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How do the risk factors of ecological levels influence the
vulnerability of children?
Sung, Ming-Jiun* Su, Hsiu-chih
Paper presented at the European Conference on Educational Research,
University of Ghent, 19-21 September 2007
ABSTRACT
This study tested hypotheses from an ecological-developmental model for
childhood vulnerability and resilience. In this model, vulnerability is defined as a
child’s dysfunctional developmental outcomes which were rated by his/her
kindergarten teacher. In addition the child’s teacher checked 33 indices of risk factors.
These risk factors were from three types of ecological levels- individual
characteristics, parenting processes, and environmental context. Data of 200 at-risk
children were analyzed by using LISREL 8 statistical software. Structural equation
modeling indicated that the effect of individual characteristics factor was significant
and contributed uniquely to children’s vulnerability through parenting processes, but
the effect of environmental context factor was not significant. Additional analyses
suggested that some factors from environmental context can serve as a protective
function when parenting processes are compromised. Further research is needed to
examine the continuity and discontinuity of these distal and proximal risk factors that
influence the developmental trajectories of these at-risk children.
Keywords: risk factor, ecological system, vulnerability
*
Corresponding author
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Introduction
Over the past decades, much has been learned about factors that increase
children’s risk of maladjustment; and a variety of risk factors have been identified as
strongly tied to many kinds of child psychopathology. Understanding these risk
factors can provide a knowledge base for establishing preventive interventions for
children and family at-risk (Cowen, Wyman, Work, & Parker, 1990), and contributed
to knowledge of developmental processes, both normal and abnormal (Cicchetti &
Cohen, 1995; Sroufe & Rutter, 1984).
The challenge of understanding the origin precursors and core abnormalities of
the vulnerability of at-risk children has led a number of investigators to the “high-risk
method”. This method often focused on individuals who had experienced some
adversities, such as born to a low SES family. These individuals were found to have a
substantially higher risk for low academic achievement (Garmezy, 1978). The merit of
this method is that it avoids confounding the consequences of maladjustment or
disorders with their causes and precursors. However, the samples of different focused
groups of maladjustment or disorders often had many overlapping characteristics.
That means a lot of risk factors may co-occur, and these factors may have different
impact on different ecological systems to different people. For example, living in a
high-crime neighborhood is an index of risk factor. However, this factor too often
co-occurs with poverty, minority status, and exposure to community violence, which
also has substantial influence on children’s deviant development.
It seems that all the risk factors have same damaging effects on children’s
development, but it is not always so. Take the same example of living in a high-crime
neighborhood; harsh parenting may serve as a protecting effect by keeping children
away from deviant peers, and decreasing the events of becoming a victim of violence
or witnessing violence occurring to others, although harsh parenting often is
detrimental to children’s who are living in a safe community. So the same factor that
serves a risk function under some circumstances may not serve same function under
different conditions and may have different impacts on children’s developmental
outcome.
Besides, Bronfenbrenner (1977) suggested that not only the number of the risk
factors play a key role on children’s psychological adjustment, but also the types of
the risk factors. It means that social classes differ in many characteristics that foster or
impede psychological development of children. The ecological model proposed by
Bronfenbrenner(1977) emphasized the complexity of development and the large
number of environmental influences from a variety of domains such as neurological
domain, genetical domain, parenting domain, economical domain, family functioning
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domain, social support domain……etc. The Philadelphia Study (Furstenberg, Cook,
Eccles, Elder, & Sameroff, 1999) examined twenty factors in six different ecological
domains, ranging from those Microsystems (Bronfenbrenner, 1977) in which child
was an active participant, to those systems more distal to the child. These domains
and variables included family processes (parental investment, support for autonomy);
parent characteristics (education, mental health resourcefulness); family structure
(household crowding, marital status); management of community (social resources,
informal network); peers (antisocial, prosocial); community (neighborhood SES).
In Lengua’s (2002) study of children’s responses to multiple risk, a multiple risk
score was summed and consisted of 11 indices to represent demographic,
psychosocial and environmental, three types of risk factors. Demographic risk factors
included mother’s education level, family income, child’s ethnic or racial minority
status, single parent status, density in the home, and adolescent parent status.
Psychosocial risk factors included negative life events, maternal depression and
family history of problem. Environmental risk factors included the quality of the
home environment and the quality of the home environment and the quality of the
home environment and the quality of the neighborhood environment.
