1. Christian S. Gle
BEEd 3rd – A
QUARTILE DEVIATION
Objectives:
To present the procedures in getting the quartile deviation from ungrouped and grouped
data.
Subject Matter:
Topic:
Reference:
Materials:
Methodology:
Measures of Variability
- Quartile Deviation
Educational Measurement and Evaluation
pp. 97-99
power point presentation, chalkboard
computation
Lesson Content:
Quantiles are the extensions of the median concept because they are values which divide a set
of data into equal parts.
a. Median – divides the distribution into two equal parts.
b. Quartile – divides the distribution into four equal parts.
c. Decile – divides the distribution into ten equal parts.
d. Percentile – divides the distribution into one hundred equal parts.
Quartiles are values in a given set of distribution that divide the data into four equal parts. Each
set of scores has three quartiles. These values can be denoted by Q1, Q2 and Q3.
 First quartile - Q1 (lower quartile) – The middle number between the smallest
number and the median of the data set (25th Percentile).
 Second quartile - Q2 – The median of the data that separates the lower and upper
quartile (50th Percentile).
 Third quartile - Q3 – (upper quartile) - The middle value between the median and
the highest value of the data set (75th Percentile).
The difference between the upper and lower quartiles is called the Interquartile range.
(IQR = Q3-Q1)
Quartile deviation or Semi-interquartile range is one-half the difference between the first
and the third quartiles. (QD = Q3-Q1/2)
Getting the Quartile Deviation from Ungrouped Data
In getting the quartile deviation from ungrouped data, the following steps are used in
getting the quartiles:

2. 1. Arrange the test scores from highest to lowest.
2. Assign serial numbers to each score. The first serial number is assigned to the lowest
test score, while the last serial number is assigned to the highest test score.
3. Determine the first quartile (Q1). To be able to locate Q1, divide N by 4. Use the
obtained value in locating the serial number of the score that falls under Q1.
4. Determine the third quartile (Q3), by dividing 3N by 4. Locate the serial number
corresponding to the obtained answer. Opposite this number is the test score
corresponding to Q3.
5. Subtract Q1 from Q3 and divide the difference by 2.
Consider the test scores in the table below:
Score
17
17
26
27
30
30
31
37
=
Serial Number
1
2
3
4
5
6
7
8
=2
Q1 = (
= 21.5
=
=6
Q3 = (
N=8
= 30.5
QD = (
)
=(
)
= 4.5
Getting the Quartile Deviation from Grouped Data
In getting the quartile deviation from grouped data, the following steps are used in getting
the quartiles:
1. Cumulate the frequencies from the bottom to the top of the grouped frequency
distribution.

3. 2. For the first quartile, use the formula Q1 =L +
(i) where:
L = exact lower limit if the Q1 class
N/4 = locator of the Q1 class
N = total number of scores
CF = cumulative frequency below the Q1 class
i = class size/interval
3. For the third quartile, use the formula Q3 = L +
(i) where:
L = exact lower limit if the Q3 class
3N/4 = locator of the Q3 class
N = total number of scores
CF = cumulative frequency below the Q3 class
i = class size/interval
Example:
Computation of the Quartile Deviation for Grouped Test Scores
Classes
Frequency (f)
Cumulative Frequency (CF)
46-50
41-45
36-40
31-35
26-30
21-25
16-20
11-15
5
7
9
10
8
6
4
4
53
48
41
32
33
14
8
4
N = 53
The computational procedures for determining the quartile deviation for grouped test
scores are reflected in the above table.
For the first quartile
=
= 13. 25
CF = 8 f = 6 L = 20.5
Q1
=L+
= 20. 5 +
(i)
(5)

4. = 20. 5 +
CF = 32 f = 9 L = 35.5
(5)
Q3
= 20. 5 +
= 25.75
(i)
= 35.5 +
= 35.5 +
For the third quartile
=
=L+
(5)
(5)
= 35.5 +
= 40.5
= 40.22
After obtaining the first and third quartiles, we can now compute QD. Thus QD = (
QD
=(
=(
)
)
= 7.235 or 7.24
).