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Variability: An Introduction
Dr Rajesh Verma
Asst. Prof (Psychology)
Govt. College Adampur, Hisar, Haryana
Case
Two psychology professors assesses students’
assignment with average 38 marks (out of 50). Does this
indicate that both teachers have same evaluation
temperament (assuming such may be disastrous!). Let us
consider
(i) teacher A awards within 34 to 40 marks,
(ii) while teacher B awards within 20 to 48 marks.
If you are a student and seeking
to get your assignment assessed which
teacher you will prefer??
If you are puzzled do not worry,
study of ‘variability’ will help you.
Meaning
Variability means the ability to vary. It has come
from word ‘variable’ which means
‘not staying the same’ or ‘often
changing’. A situation, quantity, or
number which is not constant or that
can vary.
For example, ‘memory’ and
‘intelligence’ are variables.
Definition
An index of scattering of scores around the mean
(center of distribution) is called variability. It is also
known as Spread, Dispersion or Width (Levin & Fox,
2006).
Variability measures how much the scores vary
from each other.
Introduction
Nature has complexity as well as simplicity in
its creations and follows a pattern. The complexity in
the nature inherently occurs due to inter and intra-
creations ‘variability’. For example; two rose flowers
grown on the same plant are similar yet different.
Two chapattis prepared by same cook under similar
conditions are similar yet different. Even different
coloured flowers grow
on same plant.
Understanding with Figures
Now it’s time for you to tell, out of
A & B which figure have higher
variability/
spread/
dispersion in
terms of size of
the circles.
Understanding with Graph
In this figure the data represented
by B has higher
dispersion than
data represented
by A. It shows
B has higher
variability in its
data set
than A.
Measures of Variability
1. Range,
2. Interquartile range,
3. Mean deviation,
4. Variance, and
5. Standard
deviation.
1. Range – The difference between
the highest and lowest score in a data
set.
For example – In the given data set
lowest score is 4 and highest score is
30 therefore, 30-4 = 26, So, range is
26. So simple isn’t it?
Data Set
4
21
14
8
25
30
11
20
19
26
10
2. Interquartile Range – Range of middle 50% values
of a data set. Or distance between the first and third
quartile i.e. IQR = Q3-Q1
3. Mean Deviation – Average of absolute differences
of raw score from there mean.
MD =
∑|𝒙− 𝒙|
𝑵
Where, ∑ = Summation
x = score
𝒙 = mean
N = no. of score
| = Absolute
4. Variance (s) – It is the average of the squared
differences of each raw score from the mean.
It is denoted by ‘s’. 𝒔 𝟐
=
∑(𝒙− 𝒙) 𝟐
𝑵
5. Standard Deviation (σ) – The square root of
variance. It represents the average variability in a data
set because it measures the average of deviations from
the mean. σ =
∑(𝒙− 𝒙) 𝟐
𝑵
Where, ∑ = Summation
x = score
𝒙 = mean
N = no. of score
s = variance
References:
1. Levin, J & Fox, J. A. (2006). Elementary statistics
in in social research. Delhi: Dorling Kindersley (India).
2. http://sphweb.bumc.bu.edu
/otlt/mph-modules/bs/bs704_
summarizingdata/bs704_
summarizingdata7.html.
vermasujit@yahoo.com
Next
Discussion
Quartile Deviation

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variability an introduction

  • 1. Variability: An Introduction Dr Rajesh Verma Asst. Prof (Psychology) Govt. College Adampur, Hisar, Haryana
  • 2. Case Two psychology professors assesses students’ assignment with average 38 marks (out of 50). Does this indicate that both teachers have same evaluation temperament (assuming such may be disastrous!). Let us consider (i) teacher A awards within 34 to 40 marks, (ii) while teacher B awards within 20 to 48 marks. If you are a student and seeking to get your assignment assessed which teacher you will prefer?? If you are puzzled do not worry, study of ‘variability’ will help you.
  • 3. Meaning Variability means the ability to vary. It has come from word ‘variable’ which means ‘not staying the same’ or ‘often changing’. A situation, quantity, or number which is not constant or that can vary. For example, ‘memory’ and ‘intelligence’ are variables.
  • 4. Definition An index of scattering of scores around the mean (center of distribution) is called variability. It is also known as Spread, Dispersion or Width (Levin & Fox, 2006). Variability measures how much the scores vary from each other.
  • 5. Introduction Nature has complexity as well as simplicity in its creations and follows a pattern. The complexity in the nature inherently occurs due to inter and intra- creations ‘variability’. For example; two rose flowers grown on the same plant are similar yet different. Two chapattis prepared by same cook under similar conditions are similar yet different. Even different coloured flowers grow on same plant.
  • 6. Understanding with Figures Now it’s time for you to tell, out of A & B which figure have higher variability/ spread/ dispersion in terms of size of the circles.
  • 7. Understanding with Graph In this figure the data represented by B has higher dispersion than data represented by A. It shows B has higher variability in its data set than A.
  • 8. Measures of Variability 1. Range, 2. Interquartile range, 3. Mean deviation, 4. Variance, and 5. Standard deviation.
  • 9. 1. Range – The difference between the highest and lowest score in a data set. For example – In the given data set lowest score is 4 and highest score is 30 therefore, 30-4 = 26, So, range is 26. So simple isn’t it? Data Set 4 21 14 8 25 30 11 20 19 26 10
  • 10. 2. Interquartile Range – Range of middle 50% values of a data set. Or distance between the first and third quartile i.e. IQR = Q3-Q1 3. Mean Deviation – Average of absolute differences of raw score from there mean. MD = ∑|𝒙− 𝒙| 𝑵 Where, ∑ = Summation x = score 𝒙 = mean N = no. of score | = Absolute
  • 11. 4. Variance (s) – It is the average of the squared differences of each raw score from the mean. It is denoted by ‘s’. 𝒔 𝟐 = ∑(𝒙− 𝒙) 𝟐 𝑵 5. Standard Deviation (σ) – The square root of variance. It represents the average variability in a data set because it measures the average of deviations from the mean. σ = ∑(𝒙− 𝒙) 𝟐 𝑵 Where, ∑ = Summation x = score 𝒙 = mean N = no. of score s = variance
  • 12. References: 1. Levin, J & Fox, J. A. (2006). Elementary statistics in in social research. Delhi: Dorling Kindersley (India). 2. http://sphweb.bumc.bu.edu /otlt/mph-modules/bs/bs704_ summarizingdata/bs704_ summarizingdata7.html.