Russel-Research-Method-in-Anthropology

Research Design: Experiments and Experimental Thinking 111
and ‘‘getting better vs. not getting better’’ might be ‘‘the level of improvement
in high-density lipoprotein’’ (the so-called good cholesterol).
Move this logic to agriculture: ceteris paribus (holding everything else—
like amount of sunlight, amount of water, amount of weeding—constant),
some corn plants get a new fertilizer and some don’t. Then, the dependent
variable might be the number of ears per corn stalk or the number of days it
takes for the cobs to mature, or the number of kernels per cob.
Now move this same logic to human thought and human behavior: Ceteris
paribus, people in Nairobi who take this course in AIDS awareness will report
fewer high-risk sex practices than will people who don’t take this course. Ceteris
paribus here means that people in both groups—the treatment group and
the control group—start with the same amount of reported high-risk sexual
activity.
Things get more complicated, certainly, when there are multiple independent
(or dependent) variables. You might want to test two different courses,
with different content, on people who come from three different tribal backgrounds.
But the underlying logic for setting up experiments and for analyzing
the results is the same across the sciences. When it comes to experiments,
everything starts with a clear hypothesis.
Step 2.
You need at least two groups, called the treatment group (or the intervention
group or the stimulus group) and the control group. One group gets
the intervention (the new drug, the new teaching program) and the other group
doesn’t. The treatment group (or groups) and the control group(s) are involved
in different experimental conditions.
In true experiments, people are randomly assigned to either the intervention
group or to the control group. This ensures that any differences between
the groups is the consequence of chance and not of systematic bias. Some
people in a population may be more religious, or more wealthy, or less sickly,
or more prejudiced than others, but random assignment ensures that those
traits are randomly distributed through all the groups in an experiment.
Random assignment doesn’t eliminate selection bias. It makes differences
between experimental conditions (groups) due solely to chance by taking the
decision of who goes in what group out of your hands. The principle behind
random assignment will become clearer after you work through chapter 6 on
probability sampling, but the bottom line is this: Whenever you can assign
participants randomly in an experiment, do it.
Step 3.
One or both groups are measured on one or more dependent variables. This
is called the pretest.