Interval data is one of the most often used data formats in practically every sector. Since it is impossible to describe all of the subtleties of interval data in a few words, here is a detailed article covering its definition, examples, and methods for analysing it.
There are two sorts to be mindful of when it comes to data measurement: quantitative and qualitative. Though both have similar names and methodologies, the manner in which they portray data differs. To represent data, quantitative uses numerals, whereas qualitative uses terms. There are various categories within the two types, one of which is interval data, which belongs to the Quantitative group.
Interval Data: A Definition
Interval data is measured along a scale consisting of a certain number of values in a more theoretical sense. Each value is separated from the other at an equal distance. The distances which are between the values are termed intervals.
Interval data belongs to the quantitative data type since it depends on numerical values.
There are two ways one can determine whether a data is interval or not:
- If the scale is made up of equal intervals between each value,
- If the chances of zero occurring are slim to none.
Characteristics of Interval Data
Here are some of the characteristics:
- It is Quantitative
- Negative Reading
- Analysts Can Perform Arithmetic Operations on Interval Data
- Measurement Scale
- The Difference Between Intervals Is Always the Same
Interval Data Is Quantitative
Interval data is numerical data and leans more toward a discrete data type.
Sometimes called an integer, interval data not only takes quantitative value but also performs the operations of an integer.
Interval data is also referred to as integer, and that is for a reason. Using it, analysts can also get negative values.
For example, when stocks begin to fall, the scale will reflect the negative reading on the stock’s poor performance. On interval scales, zero will not display, and the scale will reflect values below zero.
Analysts Can Perform Arithmetic Operations on Interval Data
Analysts can perform arithmetic operations like addition and subtraction on interval data. However, multiplication and division cannot be done. Interval data can also be used to undertake statistical analysis, such as determining the mean, median, and mode.
Data is measured on a scale with clearly defined values and intervals. There is no space for error when measuring interval data because the scale will reflect the exact difference between the numbers.
When computing the temperature from a thermometer, the values are clear and straightforward.
The Difference Between Intervals Is Always the Same
As previously stated, the interval scale’s points are separated by equal values. This indicates that the difference between each interval is the same.
The difference between 20° and 30°, for example, is the same as the difference between 30° and 40°.
Examples of Interval Data
Interval data is used in many sectors like healthcare, marketing, education, etc. Since interval scales usually provide analysts with precise data, it is not quite a surprise that it is used in the mentioned industries.
Interval data is commonly used in statistical research, academic assessment, scientific studies, and probability calculations. Here are some examples with thorough explanations to help you understand how they are used in various industries:
- IQ Test
Thermometers are the best examples of interval scales. A thermometer consists of a series of values which is used to determine the current temperature of an environment.
Researchers can receive the approximate positive and negative readings while measuring thermometers in Celsius or Fahrenheit. But not zero. In interval scales, zero is an arbitrary value.
Hence, 0°C and 0°F cannot be read on the thermometer.
When grading test scores, such as the SAT, the digits 0 to 200 are not used when scaling the raw score to the section score. Absolute zero is not employed as a reference point in this scenario. As a result, it is interval data.
Another good example to explain interval data is time. Every hour in a clock is separated by an equal distance of 60 minutes; not too much, not too less, perfectly equal.
This is why clocks are referred to as interval scales since they are equidistant and measurable.
IQ tests are used to determine a person’s average IQ. The goal of these psychological tests is to see if the person taking them is smarter than the ordinary person.
There is no way one can score 0 at all on such tests.
Because every human is smart in their way, and since there is absolutely no way one can score 0 on an IQ test, IQ is an example of interval data.
This is a student’s Grade Point Average for a specific period of time, such as a semester. The GPA calculates a student’s cumulative grade point average over time. The CGPA has equidistant intervals, making it an interval data example.
Interval Data Collection Techniques
Since we have looked at some examples of interval data, you probably have gotten a certain idea of where it is used and how. Now arises the next question, how exactly the interval data is collected?
Though a skilled data analyst or researcher is in charge of gathering interval data, we must first understand how to collect and draw the data to solve issues. Here are some of the interval data collection techniques:
- Surveys & Questionnaire
- Probability Sampling
As the term implies, observation refers to the data collecting approach in which researchers make systematic observations through counting.
For example, count the number of people in a specific venue or the number of website views within an hour, a week, and a month.
During interviews, candidates are asked a series of standard questions in order to acquire a certain quantity of data from them.
The goal of interviews is to collect data on how different participants respond to the same set of questions, their behaviours, etc. Face-to-face interviews, telephone interviews, and computer-assisted personal interviews are among the interview approaches employed.
