Child Development, July/August 1999, Volume 70, Number 4, Pages 979–989
Family Influences on Adaptive Development in Young Children
with Down Syndrome
Penny Hauser-Cram, Marji Erickson Warfield, Jack P. Shonkoff, Marty Wyngaarden Krauss,
Carole C. Upshur, and Aline Sayer
In this study we investigated the extent to which the family environment predicted differences in trajectories of
adaptive development in young children with Down syndrome. The sample was comprised of 54 children
with Down syndrome and their families who were studied from infancy through the age of 5 years as part of a
longitudinal study of children with disabilities. Hierarchical linear modeling (HLM) was used to estimate the
parameters of hierarchical growth models in domains of adaptive development. Results indicated that growth
in communication, daily living skills, and socialization domains were predicted by measures of the family environment (i.e., family cohesion and mother-child interaction) above and beyond that predicted by maternal
education. Further, Bayley MDI measures during infancy did not predict changes in adaptive development in
any of the domains. The results are discussed in terms of implications for service provision and for expanding
theoretical frameworks to include the development of children with disabilities.
INTRODUCTION
The study of the development of children with disabilities has been based largely on either medical
models focused on clinical findings or on mechanistic
models of learning. Studies on the emerging competencies of children with biologically based impairment have only rarely and inconsistently been informed by theoretical advances in the field of child
development. Yet, by applying these evolving conceptual frameworks to children with atypical developmental patterns, our understanding of the full
range of human adaptation will be broadened.
Children with Down syndrome are the most extensively studied subgroup of those with cognitive disabilities. Several longitudinal investigations have
been conducted to document their development.
These studies, which have focused most often on describing the emergence of cognitive skills, have yielded
consistent patterns of findings. In an extensive longitudinal study, Carr (1995) investigated 44 English
children with Down syndrome from 6 months of age
to adulthood. She documented a general decline in
standard cognitive scores, followed by a slight increase during the late adolescent period. In a study of
children with Down syndrome in the United States,
Reed, Pueschel, Schnell, and Cronk (1984) reported a
lag in cognitive development over the first 3 years of
life. Other researchers have noted a similar pattern
(Connolly, Morgan, Russell, & Richardson, 1980; Gibson, 1978; Morgan, 1979; Nadel, 1988; Sharav, Collins,
& Shlomo, 1985), which indicates that by early childhood most children with Down syndrome have standard cognitive performance scores at least two standard
deviations below the normative mean. These findings do not imply a plateau in the development of
cognitive skills, but rather that cognitive skills increase at a slower rate of acceleration than found in
typically developing children.
Investigations of the emergence of other skills in
specific domains of development also have yielded
some consistent patterns. For example, children with
Down syndrome appear to demonstrate particular
weaknesses in communication, especially with respect to expressive (i.e., spoken) language (Chapman,
Schwart, & Bird, 1991; Dykens, Hodapp, & Evans,
1994; Smith & von Tetzchner, 1986; Stoel-Gammon,
1990). In contrast, impairments are relatively less pronounced in social development and in the mastery of
adaptive skills associated with the tasks of daily living
(Cornwell & Birch, 1969; Tingey, Mortensen, Matheson, & Doret, 1991); but growth in these domains still
progresses at a slower rate in comparison to that of
typically developing children.
Most longitudinal studies of the development of
children with Down syndrome have been primarily
descriptive in nature and have served an important
function, mainly to document children’s development. The work of Cicchetti and his colleagues (e.g.,
Cicchetti & Beeghly, 1990; Cicchetti & Ganiban, 1990;
Cicchetti & Pogge-Hesse, 1982) builds on these descriptive studies but provides a theoretically based
investigation of principles of development. Working
within a developmental-organismic framework, they
investigated the extent to which the development of
© 1999 by the Society for Research in Child Development, Inc.
