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# Interval Data: Definition, Examples, and Analysis

Published by at September 2nd, 2021 , Revised On February 9, 2023

There are a total of four types of data in statistics primarily. They are nominal data, ordinal data, ratio data, and interval data.

Ordinal Data: This type of data is classified into categories. However, the distance between these categories is unknown.

Nominal Data: Nominal data is used to label variables without assigning any quantitative value to them.

Ratio Data: This is a kind of qualitative data that measures variables on a continuous scale.

Interval Data: This data type is measured along a scale and has an equal distance between its values.

For now, let’s take a deeper look at Interval Data and its analysis.

## What is an Interval Data?

Interval data plays an essential role in statistical and market research.

Also known as an integer, interval data is a type of data measured along a scale in which each value is placed at an equal distance from its subsequent value. It is when two points are placed with an equal distance between them.

For example, time is interval data because its values are categorical, equal, and constant. The difference between 1 pm and 3 pm is the same as 4 pm and 6 pm. Similarly, the difference between 10 degrees Celsius and 20 degrees Celsius is the same as 8 degrees Celsius and 18 degrees Celsius.

Interval Data does not have a ‘true zero’. This means that the value of zero holds meaning and does not denote an absence of something in our data. For example, a temperature of 0 degrees Celsius represents a temperature value.

It is essential to understand that we can only perform addition and subtraction on interval data and not multiplication or division. For example, you cannot say that 100 degrees Celsius is two times greater than 50 degrees Celsius. That would be incorrect. ## Interval Data vs Ratio Data

Both interval and ratio data have equal values placed between two variables. However, one significant difference between the two is the presence of the ‘true zero’. The ratio data has a true zero, which denotes an absence of a variable. For example, in interval data, you can measure temperature beyond 0 degrees because zero, in this case, holds a value. For this reason, you can not divide or multiply the variables.

However, in ratio data, the variables never fall beneath zero. An example of this is body weight. We know weight is measured from 0 as a starting point and goes above. It does not fall beneath zero.

## Characteristics of Interval Data

Here are some of the characteristics of interval data.

Measurement: Interval data is calculated using an interval scale. This scale not only categorically shows a difference but also has a fixed direction and order.

Point Zero: Interval scale does not have a true zero, which means that a variable can be measured beyond point zero.

Calculation: Addition and subtraction can be done with interval data. However, division and multiplication are not possible. Furthermore, all statistical analyses are possible with this type of data. This is why it is also considered vital in market and statistical research.

Interval Difference: The difference between each value on an interval scale is equal.

## How is Interval Data Collected?

There are several techniques used to gather interval data. Let’s look at some of them below.

• Interviews: In this method, participants are interviewed and data is collected from there. Such discussions are often structured, semi-structured, or unstructured. They can be either face-to-face, telephonic, panel or group, and computer-assisted.
• Questionnaires: This is a standard data collection method for qualitative data. A survey or a questionnaire is handed out to participants, and they are asked to answer a series of questions. Their answers are then recorded for qualitative analysis.

## Analysing and Interpreting Interval Data

Interval data can be analysed using two significant statistical methods. They are descriptive and inferential statistics. Let’s look at each one of them.

### Descriptive Statistics

Descriptive statistics qualitatively describes data collected from a particular population. The main methods included in interpreting interval data are:

• Mean: This is the average of the interval data set. Mean can be calculated by adding the numbers in the interval data set and dividing this total by the number of values in the set.
• Median: Median is used to determine the middle value in the data set. Since all values in the interval data are equal distance apart, the median is easy to calculate.
• Mode: This is the value that is the most occurring in the data set.
• Standard Deviation: This measures the dispersion of a data set relative to its mean.
• Variance: This measures the spread from the mean.
• Range: This is the difference between the highest and the lowest value in the data set.

### Inferential Statistics

In this type of statistics, you take data from a sample and generalise it to a larger population. In simple words, you make inferences on a large population-based on your data from a sample population.

Let’s look at some of the methods used to analyse interval data using inferential statistics.

• Trend Analysis: This technique predicts a future trend based on what has happened in the past. It studies trends for a certain period, formulates a pattern, and then analysis and makes inferences about the future.
• SWOT Analysis: SWOT stands for strengths, weaknesses, opportunities, and threats. It is used to analyse a company’s position in the market, help them overcome their weaknesses and improve their overall performance by making effective business strategies.
• Conjoint Analysis: This is a statistical technique used in market research and analyses how the consumers measure the value of a product and make purchase choices. It takes into notice all the factors that consumers decide on. For example, when buying a car, a consumer will consider the price, specifications, mileage, colour etc.
• TURF Analysis: This analysis is used in marketing and studies the potential of several products or services in the market. It stands for Total, Unduplicated, Reach, and Frequency.

Interval Data is a widely used form of analysing data. It is used in several domains such as:
● Marketing
● Medicine
● Education
● Product Development

Nominal Scale
● Interval Scale
Ratio Scale
Ordinal Scale

Here are some of the common examples of interval data:
● Temperature
● Time
● Test scores, i.e. SAT score, which has a score range from 400-1600 Ingram is a dissertation specialist. He has a master's degree in data sciences. His research work aims to compare the various types of research methods used among academicians and researchers.