Surveys & Questionnaire
Firms typically conduct surveys and distribute them to their clients to obtain feedback on their services and products.
Firms will understand how customers perceive their firm as a result of the survey responses, which will enable them to introduce innovations further to improve the quality of their products and performance.
Probability Sampling is a technique that entails making a random selection of data to allow researchers to draw plausible conclusions based on the information gathered. Simple random sampling, systematic random sampling, and stratified random sampling are the three main probability sampling methods.
Uses of Interval Data
Interval data is used in many sectors. Since they are easy to analyse and note, analysts find it easy to work with interval scales.
In what industries are interval data most used?
Industries that receive a plethora of customers a day and require collecting mass amounts of data by selling and marketing their services employ interval scales in their line of work. Here are some of the examples of uses of interval data:
- Marketing & Advertising
- Product Development
- Weather Forecast
Marketing & Advertising
Before embarking on one, companies must first analyse several internal and external aspects that may affect a marketing or advertising campaign. They almost always do a SWOT analysis.
Strength and Weakness are internal factors, whereas Opportunities and Threats are external factors. This study technique is also applied when a new firm wishes to enter the market.
Throughout the product development stage, researchers use TURF analysis to determine whether a new product or service will be well-received in the target market.
If a software firm wants to add a new feature to their product, they could run surveys or release a prototype to get input from their customers.
Interval data is utilised to calculate the grading system in the education industry. The examiner considers interval data of the student’s scores in the various courses offered for determining the Cumulative Grade Point Average of a student.
Doctors use a thermometer to check a patient’s body temperature as part of a medical examination. Body temperature is usually measured in Celsius and so passes as interval data.
The weather is frequently predicted based on the environment’s temperature range. Interval data is utilised in weather forecasting since temperatures are commonly measured in either Celsius or Fahrenheit.
Disadvantages of Interval Data
Interval data come with a set of disadvantages; they are:
- Interval data give quantitative descriptions. Therefore the information acquired from them is limited.
- There is no actual zero point or set starting on the interval scale. Even though one of the values is labelled “zero,” they lack an absolute zero.
- Researchers’ use of standard questions may lead to bias and false representation, in which the data represents their viewpoint rather than that of the participants. This is true for IQ testing, the SAT, and other interval data types.
- Interval Data collects a substantially smaller dataset than other data types.
How to Analyse Interval Data?
There are a variety of analytical methods used to analyse interval data. One can analyse interval data in the following analytical methods. Since it belongs to the quantitative data group, the methods will be similar.
This type of statistics is used to describe a sample population using data sets from that population. The following descriptive statistical methods are used to analyse interval data:
- Standard Deviation
The average of the interval data sample provided is calculated.
This is used to determine which variable in the data set is the most important. Because interval data is equidistant, it is simple to calculate.
This graphic depicts the data spread concerning the mean.
It’s used to show how much variation there is in the data.
Inferential statistics is a type of statistics that is used to generate predictions or inferences about a large population using data from a sample group. In other circumstances, the assumption is made based on time rather than people, as previously mentioned. Some of the approaches for analysing interval data are listed below.
- Trend Analysis
- SWOT Analysis
- Conjoint Analysis
- TURF Analysis
Trend analysis is a standard interval data analysis approach for drawing trends and insights from survey data collected. To do a trend analysis on interval data, data is collected using an interval scale survey in several iterations with the same question.
SWOT analysis is a type of analysis commonly used to examine interval data and evaluate an organisation’s strengths, weaknesses, opportunities, and threats. Internal aspects of an organisation are strengths and weaknesses, whilst external aspects are opportunities and threats. Using the SWOT analysis results, a firm can measure interval data to evaluate the market competition and plan future marketing efforts.
Conjoint Analysis is a high-level market research technique used to examine how people make complex decisions on an interval scale. The conjoint analysis derives answers to determine which aspects are vital for customers to consider before purchasing.
The acronym TURF stands for “Totally Unduplicated Reach and Frequency.” It is a process that allows a marketer to assess the market research potential for various products and services. It considers the frequency and interval data of clients reached by a specific source of communication.
Researchers utilise this study technique to determine whether a new product or service will be well-received in the target market. TURF Analysis originally developed this kind of analysis to develop media campaigns, but it has now evolved to include product distribution and line analysis.
Interval data is utilised to gather insights and values that data analysts can use to develop better solutions for continuing problems. It can also be used to forecast future events, and researchers and analysts favour it since it is simple to interpret.
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