What are the Measures and Importance of Relative Position in Measuring Data in Research

Relative position measures a value that is relative to other values in a data distribution. The most common measures of position are percentiles, quartiles, and standard scores or z-scores.Measures of position helps to identify where a value falls in a data set. It lets us knowwhether a value is average, or unusually low or high. A measure of position determines the position of a single value in relation to other values in a sample. 

Measures of Relative Position

There are three measures of relative position: Percentiles, Quartiles, z-Scores.


The items in a data distribution are arranged from ascending to descending order. The values that divides the items into 100 equal parts are called percentiles. The formula of percentiles is:

Percentile of  X= number  of  data  points  less  than X ×100

                                  Total  number  of  data  points


Quartiles divides a data set into four equal parts. The values dividing each part are called the first, second, and third quartiles having the symbols Q1, Q2, and Q3 respectively.

Standard Scores (z-Scores)

A standard score or a  z-score shows how many standard derivations an item is from the mean. The formula of standard score:

z = (X – μ) / σ

where z represents the z-score, X is the data value, μ is the mean of the data set, and σ is the standard deviation.

Importance of Relative Position in Measuring Data 

i. Allows us to know where certain value lies in a data distribution. 

ii. Helps to find if a value is average, unusually high or low. 

iii. It is useful for quantitative data on a numerical scale.