Sameroff and Fiese (2000) argue that the number of risk factors was the prime
determinant of developmental outcomes, and the same outcomes were the result of
different combination of risk factors. No single factor was consistently related to
either poor or good outcomes. So for each child, we need to analyze his unique set of
intervention strategies embedded within a developmental model. Rutter (1987)
reminded us that if we want to help vulnerable children, we need to pay special
attention to the protective processes that bring about changes in life trajectories from
risk to adaptation, such as reducing the risk impact on children, reducing the
likelihood of negative chain reactions to children, promoting self-esteem and
self-efficacy of children, and opening up opportunities for children.
In the Kauai Longitudinal Study (Werner & Smith, 1992), several risk factors
that exposed to a cohort of children were examined in the beginning. As their
longitudinal investigation progressed, those who successfully coped with their
negative experiences or adversities attracted more attention from the researchers.
Latent-variables path analyses were used to examine the links between protective
factors in the individual and outside sources of support in childhood and adolescence
that led to successful adult adaptation, and five clusters of protective factors were
identified. Cluster 1 included temperamental characteristics of individuals that helped
them to elicit positive responses from their caring persons. Cluster 2 included skills
and values that led to their effective abilities, such as vocational plan. Cluster 3
included parent’s caregiving styles that fostered self-esteem in the child. Cluster 4
consisted of supportive adults who acted as gatekeepers for the future. Cluster 5 was
the opening of opportunities at major life transitions.
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Our primary interest in the present study was to determine the direct and indirect
effects of risk factors from different levels of ecological system. We proposed a
hypothetical model based on Guralnick’s (1998) early development and risk factor
model Thurman’s (1997) ecological congruence model, and Brofenbrenner’s (1979)
ecological systems approach. Our model depicted in Figure 1 guided our examination
of the unique contribution that three types of ecological factors that influence the
children’s developmental outcomes. Figure 1 showed that the children’s
developmental outcomes were directly influenced by the factors of individual
characteristics, parenting processes, and environmental context. Then the parenting
processes factor had indirect effects on the child developmental outcomes through its
effects on individual characteristics. The environmental context factor may also have
indirect effects on child developmental outcomes through parenting processes and
through individual characteristics.
Individual risk
sex
Parenting risk
age
Child
vulnerability
Contextual risk
Figure 1 Model summarizing the hypothesized pattern of relations among individual
characteristics, parenting processes, and the environmental context, and the child
developmental outcomes.
Method
Participants
This study utilized a sample of 200 at-risk children. Participants were recruited
through the children’s kindergarten. The selected kindergartens can represent a variety
of sociodemographic characteristics of central Taiwan area. Information forms were
sent to the kindergartens, and the principals were asked to nominated at-risk children
in their kindergarten. Ninety-four kindergartens from 29 administrative districts
notified the potential respondents to us. Then a risk factor checklist was sent to the
teachers of these at-risk children. Two hundred and three mailed back checklists were
useful for further analysis.
Measures
Risk indices measurement
We used a checklist consisted of 3 subscales to collect the data of whether a child
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was exposed to any risk that is detrimental to a child’s development. These risk
factors were identified because they were either theoretically or empirically associated
with developmental outcome. We included factors examined by Sameroff and
colleagues (1987, 1993) and added a few other factors identified as risk factors for
seven domains of developmental outcomes. Some original criteria used by the
Rochester Project could not be examined in this study because we did not collect
information on these constructs, such as the number of recent contacts with mental
health professionals, whether parental occupations were skilled or unskilled.
Risk-index scores were computed as the sum of the numbers of the risk factors in the
subscales.
These indices are categorized into three levels. The first level is the individual
characteristics of a child, including peer reject, peer exclusion, social withdrawal,
depression, hyperactivity, impulsivity, attention deficit, aggression, self-care inept,
learning difficulty, chronic organic disorder, congenital disease, disability/ impairment
diagnoses, developmental delay. Cronbach's alpha based on standardized items for the
individual characteristics subscale is .813(9 items).
The second level is the parenting process, including education below high school
level, ethnic minority, parental psychopathology, parental disability diagnoses, out of /
instable job, take care by 24 hours natty, cross-generation parenting, harsh discipline,
inconsistent discipline, negative control. Cronbach's alpha based on standardized
items for the parenting process subscale is .724(9 items).
The third level is the environmental context, including live in a foster family,
single parent, homeless, latchkey child, abnormal parenting, low SES, poverty,
overcrowded family, domestic violence, alcohol addiction, drug abuse, reconviction /
criminal, neighborhood with juvenile, delinquency, few cognitive stimulation /
activities, low warmth / aloof. Cronbach's alpha based on standardized items for the
environmental context subscale is .529(15 items).
