim-
ilar trends (e.g. Aarons et al., 2008; Vaughn, Ollie, McMillen,
Scott, &
Munson, 2007). Youths in this cluster were also older during
the latest
out-of-home placement and spent less time in foster care
compared to
members of the other groups. Many youth were placed in group
homes or institutions, possibly due to difficulties related to
substance
abuse behaviors. Furthermore, given that over 40% were placed
in out-
of-home care exclusively due to child-related difficulties, many
may
have entered foster care with unmet behavioral and/or emotional
needs, making it harder to find stable, family-based placements

(Stott,
2012).
Noteworthy, all youth in this cluster were enrolled in school and
had
a supportive adult – these are important strengths which can be
built
upon when designing intervention strategies. Timely
permanency
planning that incorporates supportive adults, as well as school-
based
programming to increase independent living preparedness, can
be par-
ticularly beneficent.
4.3. The “multiple problems” cluster
The multiple problems cluster was characterized by a
constellation of
difficulties considered “typical” for emancipating foster youth,
though it
represented merely 15% of the sample. About one-third of these
youth
were not enrolled in school, a similar proportion had no
supportive
adults, and many reported homelessness, substance abuse
referrals
and incarceration. Furthermore, nearly 50% had at least one
child.
These findings are consistent with both variable-oriented and
person-
oriented studies, reporting an array of difficulties presented by
some
foster youth (e.g. Courtney, 2009; Keller et al., 2007; Yates &
Grey,
2012). In the study by Keller et al. (2007), such youth

accounted for
over 40% of the sample, though in Yates and Grey's study
(2012), they
represented merely 17%. These differences may at least
partially relate
to variations in methodology, such as sample size, recruitment
strate-
gies, and the selection of specific outcomes included in the
analysis.
Members of the current cluster were somewhat more likely to be
fe-
male, non-White and Hispanic. Given their high rates of
childbirth and
the established link between minority status and teen
parenthood
(Leathers & Testa, 2006), such patterns are aligned with
existing litera-
ture. African–American and Hispanic foster youth, in particular,
were
found to have high rates of pregnancy and childbirth (e.g. King,
Putnam-Hornstein, Cederbaum, & Needell, 2014), possibly
driving the
increased representation of minorities in this cluster.
Furthermore, mi-
nority youths may have higher likelihood of placement in
residential
settings, which can impede engagement in age-appropriate tasks
(e.g.
attending school, developing supportive relationships), and
increase
the likelihood of problematic behaviors (e.g. criminal
involvement)
(Courtney, 2009; Cusick et al., 2011; Stott, 2012).
Youths were placed in out-of-home care for a variety of

reasons,
including both parent-related and child-related difficulties. At
the
time of data collection, nearly 40% were placed in group homes
or in-
stitutions, one-third resided in non-relative foster homes and
less
than 10% lived with relatives. The difficulties presented by
these
youth are noteworthy, especially as many of them may parent
minor children. It is unknown to what extent having children
con-
tributed to challenges presented by these youth, though prior re-
search identified teen parenthood as a risk factor for behavioral/
emotional difficulties, educational underachievement and
poverty
(Barnet, Liu, & DeVoe, 2008; Boden, Fergusson, & Horwood,
2008).
However, other research suggested that parenthood may be a
posi-
tive factor in the lives of foster youth, increasing their
motivation
for education and employment, and decreasing engagement in
vari-
ous problematic behaviors (e.g. Chase, Maxwell, Knight, &
Aggleton,
2006). Overall, some youths in the multiple problems cluster
may re-
quire extensive supports as they transition to independent adult-
hood, while others may need more targeted services designed to
support adequate parenting, as well as economic independence.
4.4. The “incarceration only” cluster
The fourth cluster, incarceration only, represented 14% of the
sample.

