Children: A Comparison of School-age and Infant Populations Introduction
Healthy development of children depends on various environmental influences (Shonkoff &
Phillips, 2000). One of these influences, socioeconomic status (SES) has been consistently associated with child outcomes (Duncan, Brooks-Gunn, & Klebanov, 1994; Hart & Risley, 1995;
Magill-Evans & Harrison, 2001; Melhuish, Sylva, Sammons, Siraj-Blatchford, & Taggart, 1999).
SES is conceptualised as a distal variable, and its effects may have an indirect impact on child outcomes through its influence on more proximal variables (Bradley & Corwyn, 2003). That is, SES is related to the quality of the child’s immediate home environment, and the home environment predicts children’s outcomes. Proximal environmental variables are those processes within the child’s immediate home environment that the child experiences directly. As Bronfenbrenner postulates, the effect of these factors increases with their proximity to the developing child (Bronfenbrenner & Morris, 2006). This suggests that variables associated with proximal processes are likely to have greater effects on children’s development than distal contexts. To illustrate this point, although maternal education, one of the commonly used SES variables, influences child outcomes, the child does not experience his/her mother’s level of education directly. Instead, what the child may experience proximally with a well-educated parent is responsive interactions, regular daily routines, as well as the provision of opportunities for stimulation. Distal contexts therefore directly and indirectly influence child development.
The quality of the home environment describes the degree to which the child’s environment provides opportunities for optimal development. Details on important aspects of the home environment are provided in an earlier chapter (Chapter 6). More supportive and stimulating home environments have been linked to better outcomes across several spheres for both infants and school-age children (Hart & Risley, 1995; Sarsour et al., 2011). However, in resource-poor settings, associated risk factors such as poverty are likely to preclude the provision of stimulating home environments for children mainly due to the level of daily stressors experienced (Baker- Henningham et al., 2003). For instance, poor parental psychosocial well-being related to stress (Conger, Rueter, & Conger, 2000) may impede parents’ abilities to engage in positive interactions with their children and to provide supportive home environments. Lack of economic resources may also limit the availability of developmentally appropriate physical and psychosocial resources (Brooks-Gunn & Duncan, 1997). Furthermore, the physical structures of the homes of these children which may be characterized by inadequate lighting conditions and overcrowding are
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associated with negative outcomes (Evans, 2006). Many of these children may therefore lack exposure to stimulating experiences and thus not acquire various skills as rapidly as children living in affluent homes (Elardo et al., 1975; Hart & Risley, 1995; Hoff-Ginsberg, 1991; Kiernan &
Huerta, 2008) which results in poorer developmental outcomes (Dollaghan et al., 1999).
Associations among the home environment, SES and child outcomes have been previously reported in resource-poor settings (Abubakar, van de Vijver, et al., 2008; Sarsour et al., 2011).
However, there have been no reports comparing the magnitude of these associations among populations of different ages within the same study. We therefore investigated whether the effects of the home environment and SES on language and motor outcomes among school-age children were similar to or different from those observed among infants.
Method
The study drew data from two sources. First was a study among a school-age population which sought to determine the neurocognitive functioning of children assessed using a battery of neuropsychological tests. Second was an infant study of the influence of prenatal exposures on child outcomes. Detailed descriptions of both studies are presented in Chapter 3. We set up a model with all the variables in Figure 1, including the two child outcomes, language skills and motor abilities.
School-age Study
We used a battery of neuropsychological tests to assess children’s neurocognitive functioning (Kitsao-Wekulo et al., 2012). In addition, we administered the home environment measure to 146 children (for details, refer to Chapter 6).
School-age outcomes. Two outcomes, language skills and motor abilities were selected for the current study. The Kilifi Naming Test (KNT), a test of confrontation naming, was used to assess expressive vocabulary (refer to Chapter 4). Children’s motor abilities were represented by an Overall Motor Index derived from scores obtained on tests of motor co-ordination and balance (refer to Chapter 3).