Outcome measurement
The developmental outcome measurement in this study is focusing on the school
readiness of children. We concerns about seven domains of children’s performance,
including class and task performance, cognitive performance, language and
communication, social functioning, externalizing problem, internalizing problem,
positive emotionality. Cronbach's alpha based on standardized items is .814(7 items).
Missing Data
The range of data point missing on single variables across models was between
1.4% and 31.3%. The data are no balanced, as some individuals will have missing
data on some of the responses. An estimation technique is required that and fit a
multivariate response model that includes missing data. In order not to introduce bias
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into the sample through listwise deletion of missing data, we used the multiple
imputation technique described by Rubin(1987) & Schafer(1996). This has been
identified as one of the best techniques for handling missing data (Allison, 2002). The
basic idea of multiple imputations is to generate a series of data sets from the joint
distribution of all variables. We used Markov Chain Monte Carlo (MCMC) estimation
(Browne, 2003). This model includes all missing data values as parameters to be
estimated. Where an individual had missing observations we estimated a mean and
variance for each missing observation based on the overall covariance structure of the
variables and the nonmissing values present for the individual. The distribution of
every missing value in the data set was estimated. The MCMC method provides
samples from these distributions. We replaced missing values in risk and outcome
variables with the imputation values.
Result
Descriptive Analyses
Descriptive analyses examined the association among the levels of risk factors,
children’s outcome measures, sex and age. Risk-index scores were computed as the
sum of the number of risk conditions experienced by each child. The intercorrelation
of variables is in Table 1. Several finding are worth mentioning. First, no differences
were found between any risk and outcome variables related to age. Second, more
significant correlations among risk and outcome variables were found for being a girl
than boys. Being a girl was associated with less individual characteristics risk than
boys. For the other variables, no significance was found between boys and girls. Third,
total risk is more consistently related to outcome variable than the individual
characteristics risk, Parenting process risk and environmental context risk.
Table 1 Intercorrelations among the study variables
Age
Age
Individual risk
.015
Parenting risk
Contextual
risk
Risk sum
.102
.117
.133
.060
-.067
.016
.448(***)
.407(***)
.271(**)
.557(***)
.150
.815(***)
.298(***)
Individual risk
-.111
Parenting risk
-.164
.315(**)
Contextual risk
.179
.429(***)
.378(***)
Risk sum
-.009
.772(***)
.658(***)
.842(***)
Child vulnerability
-.091
.616(*(*)
.272(*)
.420(***)
Child
vulnerability
.457(***)
.584(***)
Upper triangle matrix represents boys; lower triangle matrix represents girls.
For boys N = 118; for girls N=78 *p<.05; **p<.01; ***p<.001;
Variance in children’s vulnerability explained by individual, parenting and
environmental risk
A multiple regression analysis was performed in which the individual
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characteristics risk, parenting process risk and environmental context risk were
entered simultaneously into the regression equation. Multicollinearity was not
detected. The standardized regression coefficients shown in Table 2 indicate that
among the levels of risk factors, individual risk and environmental risk made
significant and independent contributions to the explained variance in children’s
developmental outcomes. The multiple regression analysis confirmed our expectation
that individual, parenting and environmental risk would explain significant and
unique portions of the variance in children’s vulnerability. The next section, in which
the path model was tested, will shed more light to this matter.
Table2
Linear regression analysis prediction predicting overall developmental vulnerability from
individual risk , parenting risk, and environmental risk domains.
Variable
Beta
T
Individual
.511
9.738***
Parenting
.138
2.338*
Contextual
.292
4.696***
Note: N = 203; R2 = .714; F = 166.348; p = .000; Durbin-Watson = 1.751
*p<.05; ***p<.001
The overall goodness-of-fit of the path model depicted in Figure 1 was assessed
using the LISREL 8 Statistical Software (Jöreskog & Sörbom, 1989). Given that
single indicators were used to represent all of the variables in the model, a structural
equation model in which all variables were directly measured, with no assumed
measurement error, was examined. The interrelations among the indicators depicted in
Table 1 constituted the input matrix for the analyses
The analysis of the initial model did not yield a significant fit, χ2(7df, N=203)
=16.167, p= .024; GFI = .974; AGFI = .923; RMSEA = .081; NFI = 879, TLI = .835;
IFI = .928; CFI = .923; RMR= .076, AIC = 44.167; NCP = .9.167.