All rights reserved. 0009-3920/99/7004-0014
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Child Development
children with Down syndrome is similar structurally
to that of other children. They found that development progresses through an orderly hierarchy of
stages, with predictable points of stage consolidation
and transition. For example, using a range of indicators, including children’s affective development and
growth in symbolic play skills, Cicchetti and Beeghly
(1990) describe the way in which the behavior of
young children with Down syndrome is organized
and integrated hierarchically. These findings support
the hypothesis that organismic principles of development apply to children with Down syndrome as they
do to all children. This proposition has implications
for parents and practitioners who seek to influence
the emerging capabilities of children with special
needs as well as for scholars concerned with furthering developmental theory.
Beyond the intrachild focus of most organismic
models, contemporary frameworks for studying child
development increasingly emphasize the importance
of transacting biological, social, and psychological
factors (Sameroff & Fiese, 1990), the various systems
in which children develop, including the family, school,
and community (Bronfenbrenner, 1986), and bidirectional transactions within and across contexts over
time (Lerner, 1991, 1998). Central to all of these current
perspectives is the recognition of the family as the critical context of development for the young child.
Recently, in reviewing investigations of interventions for young children at risk for developmental
problems, Guralnick (1998) proposed a model that
identifies key predictors of children’s cognitive development based primarily on family factors. Specifically,
he delineates two separate dimensions of families
that have effects on children’s developmental trajectories: (1) family characteristics, including sociodemographic features; and (2) patterns of interactions
within families. Whereas he posits an array of family
patterns that are associated with children’s development, he stresses the important predictive power of
the relational aspects of the family environment. Relationships within families include both connectedness of family members to each other and dyadic interactional systems (Krauss & Jacobs, 1990).
Many studies of typically developing children focus on the importance of relational aspects of families
in promoting children’s functioning (Minuchin, 1988),
but comparable investigations of children with disabilities have been more limited. In the bulk of published studies, researchers have concentrated largely
on the relation between children’s development and
family demographic characteristics, such as socioeconomic status (SES), parent education levels, or measures of parent intelligence. The findings from these
studies have revealed a mixed pattern. Some report a
significant positive relation between family demographic factors and the intelligence of their offspring
with Down syndrome (Fraser & Sadovnik, 1976;
Golden & Pashayan, 1976; Sharav et al., 1985; Turner,
Sloper, Knussen, & Cunningham, 1991), whereas
others fail to find evidence of such a relation (Bennett,
Sells, & Brand, 1979; Carr, 1995; Irwin, 1989).
A growing base of studies of children with disabilities is emerging that extends developmental findings
beyond their association with sociodemographic features of families to relational features of the family
system. Although few in number, investigations of
the relation between family cohesiveness, or connectedness, and the development of children with disabilities suggest that this aspect of family functioning has
important predictive value. In a study of 115 families
of children with mental retardation during middle
childhood, Mink, Nihira, and Meyers (1983) found
that cohesive, harmonious families had children with
more positive socioemotional functioning. Mink and
Nihira (1986) further found that family cohesion influenced the psychological adjustment of slow-learning
adolescents. Warfield (1995) investigated a subsample
of 85 children with developmental disabilities participating in the Early Intervention Collaborative Study
(EICS; Shonkoff, Hauser-Cram, Krauss, & Upshur,
1992) and reported that greater family cohesiveness
measured at the age of 3 years predicted fewer children’s behavior problems at the age of 5 years. In
other investigations of the children with Down syndrome within the EICS sample, higher levels of family cohesiveness predicted greater motivation on mastery tasks during the preschool years (Hauser-Cram,
1993) and more positive peer interactions in the preschool classroom (Hauser-Cram, Warfield, Bronson,
Krauss, Shonkoff, & Upshur, 1997).
Dyadic relationships within families, especially
mother-child interactive behaviors, have been more
extensively studied than other aspects of the family
environment. The link between contingent and responsive mother-child interaction and strong performance on child skills is well documented for young
children with and without disabilities (Barnard &
Kelly, 1990). Research on mother-child dyads in samples of children with Down syndrome indicates that
such children may be less responsive social partners
for their parents, especially in early infancy, as they
tend to initiate less interaction and produce fewer discernable social cues (Spiker & Hopmann, 1997). Nevertheless, positive interactions between mothers and
infants (both typically developing and those with disabilities) are associated with better subsequent cognitive and communicative skills (Barnard, 1997).
