A part of our group presentation way back 4th year

1. 5.2.1 The Range

2. Of all the measures of variability, the range is
the easiest and quickest way to determine. It
is simply the difference of the highest (H)
and the lowest (L) scores in a set of data
under consideration.
Formula for the Range:
r = H – L
where: r = range
H = highest score
L = lowest score

3. Example:
The scores of Maria in her math quizzes are as
follows: 12, 25, 27, 29, 36, 38, 40, 43, 50, and 62.
Find its range.
Solution:
Highest score (H) = 62 Lowest score (L) = 12
r = H – L
= 62 – 12
= 50
Therefore, the range is 50.

4. 5.2.1.1 Other
Characteristics of the
Range

5. Advantages
 Gives quick approximation of the variability
of data.
 It is not very sophisticated/complicated.
 Used when the mode is preferred measure of
central tendency. (i.e./ when you have
nominal/titular level data.)
 It is the simplest measure of variability/dispersion.

6. Disadvantages
 It is limited/partial, if extreme scores are not
representative of the sample, but are included
among the scores.
 It is not very informative, because it is based only
on the most extreme scores.
 It is severely affected by extreme scores in your
data distribution.

7. 5.2.2 The Average
Deviation

8. The average deviation is the “average”
amount each score deviates/differs from the
mean. It is always zero, as a consequence of
one of the properties of the mean (that the sum of
the deviations about the mean is zero ). It
cannot be calculated without first calculating
the mean.
Consequently, the mean deviation is usually
expressed as the mean of the absolute values of
the deviations from the mean. That is why it is
also called the Mean Absolute Deviation
(M.A.D.).

9. 5.2.3.3 Characteristics of the
Inter-Quartile Range of the
Quartile Deviation

10. 1. It is less likely influenced by extreme scores,
therefore, giving a better and stable measure of
variability than the range.
2. It discards/removes too much of the data. It does
not give a complete picture of the variability.
3. The mean and mean absolute deviation are both
affected by outliners and may not be good
representations of the data in cases where
outliners are alike. In these cases, the median and
the interquartile range/Q.D. may be a better
choice.

11. 4. The IQR is a nice measure to use if one wishes to
do short numerical description of a data set. It is
resistant to the effects of outliners, but it has the
significant disadvantage of not being easily
analyzed. One of the uses of the IQR is in the
identification of outliners.
5.The Semi-Interquartile Range or the Q.D. is used
when the median is the referred measure of the
central tendency (i.e., with ordinal level or
skewed data)