Youths in this cluster were enrolled in school, had a supportive
adult
and did not report childbirth, homelessness or substance abuse,
howev-
er, all had been incarcerated at some point in their lives.
Consistent with
existing research (e.g. Cusick, Havlicek, & Courtney, 2012),
nearly 70%
were male, with race and ethnicity resembling the overall
sample. Age
of removal also matched the overall sample; however, many
youth
were removed exclusively due to child-related difficulties.
Furthermore,
over 50% were placed in group homes or institutions and
placement in-
stability was relatively high. This may be the result of
movement in and
out of correctional facilities, or due to problem behaviors which
made it
difficult to find stable arrangements (Stott, 2012). During the
period of
transition to adulthood, this group is likely to require
interventions spe-
cifically targeting emotional and/or behavioral difficulties. The
absence
of other risk factors in this group (e.g. substance abuse,
childbirth),
and the presence of strengths, such as school enrollment and
supportive
relationships, indicate that the period between ages 17 and 21
may
present a unique opportunity to address existing challenges and
facili-
tate competent functioning.

4.5. The “homelessness” cluster
The homelessness cluster made up the smallest portion of the
sample,
at 13%. All members of this cluster had been homeless at some
point in
their lives, and some also had substance abuse and incarceration
histo-
ries. Nevertheless, all were enrolled in school, had a supportive
adult
and did not have children. These youths' gender resembled the
overall
sample, but they were likely to be White. In addition, they
tended to
be slightly older during the latest removal, with reasons for
removal
predominantly parent-related. Many were placed in non-relative
foster
homes and group homes/institutions, with average rates of
placement
disruption. Prior research identified homelessness as a
significant prob-
lem among emancipating foster youth (Dworsky & Courtney,
2009),
and demonstrated that it often coincided with other risks (e.g.
Courtney et al., 2012). While it is unknown if homelessness
preceded
or followed out-of-home placement in the current sample, some
mem-
bers of this cluster may require intensive supports as they
transition to
adulthood due to other difficulties (i.e. substance abuse and
criminal in-
volvement). As with the substance abuse group, services should
build
upon existing strengths, such as school enrollment and

relationships
with supportive adults.
5. Recommendations for policy and practice
The heterogeneity of foster youth in this sample and in similar
stud-
ies (e.g. Courtney et al., 2012; Keller et al., 2007; Yampolskya
et al.,
2014; Yates & Grey, 2012), suggests significantly different
service
needs. Youths in the resilience cluster seem to require less
tangible
supports, though they may still exhibit internalizing problems
not
accounted for in the present investigation. This is especially
likely
given that child maltreatment rates were higher in this cluster
com-
pared to any other group. Child welfare officials should
continue to
monitor these youths' condition, including periodic, age
appropriate
assessment of needs, informed by their abuse and neglect
histories.
During this time, caseworkers should reduce their reliance on
foster
parent reports of problems, instead seeking out opportunities to
interact
directly with the youth. Evaluating and addressing difficulties
such as
Review 58 (2015) 227–235

mental health problems, disturbed peer-relationships and low
self-
esteem is particularly important, as these could be easily
overlooked
due to apparent competence exhibited by these youth (Yates &
Grey,
2012).
In contrast, youths in the substance use, incarceration only and
home-
lessness clusters may require more intensive supports, with an
emphasis
on remediation of problem behaviors, such as substance abuse
and de-
linquency. Criminal involvement should receive special
attention, as it is
represented in all three clusters and its impact on subsequent
function-
ing may be particularly detrimental. Some studies suggest that
place-
ment in residential settings is a predictor of future delinquency
(e.g.
Cusick et al., 2011), therefore, youth should be placed in
family-based
care to the extent possible. For youth requiring residential care,
inter-
ventions to minimize externalizing problems may be beneficent.
Youths in the multiple problems cluster may have high risk of
prob-
lematic outcomes, though members are likely to vary widely in
the ex-
tent of difficulties presented. In general, these youth can benefit
from
educational supports, housing assistance and counseling
services to ad-

