Russel-Research-Method-in-Anthropology

Multivariate Analysis 695
just because you think they might come in handy. Of course, you can overdo it.
There is nothing more tedious than an interview that drones on for hours without
any obvious point other than that the researcher is gathering data on as many
variables as possible.
3. The source of ideas has no necessary effect on their usefulness. You can get ideas
from an existing theory or from browsing through data tables—or from talking
about research problems with your friends. The important thing is not how you
get a hunch, it’s whether you can test your hunches and create plausible explanations
for whatever findings come out of those tests. If others disagree with your
explanations, then let them demonstrate that you are wrong, either by reanalyzing
your data or by producing new data. But stumbling onto a significant relation
between some variables does nothing to invalidate the relation.
So, when you design your research, try to think about the kinds of variables
that might be useful in testing your hunches. Use the principles in chapter 5
and consider internal state variables (e.g., attitudes, values, beliefs); external
state variables (e.g., age, height, gender, race, health status, occupation, wealth
status); physical and cultural environmental variables (e.g., rainfall, socioeconomic
class of a neighborhood); and time or space variables (Have attitudes
changed over time? Do the people in one community behave differently from
those in another otherwise similar community?).
In applied research, important variables are the ones that let you target a
policy—that is, focus intervention efforts on subpopulations of interest (the
rural elderly, victims of violent crime, overachieving third graders, etc.)—or
that are more amenable to policy manipulation (knowledge is far more manipulable
than attitudes or behavior, for example). No matter what the purposes
of your research, or how you design it, the two principle rules of data analysis
are:
1. If you have an idea, test it.
2. You can’t test it if you don’t have data on it.
So, don’t be afraid to play and have a good time with data analysis. If you
hang around people who use complex statistical tools in their research, you’ll
hear them talking about ‘‘massaging’’ their data, ‘‘teasing out signals’’ from
their data, and ‘‘separating the signals from the noise.’’ These are not the sorts
of phrases used by people who are bored with what they’re doing.
Enjoy.