Hauser-Cram et al.
Thus, although family environments can be conceptualized in many ways, the pattern of results from
prior investigations suggests that the relational aspects of such microsystems—especially family cohesion and dyadic interaction—may be particularly potent in predicting the development of children with
disabilities. Although these predictions have emerged
from more than a single study or an isolated sample,
they have been limited to the prediction of status (i.e.,
development at one point in time) rather than of
growth (i.e., development across multiple points in
time). Furthermore, most prior investigations have
considered one, but not both, aspects of the family relational environment. Therefore, this investigation is
unique in its focus on the extent to which the family
environment, which incorporates measures of both
maternal dyadic interaction and family cohesion, predicts growth over the early childhood period in children with Down syndrome.
This study also differs from prior studies on the development of children with disabilities in another
way. Other contemporaneous longitudinal studies of
children with special needs have provided valuable
information on stability and change in children’s cognitive skills (e.g., Dunst & Trivette, 1994; Keogh, Bernheimer, & Guthrie, 1997). In contrast, we have chosen
to investigate indicators of adaptive behavior because
of their importance for everyday functioning in community settings and because of their value in predicting later functioning for individuals with disabilities
(Harrison, 1987). In addition, mental retardation is
not defined solely on the basis of cognitive performance, but includes an assessment of adaptive behavior (American Association on Mental Retardation,
1992). Although often correlated with cognitive performance, adaptive behavior focuses on typical functioning rather than on maximum performance, thereby
making it a more practical and meaningful outcome
to parents and service providers (Meyers, Nihira, &
Zetlin, 1979). Furthermore, although definitions of
adaptive behavior differ, they possess common elements, include skills that reflect age-appropriate expectations, are defined by the social context, focus on
independent functioning, and involve social and
communication domains (Witt & Martens, 1984). Finally, because adaptive behavior assumes modifiability rather than stability (Harrison, 1987), it is an area
of development where contextual influences may be
particularly important.
Research Hypothesis
This investigation was conducted to test the hypothesis that the family relational environment pre-
981
dicts the development of adaptive functioning over
the first 5 years of life in children with Down syndrome. Specifically, we hypothesized that family relations, defined as cohesiveness of the family and
mother-child interaction, predict positive growth in
children’s adaptive functioning above and beyond infant psychomotor functioning and maternal education level. Understanding the predictive power of features of families during early infancy, when children
with Down syndrome typically enter early intervention programs, is valuable to service providers and
program planners who target services to those who
most need them. Such understanding also can assist
and empower parents as active agents in promoting
their child’s optimal development.
METHOD
Sample
The sample analyzed for this study includes children with Down syndrome who participated in the
Early Intervention Collaborative Study (EICS), a longitudinal investigation of the predictors of resilience
and vulnerability in the emerging competencies of
young children with disabilities and the adaptive capacities of their families (Shonkoff et al., 1992). The
study sample was recruited from 29 communitybased early intervention programs in Massachusetts
and New Hampshire between 1985 and 1987.
A total of 54 children with Down syndrome entered the study at the time of their enrollment in early
intervention. This sample represented 79.4% of eligible children (i.e., all infants with Down syndrome
who entered the 29 state-supported early intervention programs during the time of sample enrollment);
sample children and families did not differ statistically from nonparticipants on child gender, race, or
age at study entry, maternal education, marital status,
employment status, or family income. Project physicians conducted an independent review of medical
records to confirm that each child had a diagnosis of
Down syndrome.
Table 1 presents the sociodemographic characteristics of the sample. On average, the children were 3.4
months of age (SD 5 2.0) when they entered the
study. Their mean Mental Developmental Index
(MDI; Bayley, 1969) at study entry was 70.7 (SD 5
14.3), and their mental age was 2.0 months (SD 5 1.6).
