Qualitative data is defined as and referred to the data that approximates or characterizes but is unable to statistically measure any attributes. This type of data is descriptive in nature. Quantitative data in statistics is also known as categorical data. The data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.
Qualitative data is important in determining the particular frequency of traits or characteristics. It allows the statistician or the researchers to form parameters through which larger data sets can be observed. Qualitative data provides the means by which observers can quantify the world around them.
For a market researcher, collecting qualitative data helps in answering questions like, who their customers are, what issues or problems they are facing and where do they need to focus their attention so problems or issues are resolved.
Qualitative data is about the emotions or perceptions of people, what they feel. In quantitative data, these perceptions and emotions are documented. It helps market researcher understand the language their consumers speak. This, in turn, helps the researchers identify and deal with the problem effectively and efficiently.
Qualitative data is also called categorical data since this data can be grouped according to categories.
For example, think of a student reading a paragraph from a book during one of the class sessions. A teacher who is listening to the reading gives a feedback on how the child read that paragraph. If the teacher gives a feedback based on fluency, intonation, throw of words, clarity in pronunciation without giving a grade to the child, this is considered as an example of qualitative data.
It’s pretty easy to understand the difference between qualitative and quantitative data, qualitative data does not include numbers in its definition of traits whereas quantitative data is all about numbers.
In order to better understand the concept of qualitative data and quantitative data, it’s best to observe examples of particular data sets and how they can be defined. Following are the examples of qualitative data and quantitative data:
- The cake is orange, blue and black in color (qualitative).
- Females have brown, black, blonde, and red hair (qualitative).
- There are 4 cakes and three muffins kept in the basket (quantitative).
- 1 glass of fizzy drink has 97.5 calories (quantitative).
Qualitative data collection is exploratory in nature, it involves in-depth analysis and research. Qualitative data collection methods are mainly focused on gaining insights, reasoning, and motivations hence they go deeper in terms of research. Since the qualitative data cannot be measured, this leads to the preference for methods or data collection tools that are structured to a limited extent.
Here are the qualitative data collection methods:
1. One-to-One Interviews: One of the most commonly used data collection instrument for qualitative research, mainly because of its personal approach. The interviewer or the researcher collects data directly from the interviewee on a one-to-one basis. The interview may be informal and unstructured – conversational. The questions asked are mostly open-ended questions, spontaneous, with the interviewer letting the flow of the interview dictate the next questions to be asked.
2. Focus groups: This is done in a group discussion setting. The group is limited to 6-10 people and a moderator is assigned to moderate the ongoing discussion.
Depending on the data which is sorted, the members of a group may have something in common. For example, a researcher conducting a study on track runners will choose athletes who are track runners or were track runners and have sufficient knowledge of the subject matter.
3. Record keeping: This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in a new research. This is similar to going to a library. There one can go over books and other reference material to collect relevant data that can likely be used in the research.
4. Process of observation: In this qualitative data collection method, the researcher immerses himself/ herself in the setting where his respondents are, and keeps a keen eye on the participants and takes down notes. This is known as the process of observation.
Besides taking notes, other documentation methods, such as video and audio recording, photography and similar methods can be used.
5. Longitudinal studies: This data collection method is performed on the same data source repeatedly over an extended period of time. It is an observational research method that goes on for a few years and in some cases can go on for even decades. The goal of this data collection method is to find correlations through an empirical study of subjects with common traits.
6. Case studies: In this method, data is gathered by in-depth analysis of case studies. The versatility of this method is demonstrated in how this method can be used to analyze both simple and complex subjects. The strength of this method is how judiciously it uses a combination of one or more qualitative data collection methods to draw inferences.
Learn more: Qualitative Research Methods
Analyzing your data is vital, as you have spent time and money collecting it in the first place. This is an essential process because you don’t want to find yourself in the dark even after putting in so many efforts. However, there are no set ground rules for analyzing qualitative data, it all begins with understanding the two main approaches to qualitative data.
