Russel-Research-Method-in-Anthropology

428 Chapter 15
Thus, Johnson used a nonrandom sample of all Machiguenga households,
but he randomized the times that he visited any household in his sample. This
sampling strategy sacrificed some external validity, but it was high on internal
validity. Johnson could not claim that his sample of households statistically
represented all Machiguenga households. His 14 months of experience in the
field, however, makes his claim for the representativeness of his data credible.
That is, if Johnson’s data on time allocation in those 13 households seem
to him to reflect time allocation in Machiguenga households generally, then
they probably do. But we can’t be sure. Fortunately, randomizing his visits to
the 13 households, and making a lot of observations (3,945 of them, over 134
different days during the 14-month fieldwork period), gives Johnson’s results
a lot of internal validity. So, even if you’re skeptical of the external validity of
Johnson’s study, you could repeat it (in Shimaa or in some other Machiguenga
community) and see whether you got the same results.
Regina Smith Oboler (1985) did a TA study among the Nandi of Kenya.
She was interested in differences in the activities of adult men and women.
The Nandi, Oboler said, ‘‘conceptualize the division of labor as sex segregated.
Is this true in practice as well? Do men and women spend their time in
substantially different or similar types of activities?’’ (p. 203).
Oboler selected 11 households, comprising 117 people, for her TA study.
Her sample was not random. ‘‘Selecting a random sample,’’ she said, ‘‘even
for one kokwet (neighborhood) would have made observations impossibly difficult
in terms of travel time’’ (ibid.:204). Instead, Oboler chose a sample of
households that were matched to social and demographic characteristics of the
total population and within half an hour walking distance from the compound
where she lived.
Oboler divided the daylight hours of the week into 175 equal time periods
and gave each period (about two hours) a unique three-digit number. Then,
using a table of random numbers, she chose time periods to visit each household.
She visited each household four times a week (on different days of the
week) during 2 weeks each month and made nearly 1,500 observations on
those households during her 9 months in the field.
Oboler found that, for her sample of observations, adult men spend around
38% of their time ‘‘in activities that might reasonably be considered ‘work’
by most commonly used definitions of that term’’ (ibid.:205). Women in her
sample spent over 60% of their time working.
Table 15.2 shows the number of spot observations necessary to estimate the
frequency of an activity to within a fractional accuracy. It also tells you how
many observations you need if you want to see an activity at least once with
95% probability.
Here’s how to read the table. Suppose people spend about 5% of their time