The vast majority of the sample was White, and one
half was female.
The mothers, on average, had 14.1 years of education. Most were married, two fifths worked outside
the home, and slightly more than one half of the sam-
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Table 1 Demographic and Socioeconomic Characteristics of
the Samplea
Characteristics
n
%
Child
Age at T1 (months)
Mental age (months)
Bayley MDI score
Racial/ethnic origin
European American
African American
Hispanic American
Other
47
1
3
3
87.0
1.9
5.6
5.6
Mother
Years of education
Married
Employedb
48
21
88.9
39.6
Family
Income at study entryc
Less than $20K
$20K–$30K
Greater than $30K
15
14
23
28.8
26.9
44.2
M
SD
Range
3.4
2.0
70.7
2.0
1.6
14.3
1.3–10.8
1.0–7.0
43.0–100.0
14.1
2.4
8.0–21.0
5 54.
percentage is based on a sample size of 53, due to missing data.
c The percentage is based on a sample size of 52, due to missing data.
aN
b The
ple had an annual family income of less than $30,000
at study entry.
Measures
Adaptive functioning. Repeated assessments of adaptive functioning measured at four time points were
the outcome of interest in this investigation. The interview form of the Vineland Adaptive Behavior
Scales (Sparrow, Balla, & Cicchetti, 1984) was used to
assess child functioning in four domains: communication, daily living, motor, and social. The Vineland is
a 577-item questionnaire measuring individual personal and social competence from birth through
adulthood. Data on this questionnaire were gathered
through a semistructured interview that was conducted with the mother during each of four home
visits so that growth in the four domains could be
charted over time.
The interviewer asked the mother to identify the
skills her child demonstrated on a regular basis. For
example, communication skills included smiling and
use of 50 words or more; daily living skills included
eating solid food and being toilet trained at night;
social skills included showing interest in others and
having a preferred friend; and motor skills included
sitting supported for 1 minute and climbing on low
play equipment.
Growth modeling requires that scores have three
characteristics: (1) they are interval, (2) they show
metric invariance over time, and (3) they are not standardized. To conform to these requirements and to
maximize interpretability of the results, we used subscale scores that were converted to age equivalents.
The Cronbach’s a reliability coefficients for each of
the four data collection points in order were .74, .80,
.89, and .94 for communication; .73, .72, .77, and .92
for daily living; .80, .79, .78, and .87 for motor; and .81,
.74, .75, and .91 for social.
Independent variables were measured at study entry and included the following:
Psychomotor development. The Bayley Scales of Infant Development (Bayley, 1969) was used to assess psychomotor development. This well-known instrument
has 163 items and assesses object relations, perceptualmotor skills, memory, learning, problem-solving, and
early communication through a series of tasks presented to the child by an examiner. A raw score is calculated based on the number of items passed in the
domains described above. The raw score can be converted to a standard score, the Mental Development
Index (MDI). In this sample, the MDI during infancy
and toddlerhood, the two time points in which this
measure was used, was moderately stable, r(53) 5 .54,
p , .001.
Mother’s education. Mothers reported the number
of years of education they had completed on a demographic information form.
The Nursing Child Assessment Teaching Scale (NCATS).
This widely used measure is an observational tool designed to assess a teaching interaction between a
mother and her child (Barnard, 1978). The mother is
asked to teach her child a task, such as reaching for a
ring or building a tower out of blocks, that is just beyond his/her ability level as determined by the child’s
performance on the Bayley Scales. The scale consists of
73 binary items that produce two summary scores; one
score for the mother’s interaction with her child and
one score for the child’s interaction with his/her
mother. Building on the results of prior studies that
emphasize the importance of maternal contingent interaction (Barnard & Kelly, 1990), we selected the
score for the mother’s interaction with her child for
use in this analysis. This score is computed by summing the scores from the 50 items that comprise four
subscales: sensitivity to cues, response to distress,
social-emotional growth fostering, and cognitive
growth fostering. The NCATS assessment conducted
during the first home visit (i.e., Time 1, T1) was used
in the analysis. At T1, the mean score was 37.3 (SD 5
5.8) and the scores ranged from 23 to 48. Higher
scores indicate better mother-child interaction. The
Cronbach’s a reliability coefficient was .79.
