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# Interval Data: Definition, Characteristics and Collection

Published by at August 31st, 2021 , Revised On July 20, 2023

Whether it is market research or any other form of economic, educational, or social research, data types are a great demand that supports most statistical transformations and tests. This demand is made in order to make better the analysis process so that a proper conclusion can be drawn.

When conducting market research and studying data types, interval data is quantitative data measured along a scale, really crucial, and must be taken into consideration. We discover that interval data is perfectly compatible with most of these statistical tests. It has distinct attributes, making it the most in-demand type compared to its counterparts.

Having that said, this blog, therefore, thoroughly discusses what interval data is and how it can be analyzed today.

## What is Interval Data?

Interval data is yet another type of data that can be calculated along a scale where every point is placed at an equal interval from another, just as the name explains itself.

It is one of the two types of discrete data. The data collected on a thermometer is an example of interval data, as its gradation markings are equally distanced from each other. Interval data is always expressed in the form of numbers, unlike ordinal data.

Moreover, arithmetic operations are all performed on this type of data. But there is a but here. These operations are bound to subtraction and addition only.

That was easy, no?

Let us now look at some of the characteristics of interval data.

## Characteristics of Interval Data

Below are some of the essential characteristics of interval data you must know about:

### 1.    Arithmetic Operation

As we just mentioned in the previous heading, one can perform arithmetic operations on this kind of data, such as subtractions and additions. You cannot, however, divide or multiply interval data. When calculating interval data, practically every statistical analysis is applicable due to its quantitative nature. This contains the mean, mode, and median but is not restricted to them.

Even if a variable is negative, it can be measured on an interval scale. Whether positive or negative, the interval scale reads both values. For instance, if we take a winter temperature, say -10 degrees Celsius, it can be measured and read with an interval scale. Similarly, those with positive values would also be read by it.

### 3.    Interval Difference

You will note that the difference between each interval will be equal. For instance, the difference between 20 degrees Celsius and 40 degrees Celsius will be the same as the difference between 15 degrees Celsius and 25 degrees Celsius. You must already be familiar with these characteristics.

### 4.    Quantitativeness

As we said earlier, interval data is one of the two kinds of numerical data; it is either quantitative or parametric. Interval data, sometimes known as integer, shows the quantitative value and performs operations of an integer. That is why it is quite different from categorical data, such as ordinal and nominal data, which takes numerical value but demonstrates qualitative nature.

### 5.    Measurement Scale

Interval data is calculated with an interval scale that gives you the order and shows the exact difference in the values. It is again different from ordinal data because ordinal data did not tell us about the order and direction, with no standardized difference in the values of variables.

An example of an internal data measurement scale would be a ruler where the markings between intervals are equal. Thus, it is an interval scale. Like literally!

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## What is The Process of Interval Data Collection?

Since we are pretty gnostic about what interval data is and know some of its characteristics, it is high time we study the interval data collection process now. Do not worry; it is not as difficult as it sounds.

So, there are many ways to collect interval data. We will discuss a few in this guide. Methods or techniques used by researchers depend on the data usage, the person collecting the data, and the audience being targeted.

Following are interval data collection techniques:

1. Observations
2. Surveys and Questionnaires
3. Interviews

### Observation

In this data collection method, researchers make systematic observations via counting. This could be counting the number of individuals present at a particular event at a specific time and location or the number of persons attending an event at a specific location. Naturalistic and standardized observation approaches are the two types of observation methods.

### Surveys and Questionnaires

If you are wondering how daunting would it be to prepare questionnaires and surveys, you do not really have to. There is a lot of online software that will do this job for you in a matter of seconds. For instance, you get your survey created with Formplus, a questionnaire builder. Surveys are used to improve the experience of respondents while working towards gaining trust from them. When it comes to the types or kinds of surveys you can go for; there are two major ones. One is called a web-based questionnaire, and the second is an online questionnaire. You do not really need to know each type as both are exactly how they sound.

### Interviews

This is probably the most fun technique for most people.

Are you an interview person or a survey person?

Respondents in this technique are interviewed so that information could be collected and analyzed. However, these interviews are different from the casual interviews that we see happening around where people are swerving from the topic at hand. Interviews for interval data collection are planned and structured, with researchers asking a set of standardized questions. Even the interviews can be taken through different mediums. For instance, in the current COVID-19 times, an online interview would be more suitable. You can also go for a face-to-face interview, depending on the nature of your study.

Interval data is yet another type of data that can be calculated along a scale where every point is placed at an equal interval from another, just as the name explains itself.

The data collected on a thermometer is an example of interval data, as its gradation markings are equally distanced from each other. Interval data is always expressed in the form of numbers, unlike ordinal data.

Methods or techniques used by researchers depend on the data usage, the person collecting the data, and the audience being targeted.

Following are interval data collection techniques:

• Observations
• Surveys and Questionnaires
• Interviews