Russel-Research-Method-in-Anthropology

The Foundations of Social Research 59
Indeed. If you want to measure someone’s height, use a tape measure. Don’t
substitute a lot of fuzzy proxy variables for something that’s directly measurable
by known, valid indicators. But if you want to measure things like quality
of life and socioeconomic class—things that don’t have well-understood, valid
indicators—then a complex measure will just have to do until something simpler
comes along.
The preference in science for simpler explanations and measures over more
complicated ones is called the principle of parsimony. It is also known as
Ockham’s razor, after William of Ockham (1285–1349), a medieval philosopher
who argued Pluralitas non est ponenda sine necessitate, or ‘‘don’t make
things more complicated than they need to be.’’
You can tap the power of criterion validity for complex constructs with the
known group comparison technique. If you develop a scale to measure political
ideology, you might try it out on members of the American Civil Liberties
Union and on members of the Christian Coalition of America. Members of
the ACLU should get high ‘‘left’’ scores, and members of the CCA should get
high ‘‘right’’ scores. If they don’t, there’s probably something wrong with the
scale. In other words, the known-group scores are the criteria for the validity
of your instrument.
A particularly strong form of criterion validity is predictive validity—
whether an instrument lets you predict accurately something else you’re interested
in. ‘‘Stress’’ is a complex construct. It occurs when people interpret
events as threatening to their lives. Some people interpret a bad grade on an
exam as a threat to their whole life, while others just blow it off. Now, stress
is widely thought to produce a lowered immune response and increase the
chances of getting sick. A really good measure of stress, then, ought to predict
the likelihood of getting sick.
Remember the life insurance problem? You want to predict whether someone
is likely to die in the next 365 days in order to know how much to charge
them in premiums. Age and sex tell you a lot. But if you know their weight,
whether they smoke, whether they exercise regularly, what their blood pressure
is, whether they have ever had any one of a list of diseases, and whether
they test-fly experimental aircraft for a living, then you can predict—with a
higher and higher degree of accuracy—whether they will die within the next
365 days. Each piece of data—each component of a construct you might call
‘‘lifestyle’’—adds to your ability to predict something of interest.
The Bottom Line
The bottom line on all this is that while various forms of validity can be
demonstrated, Truth, with a capital T, is never final. We are never dead sure of