Hauser-Cram et al.
The Family Adaptability and Cohesion Evaluation Scale
(FACES II). This measure is a 30-item self-administered
questionnaire that assesses two aspects of family functioning: emotional cohesion (16 items) and adaptability (14 items) within the family (Olson, Bell, & Portner, 1982). The measure of emotional cohesion was
used in the present analysis. Each item describes how
a family may function (e.g., family members are supportive of each other; the family does things together)
and respondents rate how often this description applies to their family on a scale from 1 (never) to 5 (always). The cohesion score is calculated using the formula developed by Olson et al. (1982). Mothers
completed the questionnaire following the T1 home
visit. At T1, the mean score was 67.6 (SD 5 7.8) and
the scores ranged from 44 to 80. Higher scores indicate more family cohesion. The Cronbach’s a reliability coefficient for the cohesion subscale was .87.
Procedure
Data were gathered during home visits conducted
at four points in time: Time 1 (T1), at entry into the
study that occurred within 4 to 6 weeks of the child’s
enrollment in an early intervention program (M 5 3.4
months, SD 5 2.0); Time 2 (T2), after 1 year in the
early intervention program (M 5 15.5 months, SD 5
2.0); Time 3 (T3), within 4 weeks of the child’s third
birthday (M 5 36.6 months, SD 5 1.0); and Time 4
(T4), within 4 weeks of the child’s fifth birthday (M 5
60.7 months, SD 5 1.5). Each home visit was conducted by two research field staff who were not familiar with the study’s hypotheses. Field staff were
trained to the levels of reliability required by each
measure, and were checked for reliability throughout
the study.
One staff member interviewed the mother,
while the other staff person conducted the child assessments followed by the interactional teaching
measure. Finally, a packet of self-report questionnaires, which included the FACES II, was left for the
Table 2
983
mother to complete after the visit and mail back to the
project office.
Data Analysis
The analytic strategy employed for this investigation used individual growth curve analysis to model
intra-individual change over time in the four domains
of adaptive functioning and to detect the predictors of
interindividual variation in growth in adaptive functioning (Rogosa & Willett, 1985; Willett, 1988). We used
the hierarchical linear modeling (HLM) program of
Bryk, Raudenbush, and Congdon (1994) to estimate
the parameters of the hierarchical growth model. The
Level-1 model estimated a growth trajectory in each
domain of adaptive behavior for each individual in
the sample based on the repeated measurements.
Thus, for each domain of adaptive behavior, each individual’s trajectory was summarized by a set of
growth parameters. The values (e.g., an intercept and
a slope that represents change over time) of these parameters vary across individuals and can be used as
outcomes in Level-2 analyses (see Appendix). Variation in growth can be predicted from a prescribed set
of specific person-level variables. To test our central
hypothesis, the model included three person-level
predictors: the Bayley MDI, mother’s education, and
family environment. The family environment variable was a composite score created by standardizing
the mother-child interaction and family cohesion
measures using a z-score transformation. The composite measure was the sum of the two z-score variables. Preliminary models that included both motherchild interaction and family cohesion indicated that
these variables were correlated, r(53) 5 .57, p , .001.
RESULTS
Table 2 presents the percent of variance explained by
each model and the parameter estimates and associated p values for the three predictors. Four findings
Growth Curve Analysis: Predicting Development in Adaptive Functioning from Child and Family Factors
Rate of Change
Predictors
Bayley MDI
Mother’s education (years)
Family environment composite
Percent explained
Communication
.001 (.001)
.014 (.009)
.032* (.013)
14.3
Note: Numbers are b coefficients. Standard errors are in parentheses.
* p , .05.
