Module 57
Measures of Variability
A-11
The fact that a range is simple to calculate is about its only virtue. The problem
with this particular measure of variability is that it is based entirely on extreme
scores, and a single score that is very different from the others in a distribution can
distort the picture of the distribution as a whole. For example, the addition of a score
of 20 to the test score distribution we are considering would almost double the range
even though the variability of the remaining scores in the distribution would not
have changed at all.
The Standard Deviation:
Diff erences from the Mean
The most frequently used method of characterizing the variability of a distribution
of scores is the standard deviation. The standard deviation bears a conceptual rela-
tionship to a mean. Recall that the mean is the average score in a distribution of
scores. A
standard deviation is an index of the average deviation of a set of scores
from the center of the distribution.
Consider, for instance, the distributions in Figure 1. The distribution on the left
is widely dispersed; on the average an individual score in the distribution can be
thought of as deviating quite a bit from the center of the distribution. Certainly the
scores in the distribution on the left are going to deviate more from the center of the
distribution than those in the distribution on the right.
In contrast, in the distribution on the right, the scores are closely packed together
and there is little deviation of a typical score from the center of the distribution. On
the basis of this analysis, then, it would be expected that a good measure of vari-
ability would yield a larger value for the distribution on the left than it would for
the one on the right—and, in fact, a standard deviation would do exactly this by
indicating how far away a typical score lies from the center of the distribution.
In a normal distribution, 68% of the scores fall within one standard deviation of
the mean (34% on either side of it), 95% of the scores fall within two standard devi-
ations, and 99.7% fall within three standard deviations. In the general population, IQ
scores of intelligence fall into a normal distribution, and they have a mean of 100
and a standard deviation of 15. Consequently, an IQ score of 100 does not deviate
from the mean, whereas an IQ score that is three standard deviations above the mean
(or 145) is very unusual (higher than 99% of all IQ scores).
The calculation of the standard deviation follows the logic of calculating the dif-
ference of individual scores from the mean of the distribution (see Figure 2). Not
only does the standard deviation provide an excellent indicator of the variability of
a set of scores, it provides a means for converting initial scores on standardized tests
such as the SAT (the college admissions exam) into the scales used to report results.
In this way, it is possible to make a score of 585 on the verbal section of the SAT
exam, for example, equivalent from one year to the next even though the specifi c test
items differ from year to year.
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