Russel-Research-Method-in-Anthropology

Bivariate Analysis: Testing Relations 621
Yule’s Q for these data is .80. Most of the women (135/148.91) who
come from two-parent homes are close to their fathers, compared to fewer than
half who come from one-parent homes (31/67.46). The reason is obvious:
Overwhelmingly, one-parent homes are headed by mothers, not by fathers.
In general:
What to Use for Nominal and Ordinal Variables
1. Use 2 to see how often you could expect to find the differences you see in the
table just by chance. Calculate odds ratios to measure the strength of relationships.
2. Use gamma (or tau, or—in the case of 2 2 tables—Yule’s Q) to measure the
association between two ordinal variables.
In actual practice, ordinal variables with seven ranks are treated as if they
were interval variables. In fact, many researchers treat ordinals with just five
ranks as if they were intervals, because association between interval-level variables
can be analyzed by the most powerful statistics—which brings us to correlation
and regression.
Correlation: The Powerhouse Statistic for Covariation
When at least one of the variables in a bivariate relation is interval or ratio
level, we use a measure of correlation: Spearman’s r, written r s when the data
are rank ordered; Pearson’s product moment correlation, written simply as
r, to measure the strength of linear relations; or eta squared (eta is the Greek
letter , pronounced either eat-a or ate-a) to measure the strength of certain
kinds of nonlinear relations. (Go back to the section on ‘‘shape of relations’’
at the beginning of this chapter if you have any doubts about the concept of a
nonlinear relation.)
Spearman’s r
Table 20.12 shows some data collected by Allyn Stearman (1989:224). She
measured the amount of game meat taken, over 56 days, by 16 Yuquí hunters
in the Bolivian Amazon.
These data are arranged in rank order of meat taken. So, Alejandro is the
winner, with 226 kilos of meat during the 56-day observation period. Lorenzo