Daily Living
Social Skills
Motor Skills
.000 (.001)
.004 (.008)
.026* (.011)
.001 (.001)
.019* (.009)
.026* (.013)
.001 (.001)
.002 (.008)
.008 (.011)
6.8
23.3
0
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Child Development
are notable. First, none of the variables tested was a
significant predictor of growth in motor skills. Second, the MDI measured during infancy was not a significant predictor of growth in any of the domains of
adaptive functioning. Third, mother’s education was
a significant predictor of growth in social skills only.
On average, higher maternal education was associated with greater growth in this area. Finally, and
most important, the family environment composite
measure was a significant predictor of growth, over
and above the Bayley MDI and mother’s education
level, in three domains: communication, daily living, and social skills. The model explained the greatest percent of variation in the growth in social skills
(23.3%), and a lower percent of variance in the
growth in communication (14.3%) and daily living
skills (6.8%).
In order to depict the influence of the family context on growth rates over time, the results are presented as a series of graphic displays that summarize
the fitted relations between growth and the predictors
of interest. Thus, Figure 1 presents the growth curves
of “prototypical” children (i.e., children who have
specific values for the predictor variables and are chosen to serve as exemplars of the range of effects). By
using the estimates from the models presented in
Table 2, predicted values at each time point can be calculated for an individual child with any combination
of values on the Level-2 variables.
We present the growth curves of “prototypical”
children who have the same Bayley MDI and
whose mothers had the same level of education but
whose families differed in their scores on the family
environment composite. In Figure 1A, we present the
communication trajectories of two prototypical children. The children each had a Bayley MDI score of
70.74 and the mothers of both children had 14.1 years
of education. These values were chosen because they
are the mean values for each variable. The family environment composite score for Child A was selected
to be 1.774 (i.e., one standard deviation above the
sample mean score), and the score for Child B was selected to be 21.774 (i.e., one standard deviation below
the sample mean score). Therefore, the gap between
the two trajectories in Figure 1A represents the effect
size, or the magnitude of the effect of the family context composite variable on growth in communication
skills. Note that there is no significant difference in
where the trajectories begin, illustrating that the children do not differ in communication at entry into the
study (at 3.4 months of age). There are, however, important differences in the rate at which the children
progress. Child A (in a positive family context)
progresses at a faster rate than Child B (in a less desirable family context) so that at the age of 5 they are
separated by a gap of 6.2 points. Because the out-
Figure 1 Growth trajectories in communication, daily living,
and social skills for two prototypical children, one with a high
composite and the other with a low composite measure of
family environment.
Hauser-Cram et al.
comes are age equivalence scores measured in months,
this translates into a difference of 6.2 months. Similarly, the daily living score and the social skills score
for Child A at age 5 are 4.8 and 5.5 months higher respectively than the scores for Child B (see Figures 1B
and 1C).
DISCUSSION
The analyses in this study were based on ecological
and contextual theories of development which highlight the importance of the family environment in
supporting the emergence of children’s skills. More
specifically, the analyses were driven by a conceptual
framework that positions the relational aspects of the
family environment as central to children’s developmental trajectories. Three key findings from this investigation have important implications for both
practice and theory. First, the lack of predictive power
of the Bayley MDI indicates that such assessments of
infant psychomotor skills are poor prognosticators
of development in adaptive domains, at least for children with Down syndrome. The modest correlation
of infant psychomotor assessments with later cognitive performance has been demonstrated frequently
(Cicchetti & Wagner, 1990; McCall, 1979). Our results
suggest that although data from other studies indicate
that cognitive and adaptive behavior appear to be correlated during later phases of the life cycle (Carr, 1988),
they may have different trajectories and predictors of
growth during the early childhood period.
Second, although motor skills were similar to other
domains of adaptive behavior in their lack of relation
to infants’ MDI scores, they differed significantly in
their lack of relation to other measures of the family
environment. Of the four areas of adaptive behavior
investigated, motor development was the only domain that was found to be impervious to measured
environmental effects. This lack of sensitivity to contextual differences indicates that the motor domain,
more than other dimensions of adaptive behavior, appears to be less influenced by variations in the psychosocial aspects of family experiences.
