Russel-Research-Method-in-Anthropology

Univariate Analysis 551
But if you actually code the data into ordinal chunks to begin with, you can
never go back.
Here’s a concrete example of something that’s a little more complex than
age. Gene Shelley studied the strength of ties between friends and acquaintances
(Shelley et al. 1990). Every other day for a month, she called 20 informants
on the phone to talk about things they’d learned in the previous two
days about their friends and acquaintances. People mentioned things like ‘‘Soand-so
told me she was pregnant,’’ ‘‘So-and-so’s father called and told me my
friend made his first jump in parachute school,’’ and so on. Shelley asked people
to estimate how long it had been between the time something happened to
one of their friends/acquaintances and the time they (the informants) heard
about it. This estimated time was the major dependent variable in the research.
There were 20 informants, who submitted to 15 interviews each, and in
each interview almost every informant was able to name several events of
interest. Thus, there were over 1,000 data records (one for each event remembered
by an informant). The length of time estimated by informants between
an event happening to someone they knew and their hearing about it ranged
from ‘‘immediately,’’ to ‘‘10 years,’’ with dozens of different time periods in
between (‘‘about 5 minutes,’’ ‘‘two and a half months,’’ etc.).
The temptation was to make up about five codes, like 1 5 minutes or
less, 2 6 minutes to 19 minutes, 3 20 minutes to an hour, and so on. But
how do you decide what the right breaks are? Shelley decided to code everything
in days or fractions of days (1 minute is .0007 days; 10 years is 3,650
days) without worrying about leap years (ibid.). Shelley didn’t throw away
data by turning a ratio-level variable (minutes) into an ordinal variable (arbitrary
chunks of time).
Here’s another example, using a nominal variable. Suppose you are studying
the personal histories of 200 Mexican men who have had experience as
illegal labor migrants to the United States. If you ask them to name the towns
in which they have worked, you might get a list of 300 communities—100
more than you have informants! The temptation would be to collapse the list
of 300 communities into a shorter list, using some kind of scheme. You might
code them as Southeast, Southwest, California, Midwest, Northwest, mid-
Atlantic, and so on. Once again, you’d be making the error of doing your analysis
in the coding.
Once you’ve got all the data entered into a computer, you can print them,
lay them out, stare at them, and start making some decisions about how to
‘‘package’’ them for statistical analysis. You might decide to label each of the
300 communities in the list according to its population size; or according to
its ethnic and racial composition (more than 20% Spanish surname, for example);
or its distance in kilometers from the Mexican-U.S. border. All those