Socioeconomic status has been examined in fewer Down syndrome studies, particularly in the United States. The most readily available measure of socioeconomic status is parental education; in the United Kingdom, paternal occupational status is frequently used as a measure of socioeconomic status. A smaller number of birth defects studies have collected data on family income, which generally requires that parents be surveyed. Case–control studies of birth defects collect measures of family socioeconomic status but typically exclude chromosome abnormalities; one exception is a study from California (Torfs & Christianson, 2003). An alternative approach to controlling for income levels is to use geocoded data to link census data on median family income in a defined area to data with confirmed cases of Down syndrome.
Findings on the association of socioeconomic status with Down syndrome are mixed. Several epidemiologic studies have reported that higher-status families or residential districts are significantly more likely to have children with Down syndrome, not controlling for maternal age (Gath & Gumley, 1986; Hodapp et al., 2008; Shepperdson, 1985; Vrijheid et al., 2000). Two cohort studies from the United Kingdom reported that the distribution of paternal occupations was similar to that of a comparison group but with a small excess of higher-status occupations in the Down syndrome group along with older parental age (Carr & Hewett, 1982; Cunningham, 1996). Some other studies have reported no significant difference in socioeconomic characteristics of families with infants with Down syndrome and families of other infants, including studies from Sweden using a national birth register that included measures of family income and housing quality (Ericson, Eriksson, & Zetterstrom, 1984), birth defects surveillance data in the Czech Republic (Dzurova & Pikhart, 2005), and a case–control study of live births in Italy (Rosano, Del Bufalo, & Burgio, 2008). Other studies have reported an inverse association, with higher socioeconomic status families having a lower probability of having a child with Down syndrome (Dzurova & Pikhart, 2005; Khoshnood et al., 2006; Knox & Lancashire, 1991; Torfs & Christianson, 2003). Ecological studies in the United Kingdom found no association between local measures of socioeconomic status and the prevalence of Down syndrome (Lopez, Stone, & Gilmour, 1995).
Torfs and Christianson (2003) tested the hypothesis that social and economic disadvantage experienced by a woman throughout her lifetime increases her risk of having a fetus with Down syndrome. The data come from a survey of families of children with birth defects collected during 1991–1993 by the California Birth Defects Monitoring Program (CBDMP), in which parents were interviewed regarding potential risk factors. The authors constructed a scale based on five measures of low socioeconomic status over a woman’s lifespan up to time of conception—mothers’ education less than high school, mother’s low-status occupation, father’s education less than high school, father’s low-status occupation, and current family income less than $20,000. The scale had a linear association with Down syndrome, with a positive response on each item associated with an 18% higher risk of Down syndrome. The authors reported adjusted odds ratios ranging from 1.2 for one risk factor up to 1.9 for four or more socioeconomic status risk factors, controlling for maternal age and Hispanic ethnicity. When the association was restricted to live births, the association with low socioeconomic status was stronger for older women.
A second study from California also reported an inverse association of socioeconomic status with risk of Down syndrome, using a single socioeconomic status measure, maternal education, and outcomes restricted to live births (Dzurova & Pikhart, 2005). The investigators used routinely collected CBDMP data on live births from 1996 to 1997, with mother’s education level used as a proxy for socioeconomic status. After adjusting for maternal and paternal age, mothers with only a secondary education were roughly twice as likely and primary-educated mothers almost three times as likely as university-educated mothers to have a live-born infant with Down syndrome. No such association was observed in a parallel analysis of data from the Czech Republic. The authors suggested that the gradient of Down syndrome risk with education in California likely reflected differential use of prenatal diagnosis and termination by more highly educated couples, unlike the situation in the Czech Republic.
One US study used both maternal education and average family income in a locality as measures of socioeconomic status. Hodapp et al. (2008) reported that significantly fewer mothers of children with Down syndrome in Tennessee did not graduate from high school, 17.3% vs. 22.7% for mothers in general. However, very young mothers, who are less likely to have completed high school, are unlikely to have a Down syndrome-affected birth. Unless one adjusts for maternal age, incomplete education may not be a useful indicator of socioeconomic status. The authors also reported that the median family income based on maternal zip code of residence at time of birth was 3.7% higher ($38,584 vs. $37,218) for mothers of children with Down syndrome compared to mothers of unaffected children in Tennessee. That tabulation did not adjust for differences in maternal age or race.
Researchers in Europe have reported varying associations of socioeconomic status with Down syndrome. UK studies found that ecologic measures of both socioeconomic status and occupational status were positively associated with Down syndrome, which reflects differences in maternal age at birth (Gath & Gumley, 1986; Lopez et al., 1995; Shepperdson 1985; Vrijheid et al., 2000). A study from France found that maternal socioeconomic status, as proxied by her occupation, was inversely associated with Down syndrome live birth as a result of low socioeconomic status mothers being more likely to bring a Down syndrome fetus to term (Khoshnood et al., 2006), similar to the pattern observed in California.
A two-way ANOVA was used to examine whether there is a statistically significant main effect between teacher efficacy and student achievement within an urban school district. The main effect of teacher efficacy on student achievement was examined by comparing the student achievement of schools on the Fourth Grade Virginia Standards of Learning Reading and Mathematic Assessment to determine if there was a significant difference in the mean score between these two groups. A t-test was used as a follow-up test of simple significant main effect and interaction effect.
The correlation between all schools and overall teacher efficacy indicated a positive relationship between teacher efficacy and math scores and efficacy of instructional strategies and math scores. Moreover, the results indicated a positive relationship between overall teacher efficacy, efficacy of student engagement, and efficacy of instructional strategies and math scores. There was no relationship between efficacy levels and student achievement when just examining Non-title I Schools.
The first ANOVA indicated no statistically significant interaction between efficacy level and school type, but significant main effects for efficacy level, and school type. This test indicated the presence of significant differences in reading achievement in Title I schools. The second ANOVA indicated no significant interaction between efficacy level and school type, but significant main effect for efficacy level, and no significant main effect for school type. The t-test revealed no significant differences in top quartile and bottom quartile schools in math achievement for Title I and Non-Title I schools.
An independent sample t-test was used in order to determine whether there was a significant difference between the overall efficacy levels and efficacy levels in the three dimensions of teachers in Title I schools and Non-Title I schools. The test indicated there was no significant difference in the mean scores of Title I and Non-Title I teachers on the overall efficacy scale, nor in the three dimensions.
Descriptive statistics and pair sample t-test were used to answer questions four and five. The test indicated that Title I and Non-title I teachers scored highest in the dimension labeled efficacy for instructional strategies. There was a statistically significant difference in the mean scores of student engagement / instructional strategies and student engagement / classroom management in both Title I and Non-Title I teachers.
High levels of teaching efficacy may serve as a necessary component for teaching students who are difficult â to reachâ . Therefore it is imperative that teacher efficacy levels be considered before placing teachers in schools. It may become increasingly important for human resource to gauge a teacherâ s efficacy level during the hiring process and the placement of new teachers.
Principals must be dedicated to finding ways to increase efficacy levels in their teachers. Longitudinal studies that examine teacher efficacy levels in various teaching environments such as urban, suburban, rural, high SES, low SES, and other similar classifications would be useful.