dress problem behaviors. Connecting youths with positive adult
figures
willing to serve as mentors is also important, as about one-third
report-
ed no existing connections. A potential avenue for increasing
connect-
edness may be assisting youth to reestablish relationships with
extended family members (e.g. grandparents, aunts and uncles,
sib-
lings), providing that they can serve as positive role models.
Meaningful
relationships can also be formed through participation in
educational
and vocational settings, as well as in extracurricular activities.
Although this study examined several child welfare factors
possibly
associated with the emergence of specific clusters, their
predictive value
is difficult to assess due to the cross-sectional nature of the
analysis.
Nevertheless, emerging trends suggest that youth who are
removed at
earlier age, for reasons other than severe behavioral problems,
and
who are placed in relatively stable, family-based settings fare
better as
they approach the age of emancipation. In contrast, those who
are
removed later, placed in residential settings and experience
placement
disruption may evidence more problematic functioning, such as
school
drop-out and early childbirth. This interpretation is consistent
with
existing literature indicating that stable, family-based settings

are opti-
mal for facilitating positive outcomes, while also noting that
such place-
ments may be difficult to achieve for older, behaviorally
disturbed youth
(Newton et al., 2000; Rubin et al., 2007; Ryan & Testa, 2005;
Stott,
2012).
There is an urgent need to address the availability of family-
based
placements for youth with emotional and behavioral difficulties.
In
some cases, residential treatment settings may be appropriate
for
these youth; however, there is also emerging research indicating
that
family-based placements for troubled children can be promoted
through specialized training – such as Multidimensional
Treatment Fos-
ter Care (Fisher, Burraston, & Pears, 2005) and Keeping Foster
and Kin-
ship Parents Trained and Supported (Price et al., 2008). Further
research
is also needed to determine why relative placements are
underutilized
among these youth. Kinship placements have been noted to be
as
much as 70% less likely to disrupt than non-kin placements
(Webster,
Barth, & Needell, 1999). Although relatives asked to provide
care may
have greater reluctance to take in a youth they know to have
emotional
and/or behavioral challenges, agencies might also be less likely
to place

these youth with relatives due to safety and well-being
concerns. Fur-
ther research is needed to understand these dynamics, and
examine
how placement decisions are made for youth with special needs.
Finally, the emergence of the substance abuse and incarceration
only
clusters may reflect an effort by the child protection system to
provide
services to youth for reasons other than protection from their
parents.
Both groups were more likely to be male, and be placed in
residential
settings. The substance abuse group spent less time in foster
care and
was older at the latest removal, while the incarcerated only
group exhib-
ited increased behavioral disturbance and disabilities. Most
important,
both groups had relatively high rates of child-related reasons for
removal and out-of-home placement. In combination with the
profile
characteristics described above, this may suggest that the child
protec-
tion system was providing substance abuse and mental health
treat-
ment to youth who might have been able to receive those
services
another way. It is also possible that in some cases, parents were
per-
ceived to be ineffectual or uncommitted in addressing these
youths`
challenges, and the state agency intervened on their behalf.
States
should consider developing children's behavioral health

systems, such
as those in place in New Jersey, where treatment services for
children
are available to the general public through a single access point
(NJ
Department of Children and Families, n.d.).
6. Limitations and direction for future research
The results of the present study should be interpreted in light of
its
limitations. First, the response rate to the NYTD survey was
slightly
over 50%, and while weighing procedures were implemented to
in-
crease generalizability, biases may still occur due to the
specific proce-
dures used. Second, both NYTD and AFCARS variables are
limited in
the amount of detail they provide. For instance, the
circumstances
under which youths were referred for a substance abuse
evaluation, as
well as reasons for incarceration and homelessness are not
assessed.
Furthermore, the wording of the NYTD variables may have
included a
wide range of behaviors, especially for substance abuse and
incarcera-
tion measures. Some youth referred for a substance abuse
evaluation
may have actually abused substances, while others could have
been re-
ferred erroneously. Similarly, some youth reporting
incarceration may
have spent a single night in jail, while others could have been