Socio-demographic information. Information on child gender, age and socioeconomic status was made available through the main study, as detailed in Chapter 3.
Infant Study
A cross-sectional sample was obtained from the infant population. Every third child within the three age bands, 12, 18 and 24 months, was systematically sampled from a sampling frame.
Only the children with complete data on all measures were included in the final sample (N = 231).
Infant outcomes. Language skills and motor abilities were assessed using the CDI and KDI respectively, as detailed in Chapter 3. These scores were transformed to z-scores to allow direct
154 comparison across variables.
Socio-demographic information. Information on the child’s gender and date of birth was obtained at delivery. Interviews with mothers provided information on various aspects of socio- economic status of the family.
Data Analysis
Descriptive statistics of raw scores were used to provide summary information about the sample and the study measures.
Structural equation modelling (SEM) was conducted by developing and testing a path model based on logic and theory about how gender, age and environmental factors (home environment and SES) would be expected to influence children’s language and motor skills. Initially, the Pearson Product-Moment Correlation Coefficient was used to determine the degree of association among the various variables included in the model, in order to establish if a linear relationship existed among them.
In this model, gender, age and SES were exogenous variables, home environment was a mediator variable, and the two child outcomes were endogenous variables. The paths between the exogenous, mediator and endogenous variables represented direct causal (structural) effects.
Residual (error) terms were identified for the mediator and endogenous variables. I fixed the paths leading from the residual terms to the observed variables to be 1. The hypothesised model depicting anticipated paths between predictors and these skills is presented in Figure 11. I then used the maximum likelihood method to estimate the model.
I used the Chi-square analysis in initial examination of the goodness of fit to ensure non- significance. However, because this method is sensitive to sample size, I used other indices of comparative fit including the Tucker Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA) (Bentler & Chou, 1987; Browne & Cudeck, 1993).
The initial model did not result in a good fit (Figure 12). Specific procedures to re-specify and modify the model were to remove non-significant (p ≥.05) paths and use modification indices as suggested by the AMOS SEM program (Arbuckle, 1988) to add paths or correlations that would improve model fit. Modification indices did not however suggest the need for additional paths or correlations. For the final model, acceptable fit was defined as TLI and CFI >.90 and RMSEA <.08 and an excellent fit as TLI and CFI >.95 and RMSEA <.05.
Results School-age Study
Descriptive data and variable inter-correlations. Descriptive data for each of the variables and variable inter-correlations are presented in Tables 35 and 36, respectively. Age had weak and
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negative correlations with household wealth, r(146) = -.177, p < .05. Moderate correlations were seen between age and the two child outcomes (language, r(146) = .311, motor, r(146) = .334, both p
< .01). The two environmental variables (household wealth and HOME scores) were moderately correlated with each other, r(146) = .499, p < .01, as were the two child outcomes, r(146) = .561, p
< .01.
Figure 11. Hypothesised model of the association between SES, home environment and child outcomes
Age
Psychomotor scores Household
wealth
Gender
HOME scores
Language scores
Results of the final model. The final model, shown in Figure 13, provided a good fit to the data, χ2 (7, N = 146) = 4.712, p = .695; TLI > .95, CFI > .95, RMSEA < .05. Higher age at assessment was related to both higher language and motor scores. Age was however not related to Kilifi-HIPSC scores. While higher family resources as assessed by the index for household wealth predicted more enriched home environments, direct paths from the wealth index to the language and motor measures were not significant. Household wealth explained 23% of the variance observed in home environment scores. Gender was significantly related only to language scores. This model also included direct paths from the Kilifi-HIPSC scores to both language and motor scores, indicating associations of more enriched home environments with higher scores on both language abilities and motor skills. Finally, the negative correlation of age with household wealth provides evidence that younger children had higher family resources than older ones. Moreover, the structural errors for the two test scores documented the correlation between these two measures
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(Table 37). The full model predicted 18% and 19% of the variance in language and motor scores, respectively. The model parameters and covariances depicted in the final model were all significant.
