Measure of Dispersion

Measures of Dispersion

Greg C Elvers, Ph.D.

1

Definition
Measures of dispersion are descriptive
statistics that describe how similar a set of
scores are to each other
The more similar the scores are to each other,
the lower the measure of dispersion will be
The less similar the scores are to each other, the
higher the measure of dispersion will be
In general, the more spread out a distribution is,
the larger the measure of dispersion will be
2

Which of the
distributions of scores 125
has the larger 100
75
dispersion? 50
25
The upper distribution 0
1 2 3 4 5 6 7 8 9 10
125
100
That is, they are less 75
50
similar to each other 25
0
1 2 3 4 5 6 7 8 9 10
3

There are three main measures of
dispersion:
The range
The semi-interquartile range (SIR)
Variance / standard deviation

4

The Range
The range is defined as the difference
between the largest score in the set of data
and the smallest score in the set of data, X L -
XS
What is the range of the following data:
4 8 1 6 6 2 9 3 6 9
The largest score (XL) is 9; the smallest
score (XS) is 1; the range is XL - XS = 9 - 1 =
5
8

When To Use the Range
The range is used when
you have ordinal data or
you are presenting your results to people with
little or no knowledge of statistics
The range is rarely used in scientific work
as it is fairly insensitive
It depends on only two scores in the set of data,
XL and XS
Two very different sets of data can have the
same range:
1 1 1 1 9 vs 1 3 5 7 9 6

The Semi-Interquartile Range
The semi-interquartile range (or SIR) is
defined as the difference of the first and
third quartiles divided by two
The first quartile is the 25th percentile
The third quartile is the 75th percentile
SIR = (Q3 - Q1) / 2

7

SIR Example
What is the SIR for the 2
data to the right? 4
← 5 = 25th %tile
25 % of the scores are 6
below 5 8
5 is the first quartile 10
25 % of the scores are 12
above 25 14
25 is the third quartile
20
SIR = (Q3 - Q1) / 2 = (25 - ← 25 = 75th %tile
30
5) / 2 = 10 60 8

When To Use the SIR
The SIR is often used with skewed data as it
is insensitive to the extreme scores

9

Variance
Variance is defined as the average of the
square deviations:
∑ ( X − µ) 2
σ2 =
N

10

What Does the Variance Formula
Mean?
First, it says to subtract the mean from each
of the scores
This difference is called a deviate or a deviation
score
The deviate tells us how far a given score is
from the typical, or average, score
Thus, the deviate is a measure of dispersion for
a given score

11

Mean?
Why can’t we simply take the average of
the deviates? That is, why isn’t variance
defined as:
σ 2
≠
∑ ( X − µ)
N

This is not the
formula for
variance!

12

Mean?
One of the definitions of the mean was that
it always made the sum of the scores minus
the mean equal to 0
Thus, the average of the deviates must be 0
since the sum of the deviates must equal 0
To avoid this problem, statisticians square
the deviate score prior to averaging them
Squaring the deviate score makes all the
squared scores positive 13

Mean?
Variance is the mean of the squared
deviation scores
The larger the variance is, the more the
scores deviate, on average, away from the
mean
The smaller the variance is, the less the
scores deviate, on average, from the mean

14

Standard Deviation
When the deviate scores are squared in variance,
their unit of measure is squared as well
E.g. If people’s weights are measured in pounds,
then the variance of the weights would be expressed
in pounds2 (or squared pounds)
Since squared units of measure are often
awkward to deal with, the square root of variance
is often used instead
The standard deviation is the square root of variance
15

Standard Deviation
Standard deviation = √variance
Variance = standard deviation2

16

Computational Formula
When calculating variance, it is often easier to use
a computational formula which is algebraically
equivalent to the definitional formula:

( ∑ X) 2

∑X ∑( X −µ)
2
− 2

N
σ
2
= =
N N

σ2 is the population variance, X is a score, µ is the
population mean, and N is the number of scores
17

Computational Formula Example
X X2 X-µ (X-µ2
)
9 81 2 4
8 64 1 1
6 36 -1 1
5 25 -2 4
8 64 1 1
6 36 -1 1
Σ 42
= Σ 306
= Σ 0
= Σ 12
=

18

Computational Formula Example
( ∑ X) 2

∑X ∑( X −µ)
2 2
−
N
σ
2
σ =
2
=
N N
2
12
306 − 42 =
= 6 6
6 =2
306 − 294
=
6
12
=
6
=2

19

Variance of a Sample
Because the sample mean is not a perfect estimate
of the population mean, the formula for the
variance of a sample is slightly different from the

( )
formula for the variance of a population:
2

2 ∑ X −X
s =
N −1
s2 is the sample variance, X is a score, X is the
sample mean, and N is the number of scores
20

Measure of Skew
Skew is a measure of symmetry in the
distribution of scores
Normal
(skew = 0)

Positive Negative Skew
Skew

21

Measure of Skew
The following formula can be used to
determine skew:
(
∑ X− X )
3

3 N
s =
(
∑ X− X )
2

N

22

Measure of Skew
If s3 < 0, then the distribution has a negative
skew
If s3 > 0 then the distribution has a positive
skew
If s3 = 0 then the distribution is symmetrical
The more different s3 is from 0, the greater
the skew in the distribution

23

Kurtosis
(Not Related to Halitosis)
Kurtosis measures whether the scores are
spread out more or less than they would be
in a normal (Gaussian) distribution
Mesokurtic
(s4 = 3)

Leptokurtic (s4 > Platykurtic (s4
3) < 3)

24

Kurtosis
When the distribution is normally
distributed, its kurtosis equals 3 and it is
said to be mesokurtic
When the distribution is less spread out than
normal, its kurtosis is greater than 3 and it is
said to be leptokurtic
When the distribution is more spread out
than normal, its kurtosis is less than 3 and it
is said to be platykurtic 25

Measure of Kurtosis
The measure of kurtosis is given by:
4
 
 
 X−X 
∑ 
∑ (X − X)
2
 
 
s4 =  
N
N

26

s2, s3, & s4
Collectively, the variance (s2), skew (s3), and
kurtosis (s4) describe the shape of the
distribution

27

Measure of Dispersion

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