0.6 Examples of ncpea connexions modules  (Page 2/17)

Teaching or administering a school designated for“school improvement,”“corrective action,”or“restructuring”is professionally embarrassing. However, since both James Coleman (1966) and Christopher Jenks (1972) found that there is often astrong correlation between family wealth and student standardized achievement test scores, punishing low-achieving schools ineconomically poor neighborhoods is questionable at best.

Coleman collected data on 600,000 children in all fifty states. He noticed that there were large differences inschool quality, and believed that this was because schools in the affluent suburbs were well financed, whereas schools in the innercities were deteriorating. The Civil Rights Act of 1964 ordered the Commissioner of Education to investigate, and Coleman was asked tohead that investigation. He predicted that it was the difference in the quality of schools that accounted for the difference in theacademic achievement of the poor and minorities.

To his surprise, he found that non-school factors, particularly family background, accounted for thedifference:

One implication stands out above all: That schools bring little influence to bear on a child’s achievement that is independent of his background and general social context;and that this very lack of an independent effect means that the inequalities imposed on children by their home, neighborhood, andpeer environment are carried along to become the inequalities with which they confront adult life at the end of school. (Coleman,1966)

A subsequent large three-year study by Christopher Jenks confirmed Coleman’s findings. (Jenks, 1972)

Additional Evidence:

Given the findings of Coleman and Jenks, here is additional evidence, utilizing data from high schools in KernCounty (California):

The correlation between these two variables is -.812 (Notice that this inverse relationship is not perfect, inwhich case the correlation would have been -1.00, but very close.) Squaring this correlation coefficient allows one to compute thecoefficient of determination. In this case the coefficient of determination is .6592 Multiplying by 100 (and rounding), thissuggests that about 66% of the variability in the achievement test scores is strongly related to family wealth. High scoring schoolshave a low percentage of students qualifying for free and reduced price meals, whereas low scoring schools have a high percentage ofstudents qualifying for free and reduced price meals.