Third, the results of this investigation highlight the
power of family factors in predicting growth in communication, social, and daily living skills for children
with Down syndrome. The general relation between
sociodemographic factors, such as maternal education, and children’s development has been well documented (Duncan, Brooks-Gunn, & Klebanov, 1994).
This study builds on the results cited by prior researchers who reported a relation between maternal
education and cognitive performance in children with
Down syndrome (e.g., Golden & Pashayan, 1976), by
extending that relation to growth in domains of adap-
985
tive behavior as well. Specifically, our findings demonstrate a significant correlation between mother’s
education and growth in social skills.
More important, our data indicate that measures of
family processes appear to have important predictive
power for the development of children with Down
syndrome above and beyond that of maternal education. Specifically, early relational aspects of the
family environment appear to be important predictors of children’s growth in communication, social
skills, and skills of daily living. Families in which
members feel more connected to each other, and in
which mothers engage in emotionally supportive, contingently responsive and cognitive growth-promoting
interactions with their child with Down syndrome,
have children who, over time, demonstrate significant
benefits in communicating and socializing with others
and in their ability to engage in independent self-care
tasks. These findings clearly demonstrate the importance of the family context in promoting development
for children with biologically-based disabilities.
We recognize the need for caution in evaluating
these findings. First, the sample lacks representation
of diverse groups. The families in the study were
quite stable in terms of their configuration and economic well-being, and they varied little in ethnic background. In additional analyses, we found evidence of
stability in measures of the family environment during
early childhood (Warfield, Krauss, Hauser-Cram, Upshur, & Shonkoff, 1999). Therefore, the extent to which
the findings of this investigation relate to families
with more diverse characteristics or more fluctuating
home environments requires further inquiry.
The lack of cultural diversity in this sample is an
important limitation on the findings. Views of the
meaning of disability to a family (Hanson, 1992; Weisner, Beizer, & Stolze, 1991) and of ways in which parents interact with their children vary for different cultural groups (Garcia Coll, 1990; Laosa, 1980). Thus,
although the importance of family context in predicting development is a valuable principle to test, the
specific relations found in this investigation may not
be replicated in cultural groups where the meaning of
disability, of family cohesion, and of traditions of maternal interaction differ from those in a European
American sample.
Furthermore, not only do children affect their family, but their family also affects them. Thus, the transactional nature of those relations over time requires
further elaboration. Hodapp (1997) postulates that,
because children evoke different responses from their
caregivers, the “evocative environment” may be important to understand in investigations of families of
children with Down syndrome. In a study of children
with a variety of developmental delays and disabili-
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Child Development
ties, Gallimore, Weisner, Bernheimer, Guthrie, and
Nihira (1993) described how parents made accommodations to the daily routines of family life based on their
children’s needs. Thus, the web of relations among
children’s needs and the attention and accommodations they evoke from their caregivers is complex and
warrants thoughtful investigation and measurement.
Additionally, we note that children and families in
this study were enrolled in early intervention programs. Our data do not include measures of the quality of these programs, and it is possible that program
quality affects both family functioning and child outcomes. We do know, however, that programs provided the range of services typical of early intervention programs throughout the country (Shonkoff et
al., 1992).
Finally, the measures used in this investigation
were not all based on independent reports. Although
the measures of mother-child interaction and child
psychomotor performance were collected by trained
observers or examiners, the measures of adaptive
functioning and family cohesion were based on maternal report. The measures utilizing maternal report
were collected in different ways (i.e., by interview
and by questionnaire), but as both are based on maternal perception, they have shared variance.
