Russel-Research-Method-in-Anthropology

550 Chapter 19
independent variables. That’s the bivariate part. Finally, you’d look at the
simultaneous effect of the independent variables on the dependent variable or
variables. That’s the multivariate part.
Each part helps us answer questions about how things work.
Coding and Codebooks for Quantitative Data
Quantitative data processing depends crucially on having a useful codebook.
A codebook for quantitative data spells out exactly how to transform
observations into numbers that can be manipulated statistically and searched
for patterns.
A good codebook is worth a lot in data analysis and it’s worth more every
year. It tells you (and others) what you have in your data—what variables
you’ve studied, what you’ve called those variables, and how you’ve stored
information about them. You simply can’t analyze quantitative data without a
good, clear codebook.
Just as important, neither can anyone else. You can’t share your data with
other researchers unless you give them a codebook they can use. Six months
after you finish anything but the simplest projects (those with only half a
dozen or fewer variables), even you won’t recognize your own data without a
codebook. And if you want to reanalyze your data several years after a project
has ended, or compare data from 2002 with current data, you won’t be able to
do so unless you have built and filed away a good codebook.
Coding
The first rule for coding quantitative data is: Don’t analyze while you’re
coding. This rule is the exact opposite of the rule that applies to coding qualitative
data. Coding text is analysis—thinking about what each piece of text
means, developing hypotheses about the people who are described, boiling the
text down to a series of mnemonics.
It’s different with a set of numbers. Suppose you ask 400 randomly selected
people, aged 20–70, how old they are. You could get as many as 51 different
ages, and you’ll probably get at least 20 different ages.
I’ve seen many researchers code this kind of data into four or five categories—such
as 20–29, 30–39, 40–49, 50 and older—before seeing what
they’ve got. Recall from chapter 2 that this just throws away the interval-level
power of data about age. You can always tell the computer to package data
about age (or income, or any interval variable) into a set of ordinal chunks.