The deletion of the path between the age and child vulnerability from the initial
model did not fit as well, χ2(4df, N=203) =11.688, p= .020; GFI = .978; AGFI = .917,
RMSEA = .097, NFI = .908, TLI = .836, IFI = .938; CFI = .935, RMR= .077, AIC =
33.668; NCP = 7.668.
The deletion of the path between the sex and child vulnerability from the initial
model did not fit as well, χ2(4df, N=203) =6.787, p= .148; GFI = .987; AGFI = .951,
RMSEA = .059, NFI = .944, TLI = .938, IFI = .976; CFI = .975, RMR= .091, AIC =
28.787; NCP = 2.787.
The deletion of the path between the individual risk and parenting risk result in a
significant reduction, χ2(1df, N=203) =.015, p= .902; GFI = 1.000; AGFI = 1.000,
RMSEA = .000, NFI = 1.000, TLI = 1.053, IFI = 1.008; CFI = 1.000, RMR= .006,
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AIC = 18.015; NCP = 0.000, indicated that this model, depicted in Figure 2, fit the
data well.
Parenting risk
.16
.65
.17
Individual risk
.18
Child
vulnerability
Individual risk
.25
Contextual risk
Figure 1 Final LISREL model of relations among individual characteristics, parenting
processes, and the environmental context, and the child developmental outcomes.
Result
In sum, the analyses of the model empirically confirmed our hypotheses
regarding the different level of risk may influence the children’s vulnerability. Several
interesting phenomenon is worth of discussing. First, age and sex did not have
significant impact on children’s developmental outcome in our data set. Second,
parenting risk does not have significant correlation with individual risk. Third, the
child vulnerability can be largely account for the individual risk, the parenting risk
and contextual risk have not much influence on child vulnerability. So the individual
risk may be a strong mediator of parenting risk and contextual risk.
In the present study, a set of hypotheses model of the levels of risk factors
influencing on children’s developmental vulnerability was tested. Given that the
analyses were not based on longitudinal data, it is important to keeping mind the at
the path model summarizing the results represents a pattern of covariation among
variables and does not allow for causal explanations. Although the direction of the
paths shown in the model was chosen on theoretical grounds, the existence of effects
in the opposite direction or bidirectional effects cannot be excluded. In general, the
pattern of association found in the path analytic procedures provided good support for
Guralnick’s (1998) assertion that the vulnerability of young children is multiply
determined by influences from three domains, individual risk, parenting risk and the
contextual risk were each found to explain a significant and unique portion of the
children’s vulnerability.
Selecting a viable statistical method to describe the association between risk
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factors and children’s vulnerability is important. Our study’s model confirmed that
children who experience risk factors associated with poverty, discrimination, and
family instability, and sociocultural risk measure include poverty, low levels of
maternal IQ, minority ethnic status, large household size, single parent families,
stressful life event and authoritarian child-rearing attitudes are more likely to have
children showing delayed cognitive and language development, poor academic
performance, conduct problems and emotional disorders. This is consistent with the
results of Deater-Dechard et al.’s (1998)study that the number of risk condition in the
child’s life was somewhat less predictive of child outcome than the model based on
risk variables. Overall developmental level was predicted best by the individual risk
variables considered together and was predicted worst by age. The level of prediction
of the overall developmental level was indexed by the overall main effect test in our
longitudinal analyses and by the squared multiple correlations in this study and in
Deater-Dechard et al.’s study of social emotional problems. This is not surprising in
that the tally of number of risk conditions experienced retain far less information
about the child’s environment than the individual risk variable is able to predict
outcomes more precisely when it contains more information about the underlying
attribute it is representing.
There are several limitations that need to be considered. First, the sample size in
this study was only moderate, and the number of risk factors was relatively large. The
additional information in the assessments allowed us to address questions about
whether the risk index, the summary risk factors, or the entire block of individual risk
variables were related to overall level of outcome. Second, the major purpose of this
study was to contrast statistical methods, so multiple analyses were conducted for
each outcome. Such overanalysis of data likely leads to results that are specific to this
sample and do not generalize to the population. Only the consistent findings across
approaches are likely to be reliable.
Third, children with multiple risk factors or who experience high levels of
exposure to risk are more likely to display lower levels of developmental
vulnerabilities, this approach did not always provide good prediction of rates of
change overtime, and interpretation of regression coefficients was hampered by
moderate levels of correlations among predictors.
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