Rothstein on NY test data

[kber <kber@EARTHLINK.NET>]

in today's NY Times

Ken Bernstein

March 29, 2002

Fuzzy Data on Race

By RICHARD ROTHSTEIN

      ew York State's new data that show school test scores by race and
income give a useful picture of the yawning
      achievement gap between black and white pupils. But the data are
too crude to accomplish a larger goal: identifying
      problem schools as a step toward improving them.

Compiling separate test results for different races and income groups,
part of a new federal education law, is intended to
serve two purposes. First, because learning occurs in families as well
as schools, test data for race and income groups
permit fairer comparisons. For example, children whose families can
afford housing with quiet space, for study, or who
have many books at home, will often have higher scores than low-income
students, even if their schools are equally good.

The second purpose of breaking down scores demographically is to throw a
spotlight on schools that systematically leave
some children behind. A school can have good average scores if its white
pupils score higher than similar pupils elsewhere,
even if its minority pupils score lower than similar pupils elsewhere.
The new data should help identify schools like these,
schools that ignore disadvantaged children by hiding behind scores of
those who are better off.

Such reporting is part of President Bush's education plan, and New York
is carrying it out. But its reach is farther than its
grasp. The new data are too imprecise to permit pure comparisons of
school quality. To avoid comparing schools that are
only superficially similar, policy makers will have to supplement the
data with more expensive and nuanced observations.

Consider the family income breakdown. New York's data separate students
who participate in the federal lunch program
from those who do not. But the program's cutoff ? family income equal to
185 percent of the poverty line ? is so high
that it cannot distinguish severely disadvantaged students from those
without great hardship.

In New York, roughly 20 percent of children are from poor families,
whose incomes are below about $18,000 for a family
of four. Such children, many of whom are ill-housed, malnourished and
from troubled homes, must overcome much
greater obstacles to learning than those experienced by children at the
top of the low-income range, with incomes of about
$33,000.

Policy makers would err if they concluded that a school filled with
working-class children outperformed one with poor
children simply because it had higher scores, even though pupils in both
places got subsidized lunches.

Another problem with using lunch data to assess disadvantage is its
inaccuracy, particularly for whites. Poor white families
are more likely to be temporarily poor, from a spate of bad luck. Poor
black families are more likely to have been poor for a
long time.

Because schools are not able to monitor families' movements in and out
of poverty, white children who were previously
poor often continue to receive lunch subsidies. This happens less often
with black children whose poverty is longer-term.
So whites are more likely to be economically secure than blacks, even
when lunch eligibility is the same.

Even with better income data, racial breakdowns can be misleading. Most
people think black and white children should
perform similarly if their family incomes are similar. Educators are
puzzled by the fact that on average, whites score higher
than blacks even if their family incomes are the same.

But income alone is a poor proxy for social class. Black middle-income
parents have lower socioeconomic status on
average than white middle-income parents, and this affects children's
achievement.

Because blacks are, over all, poorer than whites, middle-class blacks
are more likely to have poor siblings and parents than
whites earning the same income. More often, middle-class blacks help to
support struggling relatives, and this leaves less
income to devote to children for summer camp, better housing or other
spending that aids achievement.

Black professionals are also more likely to be first in their families
to achieve middle-class status. For young white adults,
home ownership is often spurred by parents who help with down payments.
First-generation middle-class families cannot
get such help. So it is not surprising that black home ownership is
lower than whites', even at the same income levels. This
leaves many black middle-class children living in more distressed
neighborhoods than whites whose parents earn the same.

Family structure also affects learning. In patterns that originated in
the forced breakup of families during slavery, black
children are less likely to be raised in traditional nuclear families
than whites. Consider a black child raised, in part, by a
grandparent. Even if the child's mother graduated from high school or
college, the grandmother is less likely to have done
so than a white child's grandmother.

While grandparents may provide nurturing support, children partly raised
by less-educated grandparents will be exposed to
less complex grammar and vocabulary than children raised only by
college-educated parents. This also leads to more
difficulty in school for black than for white children, even when both
sets of parents are educated and have middle-class
incomes.

In many schools, black children's poor performance may have nothing to
do with these demographic confoundings, but
result from unfocused school leadership, less adequate teachers, less
access to challenging curriculum, poor disciplinary
climate or placement in separate classes that warehouse low achievers.
Data alone can't tell where failure results from such
school practices that are amenable to reform.

So New York's new data can only be a warning, and may be misleading. To
judge schools accurately, officials would have
to send teams of knowledgeable outsiders to observe teachers, examine
student work and determine if a school's
instruction and disciplinary climate are likely to stimulate the highest
possible learning in each group, regardless of home
advantage.

Analyzing test data is a first step, but only a first step, toward
making such determinations.


                         Copyright 2002 The New York Times Company |
Privacy Information

--------------------------------------------------------------------------
To unsubscribe from the ARN-L list, send command SIGNOFF ARN-L
to LISTSERV@LISTS.CUA.EDU.