Russel-Research-Method-in-Anthropology

Univariate Analysis 561
possible answers for the question about reducing consumption. But with a
sample of just 30 responses in table 19.2 above, we didn’t get anyone who
said they strongly disagreed with the statement that Americans are going to
have to drastically reduce their consumption in the coming years.
You can see this in the far right-hand column of table 19.3d. If all we
had were these 30 cases, we’d want to create a three-category variable—
disagree, neutral, and agree—by collapsing the data for REDUCE into (1)
the 5 people who answered 2 (disagree), (2) the 1 person who answered
3 (neutral), and (3) the 24 people who answered 4 or 5 (agree or strongly
agree).
It’s not always obvious how to group data. In fact, it’s often better not to.
Look at table 19.3c. Only two people in our sample of 30 had less than 12
years of education. We could conveniently group those two people into a category
called ‘‘less than high school.’’ There is a bulge of eight people who had
12 years of education (they completed high school), but then we see just two
people who reported a year of college and seven people who reported 2 years
of college. That bulge of seven respondents might be people who went to a
community college. We might group those two sets of people into a category
called ‘‘up to two years of college.’’
Those three people in table 19.3c who reported 4 years of college form an
obvious class (‘‘finished college’’), and so do the five people who reported
having a graduate or professional degree that required more than 4 years of
college. But what do we do with those three people who reported 3 years of
college? We could lump them together with the three respondents who finished
college, but we could also lump them with the nine people who reported
1 or 2 years of college.
The problem is, we don’t have any ironclad decision rule that tells us how
to lump data into categories. We don’t want to maintain a separate category
of just three respondents (the people who reported 3 years of college), but we
don’t know if they ‘‘belong’’ (in some socially important sense) with those
who had some college or with those who completed college.
I recommend not grouping interval-level data unless you really have to. No
matter which decision you make about those three people who reported 3
years of college in table 19.3c, you’re turning an interval-level variable (years
of education) into an ordinal-level variable (less than high school, high school,
etc.). As you saw earlier, with the variables HOWLONG and KNOW in the
Mexico City study, there are times when this is a good idea, but trading interval
for ordinal measurement means throwing away data. You need a really
good reason to do that.