Russel-Research-Method-in-Anthropology

Bivariate Analysis: Testing Relations 623
TABLE 20.13
Computing Spearman’s Rank Order Correlation Coefficient for the Data in Table 20.12
Difference in
Hunter Rank for meat Rank for fish the ranks d 2
Alejandro 1 10 9 81
Jaime 2 9 7 49
Leonardo 3 15 12 144
Humberto 4 6 2 4
Daniel 5 7 2 4
Joel 6 12 6 36
Jorge 7 14 7 49
Timoteo 8 16 8 64
Tomás 9 5 4 16
Lucas 10 8 2 4
Guillermo 11 2 9 81
Victor 12 11 1 1
Manuel 13 13 0 0
Benjamín 14 4 10 100
Jonatán 15 3 12 144
Lorenzo 16 1 15 225
total d 2 1,002
r s 1 6(1002) 1 6012/4080 .474
16(16 2 1)
association between an interval and an ordinal variable, or between an interval
and a dummy variable. (Dummy variables are nominal variables coded as 1
or 0, present or absent. See chapter 17 on text analysis.) The square of Pearson’s
r, orr-squared, is a PRE (proportionate reduction of error) measure of
association for linear relations between interval variables. It tells us how much
better we could predict the scores of a dependent variable, if we knew the
scores of some independent variable.
Table 20.14 shows data for two interval variables for a random sample of
10 of the 50 countries in table 19.8: (1) infant mortality and (2) life expectancy
for women.
To give you an idea of where we’re going with this example, the correlation
between INFMORT and TFR across the 50 countries in table 19.8 is around
.89, and this is reflected in the sample of 10 countries for which the correlation
is r .94.
Now, suppose you had to predict the TFR for each of the 10 countries in
table 20.14 without knowing anything about the infant mortality rate for those