Qualitative and quantitative data are two very different types of information that both have their strengths and weaknesses.
What if you could use one type of information to make up for what the other type lacks? That’s where combining qualitative and quantitative data comes into play!
This article will help break down what kind of metrics you can combine, how it can be done effectively, and why this strategy ends up producing better outcomes overall.
Before jumping straight into the topic, let us look through a quick description of Qualitative and quantitative data.
How To Merge Qualitative and Quantitative Data?
The Steps Involved in Merging of Qualitative and Quantitative Data
Types of Combining Both Qualitative and Quantitative Data
Benefits of Merging Qualitative and Quantitative Data
Qualitative data means dealing with subjective things like emotions, desires, and so on. This type usually comes from focus groups or interviews because it cannot be measured by standard instruments.
Qualitative data is also referred to as “soft data” or nonnumerical facts. It cannot be measured by standard instruments and is gathered from focus groups, interviews, and questionnaires
Qualitative data deals with subjective things like emotions, desires, etc. It can’t be measured by a standard instrument and it comes from focus groups or interviews.
Because this type of research deals with people’s feelings which are difficult to measure numerically.
Quantitative data means dealing with objective facts that have been captured through numeric representation. For instance, measurements in numbers values on different scales, counts of events observations made about an object using instruments designed for the purpose.
In other words – numerical information is measurable.
Quantitative data is the type of data that comes from surveys where you ask people for their opinions, like closed questions with multiple choice answers.
Quantitative data can also come from numbers stored in an event. For example: if there was a crisis and we wanted to know how many people were affected by it then we could get this information through census data.
How to merge qualitative and quantitative data?
Qualitative data helps you to get insights into your target audience.
On the other hand, quantitative research will let you know more specific information such as the demographics and behaviors of customers.
And when we combine both qualitative and quantitative data, it becomes easier for us to understand our customer base better and then improve product or service accordingly.
What is the process for merging? Let’s find out in the next section.
The steps involved in merging of qualitative and quantitative data
1. Identify your qualitative and quantitative research goals:
Before you start integrating any data, it is important to know the goal of merging.
Do you want to get deep insights about customers’ perceptions or just a general idea?
Set up clear objectives for both types of data collection processes.
2. Define variables:
Determine what type of information can be merged such as demographics, psychographics, attitudes, and so on.
Once this step is complete then merge two datasets by matching all the relevant fields so that there will not be any duplicates in the final dataset.
And do some clean-up work if necessary before sending out analysis results to stakeholders.
Remember to keep only those variables that help understand the customer base and remove unnecessary ones otherwise it will confuse you later.
3. Validate data:
Don’t forget about data validation because it helps to avoid mistakes when working with big sets of data.
4. Merge them easily:
Using Excel or similar software like SPSS (Statistical Package for Social Science) makes it possible to merge qualitative and quantitative data.
5. Use a common identifier:
Include the same variable in both datasets so that it becomes easy to match them up. Variables like ID or some other unique identifier for each record.
Then run the analysis and see how you can use combined data sets together.
6. Keep different categories of responses separate:
There would be more than one type of response coming from qualitative research.
In such scenarios, keep those variables separately on an excel sheet before merging with the quantitative dataset.
For example, if customers have rated the product on a scale of 0 – 100 for three attributes. Like: price, color & size then all these rating scales should be kept separate on the first row itself after importing into the SPSS software file.
Another best practice would be to keep all customer responses in separate columns since they are different variables.
For example, if customers have responded to the question “How much do you enjoy using product X?” with a number between 0 – 100 then put it on its column so this value can be easily changed into a binary response variable where 0= does not enjoy at all & 100 = enjoys very much.
7. Merge quantitative data set after testing for missing values:
After merging your qualitative and quantitative datasets make sure that both of these datasets should be free from any kind of missing or incorrect values (read more here).
At times some research participants may fail to respond to questions asked by researchers which leads them towards missing value.
If there is no way possible to get back to those respondents who have missed taking the survey, then it is advised to remove those missing values from the dataset.
We have thoroughly learned about the steps involved in combining the two types of data. Now it’s time to explore the types of merging the two data sets.
Types of combining both qualitative and quantitative data
There are several different ways you can combine your results:
The first way is an “and” approach where researchers look at both sets of findings side by side and compare them against each other for similarities or differences within their work. This method gives a great overall view of how people feel about something compared to something else that was found out via another survey or experiment just like it.
The second type is called the “plus” approach which involves looking at all gathered data collectively for an even more accurate picture of the participants’ feelings. This method is particularly helpful when researchers are unable to survey or interview all their participants in person. This can happen for several reasons including limited time and money allocated for research purposes.
For example, if a business used both of these methods to gather information about their customers, they would be able to see the differences and similarities between two types of people or regions more accurately.
The third type is called the “minus” approach where only one set of findings need to be looked at. Because it shows what people did not feel about something compared to another finding that was gathered via surveys or experiments just like it.
A perfect example would be if you had two different studies done on how voters feel about political candidates running in elections. However, some data has been left out either accidentally or deliberately by someone involved with both studies. Hence, this technique comes into play here as well helping us find out what was missed.
The fourth type is called the “statistical” approach and this method is one of the most commonly used. Using a statistical methodology that someone who has minimal training in statistics can use to combine both qualitative and quantitative data. It results in better outcomes. Meaning, any average person can do it as long as they have access to or know where to find both sets of findings.
This process will show us if there are differences between groups being compared, their averages together with how strong those differences might be using measures. For example, percentages along with what kinds of relationships exist among them all so we know exactly what happened during an experiment or survey.
In other words, this technique tells us if something important occurred outside our expectations based on prior research given certain conditions were met.
Ever wondered what are the benefits of combining methods? Don’t worry we have got you covered.
Benefits of merging qualitative and quantitative data
The helps in achieving clarity of the information. The audit trail for analysis to find out how it was done, what were the steps taken to achieve this outcome.
Transparency is fostered as there is nothing hidden from view if someone wants to go through each step and see where a mistake has been made.
It improves the ability to explore all possible options for hypothesis generation and testing because we can use both systems side by side so that no stone remains unturned when exploring different possibilities with regard to the subject at hand.
It helps in understanding the system to which the data is being applied in a better manner.
It helps in a better comprehension of the client’s perspective. It helps to understand if they are satisfied with what you have done or not, and how it could be made more effective.
It provides a clear picture of where there is room for improvement so that efforts can be directed towards those areas which need them most urgently.
This leads to efficient use of resources as well as the time available for achieving results.
It also ensures the proper allocation of resources required without wastage. Well, that is because these activities will remain focused on ones that produce positive outcomes instead of wasting energy on things that do not matter much at this point.
Merging quantitative data with qualitative data gives us detailed information about both systems side by side to find out how each one interacts with the other.
Merging both types of data is beneficial for analysis as well as predictive purposes because it allows us to make projections about the future with a fair degree of accuracy and clarity.
This merger technique also helps us understand how each one fits into the big picture and what role it plays for better business decisions making process.
The benefits of this method are so many, including being able to use current employees’ knowledge about market needs, consumer behavior, and more., tracking changes in perception over time by comparing past results with present ones, the list goes on.
Merging both types of data gives you detailed information which allows you to make predictions about future events and with clarity. It takes time, but the benefits are worth it. All you have to do is follow these simple steps and enjoy your insights.
Having understood how to combine qualitative and quantitative data along with its benefits, we hope you can choose how you want to use this technique for your research or personal projects.
This blog consists of a comprehensive guide to implement this strategy that you can apply practically.