Russel-Research-Method-in-Anthropology

116 Chapter 5
intervention is, the extreme scores are likely to become more moderate just
because there’s nowhere else for them to go.
If men who are taller than 67 marry women who are taller than 63, then
their children are likely to be (1) taller than average and (2) closer to average
height than either of their parents are. There are two independent variables
(the height of each of the parents) and one dependent variable (the height of
the children). We expect the dependent variable to ‘‘regress toward the mean,’’
since it really can’t get more extreme than the height of the parents.
I put that phrase ‘‘regress toward the mean’’ in quotes because it’s easy to
misinterpret this phenomenon—to think that the ‘‘regressing’’ toward the
mean of an dependent variable is caused by the extreme scores on the independent
variables. It isn’t, and here’s how you can tell that it isn’t: Very, very tall
children are likely to have parents whose height is more like the mean. One
thing we know for sure is that the height of children doesn’t cause the height
of their parents. Regression to the mean is a statistical phenomenon—it happens
in the aggregate and is not something that happens to individuals.
Many social intervention programs make the mistake of using people with
extreme values on dependent variables as subjects. Run some irrigation canals
through the most destitute villages in a region of a Third World country and
watch the average income of villagers rise. But understand that income might
have risen anyway, if you’d done nothing, because it couldn’t have gone down.
Test a reading program on the kids in a school district who score in the bottom
10% of all kids on reading skills and watch their test scores rise. But understand
that their scores might have risen anyway.
5. Selection of Participants
Selection bias in choosing participants is a major confound to validity. In
true experiments, you assign participants at random, from a single population,
to treatment groups and control groups. This distributes any differences
among individuals in the population throughout the groups, making the groups
equivalent.
This reduces the possibility that differences among the groups will cause
differences in outcomes on the dependent variables. Random assignment in
true experiments, in other words, maximizes the chance for valid outcomes—
outcomes that are not clobbered by hidden factors.
In natural experiments, however, we have no control over assignment of
individuals to groups.
Question: Do victims of violent crime have less stable marriages than persons
who have not been victims? Obviously, researchers cannot randomly
assign participants to the treatment (violent crime). It could turn out that peo-