Despite these caveats regarding overgeneralization of our findings, our analyses point to the value of
considering the family context in attempting to understand variability in the development of children
with disabilities. Although the characteristic features
of Down syndrome are biologically based, the data
from this investigation demonstrate the importance
of the family environment, especially relational processes, in explaining individual differences in crucial
areas of adaptive development. The findings from
this study have direct implications for policymaking
and individual service delivery in a wide range of
early intervention and other early childhood programs. Of greatest importance, these data underscore
the limitations of focusing solely on the child’s needs,
and highlight the importance of enhancing dyadic interaction patterns and family cohesiveness as a potent
mechanism to promote children’s development. Families who enter early intervention programs with low
levels of internal cohesion and mothers who exhibit
less responsive and less contingent interactive relationships with their infants may have greater and/or
different service needs than other families. Perhaps
more important, basing decisions about service delivery primarily on children’s medical diagnosis, levels
of cognitive impairment, or demographic variables,
like mother’s education level, may result in the provision of inadequate or even misplaced support, and
thereby fail to promote the full range of competencies
of children with special needs.
The findings presented here also have implications
for child development theory, as they suggest that
children with Down syndrome, like other children,
are affected by family relationships. Thus, contextually based ecological models, although constructed
primarily with a view toward typical development,
have considerable untapped salience for understanding and predicting the functioning of children
with biologically based impairments. Studies of
children with disabilities can add much to our understanding of the extent of variability in individual
adaptation, the range of families in which child development unfolds, and the relations between the
two. To this end, the construction of more sophisticated models of human development will be advanced considerably by the inclusion of what we
are able to learn about the emerging abilities of children with developmental disabilities.
ACKNOWLEDGMENTS
The Early Intervention Collaborative Study has been
supported by grants MCJ-250533 and MCJ-250583
from the Maternal and Child Health Bureau (Title V
Social Security Act), Health Resources and Services
Administration, U.S. Department of Health and Human Services, and by grants from the Jessie B. Cox
Charitable Trust, the Foundation for Child Development, the John D. and Catherine T. MacArthur Foundation, and from the Massachusetts Department of
Education, Bureau of Early Childhood Programs.
We are most grateful to the children and families
who have participated in this project for many years.
We also thank those who participated in data collection, especially Ann Steele, the project manager, for
her dedication to the project and thoughtful comments on this manuscript. We appreciate the careful
assistance in the preparation of this manuscript provided by Jean McCoy. Finally, we acknowledge the
contributions of Richard M. Lerner and Larry H. Ludlow to the concepts and analyses presented here.
ADDRESSES AND AFFILIATIONS
Corresponding author: Penny Hauser-Cram, Boston
College, School of Education, Department of Counseling and Developmental Psychology, Campion
Hall, Chestnut Hill, MA 02467-3813; e-mail: hausercr@
bc.edu. Marji Erickson Warfield is at the University of
Massachusetts Medical School, Worcester, MA; Jack P.
Shonkoff and Marty Wyngaarden Krauss are at the
Hauser-Cram et al.
Heller School at Brandeis University, Waltham, MA;
Carole C. Upshur is at the University of Massachusetts at Boston; and Aline Sayer is at Pennsylvania
State University, University Park, PA.
APPENDIX
Hierarchical Linear Model (HLM) used to Identify
Predictors of Adaptive Functioning in Children
with Down Syndrome
After examining plots of repeated adaptive functioning
scores in each of the four domains versus time for each sample member, a linear growth model was developed and
used at Level 1:
ADAPTpt 5 P0p 1 P1p(AGEpt 2 3.4) 1 Spt
where p 5 1, . . . , 54 subjects and t 5 age at each measurement. In this model, age has been rescaled by subtracting
the mean age at which children in the sample entered this
study from each child’s actual chronological age of entry.
The choice of centering facilitates interpretation of the
model intercept (P0p), which represents the value of adaptive functioning at the sample mean age of entry into the
study for child p. The model slope (P1p) describes the
monthly growth rate in adaptive functioning or the constant rate of change in adaptive functioning per month for
child p.
The Level-2 model can be represented as:
P0p 5 g00 1 [g01Xp 1 . . . g0kXp]
P1p 5 g10 1 [g11Xp 1 . . . g1kXp] 1 u1p
where k 5 the number of predictors in the model. The
Level-2 variance of the model intercept term was constrained to zero because children in the sample did not vary
significantly in adaptive functioning skills upon entry into
the study.
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