What is Quantitative Data? Why is it important? How to collect it?
What is quantitative data?
Quantitative data definition as the name suggests is data that can be presented in numeric terms or can be quantified. This type of data can be distinguished by answering the question like ‘how many?’, ‘how often?’ or ‘how much?’. The value in this type of data set contains countable information which can be widely used for multiple purposes, for example, any mathematical or statistical analysis done by researchers or companies.
It is usually collected for analysis as the nature of the data set makes it easy to run mathematical techniques and calculations. The quantifiable nature gives an easier analysis giving analysts and researchers more control over the data set.
The collection of this type of data is also not rocket science and can be done in multiple ways like floating surveys, polls, or questionnaires among the whole population or any particular segment or target audience.
Quantitative data types
Quantitative data come under various types but the underlying feature of all the data sets will be the same i.e., all types will be in numeric form with an unique numeric value and property. Also, each type can be verified and evaluated using various mathematical tools and techniques.
In broad terms, it can be classified into discrete data and continuous data. Digging deeper into the types, we can further classify continuous data into interval data and ratio data. Let’s look at each type and get a better understanding of each of them.
1. Discrete data
Discrete data sets are the ones which hold unique numeric values i.e., they are both unique and countable in nature. Having said this, one should be clear that discrete data need not necessarily be counted in whole numbers. Some examples of discrete data can be – the number of ad clicks, the number of web page views and many more.
2. Continuous data
Continuous data is not unique and can take any value. Another more important feature of a this data is that it is a real time data set i.e., apart from taking any value, the values can change overtime. This data type is infinite in nature and can be broken down into small ranges for meaningful analysis as per the requirement. Some examples of continuous data can be – time taken to complete a task, temperature and many more.
3. Interval data
Interval data is essentially a type of continuous data which can be measured around a continuum. Being said that interval data have equal distance on each point on scale. For example – the difference in 5ft and 6ft height is 1ft and so is the difference between 3ft and 4ft. A very important feature of interval data is that there is no null or zero value. For instance, when we say that the temperature is zero, it holds a value and does not signify that there is no temperature at all!
4. Ratio data
Ratio data is also a further breakdown of continuous data and is very much similar to that of interval data. The only characteristic that differentiates interval data and ratio data is that unlike interval data, ratio data can have a null or zero value. For example – zero percent concentration.
Importance of quantitative data collection
Over the period of time, it has gained an immense amount of importance because of varied reasons. Some of the reasons being:
- It is comparatively easy to collect and analyze and hence save both time and effort
- A single set of quantitative data can be used to generate multiple analysis and predictions for different objectives by using different statistical tools and methods
- Analysis performed on this type of data set is less exposed to biases as the reports are generated using set principles and methods of mathematics and statistics
- Reports generated by using this data are more accurate as it is objective in nature
- Statistical analysis tends to give an in-depth analysis and so quantitative data is more likely to generate in-depth insight
Quantitative data collection methods
As mentioned earlier, quantitative data are numeric data sets used for mathematical and statistical analysis. These can be collected in majorly two ways:
Survey is basically setting up a few questions based around a specific objective and asking the target segment of the population to answer these questions. The response of each respondent is then taken collectively and analyzed further. In olden days when technology was not very advanced surveys were in the form of pen and paper but with the growth in technology, the data collection has been done online covering a large segment of target audience while making the data collection and analysis easy. There are many ways in which surveys are collected these days. Some of them are:
- Sending email surveys
- Sending SMS surveys
- Embedded surveys
- Attaching QR codes for your surveys
- Buying respondents for your data collection (mostly done for research purposes)
- Using Fynzo Survey Software to float surveys among the large audience and easy analysis
- Using API integration to target potential audience for your data collection
Another way to collect this data is by conducting one-to-one interviews and asking the target audience directly the questions for which you want to collect data. Like surveys, the way of interview conduction has also changed overtime. In earlier days, interviews were face-to-face interviews but with change in technology, interviews are now conducted in an online or virtual mode. The main ways of conducting interview are:
- Face to face interviews
- Online video interviews
- Telephonic interviews
- Interview conducted by an AI or a computer assistant
Tools to analyze quantitative data
As we know by now that quantitative data analysis is mostly done using mathematical and statistical tools, methods and principles and therefore it is more likely that the data sets are handled by a data analyst. Looking from the perspective of a data analyst following tools can come in handy during analysis:
- Microsoft Power BI
Apart from the above mentioned tools, there are ways in which a person who is not a data analyst wants to analyze quantitative data. For instance, SWOT analysis, text analysis, trend analysis, cross tabulation and many more.
Quantitative data examples
There are many areas of expertise in which quantitative data is used extensively on a regular basis. Some of the common use cases are:
1. Market Research
Quantitative data is used extensively for all kinds of market research as the data sets are bigger, easier to collect and analyze.
2. Mathematics and Statistical field
Many principles and methods in the field of mathematics and statistics are backed up by quantitative data and almost every new work in these fields requires it.
3. Stock analysis
One of the most used case of quantitative data is in the stock markets where everything relies on quantifiable data and analysis is also done on the basis of the same data set.
4. Economic predictions
Just like stock markets, each and every economy is also being valued on various factors and each factor is backed up by quantitative data collected for that economy.
5. Social Media analysis
One of the fast emerging way of marketing is social media and even for the analysis of social media performance, analyst require quantitative data.
To summarize the above article, here are the pointers:
- Quantitative data is a numeric value data that can be counted or measure
- It includes numeric values and hence use statistical and mathematical ways for analysis
- It is easy to collect and comes with various advantages
- It has two types – discrete and continuous data
Why is quantitative data used?
It can be used for a variety of reasons but in a broad manner it is used to find answers to the questions like ‘how much?’, ‘how many?’ and ‘how often?’.
How can you use Fynzo Survey to collect quantitative data?
Using Fynzo Survey to collect quantitative data is extremely easy. You just need to log in>> create a new survey or use survey templates available >> share your survey with your audience and you are all set to go for data collection!