Quartile Deviation, Coefficient, Uses

Quartile Deviation is a simple way to estimate the spread of a distribution about a measure of its central tendency (usually the mean). So, it gives you an idea about the range within which the central 50% of your sample data lies. Consequently, based on the quartile deviation, the Coefficient of Quartile Deviation can be defined, which makes it easy to compare the spread of two or more different distributions. Since both of these topics are based on the concept of quartiles, we’ll first understand how to calculate the quartiles of a dataset before working with the direct formulae.

A median divides a given dataset (which is already sorted) into two equal halves similarly, the quartiles are used to divide a given dataset into four equal halves. Therefore, logically there should be three quartiles for a given distribution, but if you think about it, the second quartile is equal to the median itself! We’ll deal with the other two quartiles in this section.

• The first quartileor the lower quartile or the 25th percentile, also denoted by Q₁, corresponds to the value that lies halfway between the median and the lowest value in the distribution (when it is already sorted in the ascending order). Hence, it marks the region which encloses 25% of the initial data.

• Similarly, the third quartileor the upper quartile or 75th percentile, also denoted by Q3, corresponds to the value that lies halfway between the median and the highest value in the distribution (when it is already sorted in the ascending order). It, therefore, marks the region which encloses the 75% of the initial data or 25% of the end data.

For a better understanding, look at the representation below for a Gaussian Distribution:

Formally, the Quartile Deviation is equal to the half of the Inter-Quartile Range and thus we can write it as:

Qd  = (Q3–Q1) / 2

Therefore, we also call it the Semi Inter-Quartile Range.

• The Quartile Deviation doesn’t take into account the extreme points of the distribution. Thus, the dispersion or the spread of only the central 50% data is considered.

• If the scale of the data is changed, the Qd also changes in the same ratio.

• It is the best measure of dispersion for open-ended systems (which have open-ended extreme ranges).

• Also, it is less affected by sampling fluctuations in the dataset as compared to the range (another measure of dispersion).

• Since it is solely dependent on the central values in the distribution, if in any experiment, these values are abnormal or inaccurate, the result would be affected drastically.

Coefficient of Quartile Deviation

Based on the quartiles, a relative measure of dispersion, known as the Coefficient of Quartile Deviation, can be defined for any distribution. It is formally defined as:

Coefficient of Quartile Deviation = {(Q3–Q1) / (Q3+Q1)} × 100

Since it involves a ratio of two quantities of the same dimensions, it is unit-less. Thus, it can act as a suitable parameter for comparing two or more different datasets which may or may not involve quantities with the same dimensions.

Uses of Quartile Deviation:

• Assessing Data Spread:

Quartile deviation helps to understand the variability in a dataset by focusing on the middle 50% of the data. It is less affected by outliers and extreme values compared to other measures like range, making it useful for assessing the spread in data that may contain outliers.

• Comparing Variability:

It allows for the comparison of variability between different datasets or groups. For example, in educational research, quartile deviation can be used to compare the spread of test scores between different classes or schools, providing insights into the consistency of performance.

• Descriptive Statistics:

Quartile deviation is often used in descriptive statistics to summarize data. It is a straightforward way to convey the dispersion of data around the median, providing a clear picture of central tendency and spread without needing complex calculations.

• Financial Analysis:

In finance, quartile deviation can be applied to assess the variability of returns on investment portfolios. It helps investors understand the risk associated with different investments by focusing on the variability of returns in the middle range.

• Quality Control:

In quality control and manufacturing, quartile deviation can be used to measure consistency in production processes. By analyzing the spread of product measurements around the median, companies can identify variations and improve quality control procedures.