Figure 12. Initial model for the School-age Study
Age
Psychomotor scores R2= .19 Household
wealth
Gender
HOME scores R2= .23
Language scores R2= .16 -.03
.07 .06 .13
.43 .05 .15
.35
.04 -.17 .47
157 Figure 13. Final estimated model for the School-age Study
Age
Overall Motor Index R2= .19 Household
wealth
Gender
HOME scores R2= .23
Language scores R2= .18
.17*
.42***
.18**
.34***
-.20***
.48***
Minimum was achieved Chi-square = 4.712 Degrees of freedom = 7 Probability level = .695
*p<.05, **p<.01, ***p<.001
Infant Study
Summarised and individual descriptive data. Mean HOME scores were 18.28 (SD = 2.78) and ranged from 12 to 26. CDI scores ranged from four to 183, with a mean of 57.11 (SD = 33.08) while KDI scores ranged from 18 to 51 with a mean of 32.99 (SD = 6.00) (Table 35).
Girls had higher z-scores on the CDI, t(228) = -1.517, p = .131, and HOME, t(228) = -1.801, p = .073. Boys performed better than girls on the KDI, t(228) = 1.381, p = .169. These differences were however not significant.
Age differences were significant on CDI, F(2, 228) = 103.329, p < .001, ƞp
2 = .475, and KDI scores, F(2, 228) = 332.622, p < .001, ƞp
2 = .745, but not on the HOME scores, F(2,228) = .083, p = .920, ƞp
2 = .001. Household wealth was not significantly associated with KDI scores, F(2,228) = .752, p = .473, ƞp
2 = .007, CDI scores, F(2, 228) = 1.479, p = .230, ƞp
2 = .013, or HOME scores, F(2,228) = 1.603, p = .204, ƞp
2 = .014.
Variable inter-correlations. Correlations between age and household wealth, and between age and HOME scores were not significant. Age showed moderate (r(231) = .602, p < .001) to strong (r(231) = .836, p < .001) correlations with CDI and KDI scores, respectively. Household wealth and HOME scores had a weak correlation with each other, r(231) = .161, p = .014.
Correlations between household wealth and the two child outcomes were not significant. The two child outcome scores were moderately correlated with each other, r(231) = .529, p < .001 (Table
158 36).
Results of the final model. The initial model resulted in a good fit, χ2 (1, N = 231 = 2.004, p
= .157). However, some of the paths were not significant. Several revisions to the model were then made by deleting non-significant paths. The final model, shown in Figure 15, provided an excellent fit to the data, χ2 (9, N = 231) = 12.567, p = .183; TLI > .95, CFI > .95, RMSEA < .05. As with the School-age Study, higher age at assessment was related to both higher language and motor scores.
Higher family resources, as assessed by the index for household wealth, were correlated with more enriched home environments. Household wealth contributed to 5% of the variance observed on the home environment scores. Direct paths from the wealth index to the language and motor measures were not significant. The effects of household wealth on language scores were thus fully mediated by the home environment. Gender predicted HOME scores and was significantly related to only the language scores. This model included direct paths from the HOME scores to language scores but not to psychomotor scores. Correlation of the structural errors for the two test scores was not significant (Table 38). The full model predicts 43% and 70% of the variance in language and motor scores, respectively. The model parameters depicted in the final model were all significant.
Figure 14. Initial model for the Infant Study
Age
Psychomotor scores R2= .70 Household
wealth
Gender
HOME scores
Language scores R2= .43 .02
-.05 .01 -.03
.84 .09 .20
.61
.14 .11 .18
159 Figure 15. Final estimated model for the Infant Study
Age
Psychomotor scores R2= .70 Household
wealth
Gender
HOME scores R2= .05
Language scores R2= .43 .84***
.22***
.60***
.14* .10*
.18**
Minimum was achieved Chi-square = 12.567 Degrees of freedom = 9 Probability level = .183
*p<.05, **p<.01, p<.001
The Results of Hypotheses Testing