incarcer-
ated for long periods of time in connection with more serious
crimes.
The timing and frequency of these behaviors are also not
evaluated, as
only lifetime incidence is documented at age 17. In addition,
there is
limited detail about youths` removal circumstances, as well as
reasons
for the disruption of placements. The accuracy of large
administrative
datasets such as NYTD and AFCARS is also difficult to assess,
as states
may report partial or conflicting information in some cases.
Most impor-
tant, the findings of this study are limited by the cross-sectional
nature
of the analysis. Specifically, several indicators used to form the
clusters
(i.e. substance abuse referral, homelessness, incarceration) may
also
represent removal reasons in certain cases, as the timing of their
occur-
rence is unknown.
Future research should employ longitudinal designs to examine
the
link between child welfare factors and the functioning patterns
exhibit-
ed by the youth. Subsequent waves of NYTD data will allow
conducting
such investigations with a large, national sample. Future efforts
should
also follow youth until the age of 21 and, if possible, longer, to
assess
the stability and predictive utility of the obtained patterns, and

evaluate
the role of service provision in this regard. Such investigations
should
include additional indicators of successful adaptation as
applicable to
the age range studied. For instance, assessing employment
status, inde-
pendent living, earnings and dependence on public assistance
may be
appropriate for older youth who already left the child welfare
system.
Including indicators related to internalizing problems (e.g.
mental
health concerns, self-esteem) can be beneficent at any age, as
some
youth may function successfully in domains such as education
and em-
ployment, while still struggling with the aftermath of child
maltreat-
ment and years in out-of-home placement.
Acknowledgments
The data used in this publication were made available by the
Nation-
al Data Archive on Child Abuse and Neglect. Data from the
National
Youth in Transition Database were originally collected by the
states
and provided to the Children's Bureau. Funding was provided by
the
Children's Bureau, U.S. Department of Health and Human
Services. The
collector of the original data, the funder, the Archive, Cornell
University

Review 58 (2015) 227–235
and their agents or employees bear no responsibility for the
analyses or
interpretations presented here.
We are particularly grateful to Michael Dineen, Holly Larabee
and
Elliott Smith from the National Data Archive on Child Abuse
and Neglect
at Cornell University for their technical assistance and support.
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http://refhub.elsevier.com/S0190-7409(15)30070-0/rf0005

https://www.acf.hhs.gov/sites/default/files/cb/afcarsreport21.pd
f
https://www.acf.hhs.gov/sites/default/files/cb/afcarsreport21.pd
f
http://refhub.elsevier.com/S0190-7409(15)30070-
0/rf0230Functioning patterns among older adolescents in foster
care: Results from a cluster analysis1. Introduction1.1. A
person-oriented approach to understanding the functioning of

foster youth1.2. Factors influencing group membership1.3. The
present study2. Methods2.1. Dataset and procedure2.2.
Sample2.3. Measures2.3.1. Outcome indicators2.3.1.1. Current
school enrollment2.3.1.2. Connection to adult2.3.1.3.
Childbirth2.3.1.4. Homelessness2.3.1.5. Substance abuse
referral2.3.1.6. Incarceration2.3.2. Pre-removal factors2.3.3.
Post-removal factors2.4. Analytic strategy3. Results3.1. Cluster
analysis3.2. Pre-removal factors3.2.1. Demographics3.2.2.
Reasons for removal3.3. Post-removal factors4. Discussion4.1.
The “resilience” cluster4.2. The “substance abuse” cluster4.3.
The “multiple problems” cluster4.4. The “incarceration only”
cluster4.5. The “homelessness” cluster5. Recommendations for
policy and practice6. Limitations and direction for future
researchAcknowledgmentsReferences

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