Two Main Approaches to Qualitative Data Analysis
- Deductive Approach
The deductive approach involves analyzing qualitative data based on a structure that is predetermined by the researcher. In this case, a researcher can use the questions as a guide for analyzing the data. This approach is quick and easy and can be used when a researcher has a fair idea about the likely responses that he/she is going to receive from the sample population.
- Inductive Approach
The inductive approach, on the contrary, is not based on a predetermined structure or set ground rules/framework. This is more time consuming and a thorough approach to qualitative data analysis. Inductive approach is often used when a researcher has very little or no idea of the research phenomenon.
Whether you are looking to analyze qualitative data collected through a one-to-one interview or qualitative data from a survey, these simple steps will ensure a robust data analysis.
Step 1: Arrange your Data
Once you have collected all the data, it is largely unstructured and sometimes makes no sense when looked at a glance. It is, therefore, essential that as a researcher you need to first transcribe the data collected. The first step in analyzing your data is arranging your data systematically. Arranging data simply means converting all the data into a text format. You can either export the data into a spreadsheet or manually type in the data or choose from any of the computers assisted qualitative data analysis tools.
Step 2: Organize all your Data
After transforming and arranging your data, the immediate next step is to organize your data. There are chances you most likely have a large amount of information that still needs to be arranged in an orderly manner. One great way to organize the data is going back to your research objectives and then organizing the data based on the questions asked. Arrange your research objective in a table so it appears visually clear. At all costs, avoid temptations of working with unorganized data. You will end up wasting time and there will be no conclusive results obtained.
Step 3: Set a Code to the Data Collected
Setting up proper codes for the collected data takes you a step ahead. Coding is one of the best ways to compress a huge amount of information collected. Coding of qualitative data simply means categorizing and assigning properties and patterns to the collected data. Coding is an important step in qualitative data analysis as you can derive theories from relevant research findings. After assigning codes to your data, you can then begin to build on the patterns to gain in-depth insight into the data that will help make informed decisions.
Step 4: Validate your Data
Validating data is one of the important steps of qualitative data analysis for successful research. Since data is quintessential for research, it is extremely important to ensure that the data is not flawed. Please note that data validation is not just one step in qualitative data analysis, this is a recurring step, that needs to be followed throughout the research process. There are two sides to validating data:
- First, it is about the accuracy of your research design or methods.
- Second, it’s about the reliability, which is the extent to which the methods produce accurate data consistently.
Step 5: Concluding the Analysis Process
It is important to finally conclude your data, which means, presenting your data in a systematic manner, a report, that can be readily used. The report should state the method that you as a researcher used to conduct the research studies, the positives, and negatives and study limitations. In the report, you should also state the suggestions/inferences of your findings and any related area for future research.
1. It helps in-depth analysis: Qualitative data collected provide the researchers with in-depth analysis of subject matters. While collecting qualitative data, the researchers tend to probe the participants and can gather ample amount of information by asking the right kind of questions. From a series of question and answers, the data that is collected is used to draw conclusions.
2. Understand what customers think: Qualitative data helps the market researchers to understand the mindset of their customers. The use of qualitative data gives businesses an insight into why a customer purchased a product. Understanding customer language helps market research infer the data collected in a more systematic manner.
3. Rich data: Collected data can be used to conduct research in the future as well. Since the questions asked to collect qualitative data are open-ended questions, respondents are free to express their opinions which leads to collecting more information.
1. Time-consuming: As collecting qualitative data is more time consuming, fewer people are studies in comparison to collecting quantitative data and unless time and budget allow, a smaller sample size is included.
2. Not easy to generalize: Since fewer people are studied, it is difficult to generalize the results of that population.
3. Is dependent on researcher’s skills: This type of data is collected through one-to-one interviews, observations, focus groups etc. it relies on the researcher’s skills and experience to collect information from the sample.