Russel-Research-Method-in-Anthropology

590 Chapter 19
TABLE 19.12
z-Scores of the Data on FEMILLIT in Table 19.7
COUNTRY x x z-score
Armenia 2.4 19.49 17.09 0.8212
Chad 66.0 19.49 46.51 2.2349
El Salvador 23.8 19.49 4.31 0.2071
Ghana 37.1 19.49 17.61 0.8462
Iran 30.1 19.49 10.61 0.5100
Latvia 0.2 19.49 19.29 0.9270
Namibia 18.8 19.49 0.69 0.0332
Panama 8.7 19.49 10.79 0.5185
Slovenia 0.4 19.49 19.09 0.9173
Suriname 7.4 19.49 12.09 0.5810
Why Use Standard Scores?
There are several advantages to using standard scores rather than raw
scores. First of all, while raw scores are always in specialized units (percentages
of people, kilos of meat, hours of time, etc.), standard scores measure the
difference, in standard deviations, between a raw score and the mean of the
set of scores. A z-score close to 0 means that the raw score was close to the
average. A z-score that is close to plus-or-minus 1 means that the raw score
was about one standard deviation from the mean, and so on.
What this means, in practice, is that when you standardize a set of scores,
you create a scale that lets you make comparisons within chunks of your data.
For example, we see from table 19.12 that the female illiteracy rates for
Namibia and Ghana are 18.8% and 37.1%, respectively. One of these raw
numbers is about double the other. Similarly, the female illiteracy rate for El
Salvador is almost three times that of Panama (see table 19.8).
These raw numbers tell us something, but the z-scores tell us more: The
percentage of female illiteracy in Namibia is close to the mean for this group
of countries, but the percentage for Ghana is almost 1 sd (.85) above the mean.
The percentage for El Salvador is about .2 sd above the mean, and the percentage
for Slovenia is almost 1 sd (.92) below the mean.
A second advantage of standard scores over raw measurements is that standard
scores are independent of the units in which the original measurements
are made. This means that you can compare the relative position of cases
across different variables.
Medical anthropologists measure variables called ‘‘weight-for-length’’ and
‘‘length-for-age’’ in the study of nutritional status of infants across cultures.
Linda Hodge and Darna Dufour (1991) studied the growth and development