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. Let’s talk about it.
Qualitative Data: Definition
Qualitative data is defined as the data that approximates and characterizes.
Qualitative data can be observed and recorded. This data type is non-numerical in nature. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups, and similar methods. Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.
Qualitative Data Examples
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 feedback on how the child read that paragraph. If the teacher gives 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.
- The cake is orange, blue, and black in color (qualitative).
- Females have brown, black, blonde, and red hair (qualitative).
Quantitative data is any quantifiable information that can be used for mathematical calculation or statistical analysis. This form of data helps in making real-life decisions based on mathematical derivations. Quantitative data is used to answer questions like how many? How often? How much? This data can be validated and verified.
To better understand the concept of qualitative and quantitative data, it’s best to observe examples of particular datasets and how they can be defined. The following are examples of quantitative data.
- There are four cakes and three muffins kept in the basket (quantitative).
- One glass of fizzy drink has 97.5 calories (quantitative).
Importance of Qualitative Data
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. It provides the means by which observers can quantify the world around them.
Qualitative data is about the emotions or perceptions of people, and what they feel. In quantitative data, these perceptions and emotions are documented. It helps market researchers understand their consumers’ language and deal with the problem effectively and efficiently.
Qualitative Data Collection Methods – Types of Qualitative Data
Qualitative data collection is exploratory; it involves in-depth analysis and research. Its collection methods mainly focus on gaining insights, reasoning, and motivations; hence, they go deeper in research. Since this data cannot be measured, researchers prefer methods or data collection tools that are structured to a limited extent.
Here are the qualitative data collection methods:
1. One-to-One Interviews: It is one of the most commonly used data collection instruments 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 method may be informal and unstructured – conversational. Mostly the open-ended questions are asked spontaneously, with the interviewer letting the flow of the interview dictate the 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 the new research. It is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can be used in the research.
4. Process of observation: In this 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. It is an observational research method that goes on for a few years and, in some cases, can go on for even decades. This data collection method aims to find correlations through an empirical study of subjects with common traits.
6. Case studies: In this method, data is gathered by an 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
Qualitative Data Analysis
Analyzing your data is vital, as you have spent time and money collecting it. It is an essential process because you don’t want to find yourself in the dark even after putting in so much effort. However, there are no set ground rules for analyzing this data; it all begins with understanding its two main approaches.
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. 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. It is a more time-consuming and thorough approach to qualitative data analysis. An inductive approach is often used when a researcher has very little or no idea of the research phenomenon.
Learn more: Data analysis in research
5 Steps to Qualitative Data Analysis
Whether you are looking to analyze qualitative data collected through a one-to-one interview or 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. Therefore, it is essential that as a researcher, you first need to transcribe the data collected. The first step in analyzing your data is arranging it systematically. Arranging data 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 computer-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. You may have a large amount of information that still needs to be arranged in an orderly manner. One of the best ways to organize the data is by 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 the temptations of working with unorganized data. You will end up wasting time, and no conclusive results will be 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 tremendous amount of information collected. Qualitative data coding means categorizing and assigning properties and patterns to the collected data.
Coding is important in this 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 crucial steps of qualitative data analysis for successful research. Since data is quintessential for research, it is imperative to ensure that the data is not flawed. Please note that data validation is not just one step in this analysis; this is a recurring step that needs to be followed throughout the research process. There are two sides to validating data:
- Accuracy of your research design or methods.
- 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 systematically presenting your data, 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 areas for future research.
Advantages
1. It helps in-depth analysis: The data collected provide the researchers with a detailed analysis, like a thematic analysis of subject matters. While collecting it, the researchers tend to probe the participants and can gather ample information by asking the right kind of questions. The data collected is used to conclude a series of questions and answers.
2. Understand what customers think: The data helps market researchers understand their customers’ mindsets. 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 more systematically.
3. Rich data: Collected data can also be used to conduct future research. Since the questions asked to collect qualitative data are open-ended questions, respondents are free to express their opinions, leading to more information.
Disadvantages
1. Time-consuming: As collecting this data is more time-consuming, fewer people study than collecting quantitative data. 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. Dependent on the 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.
It is typically descriptive data and is more difficult to analyze than quantitative data. Now, you have to decide which is the best option for your research project; remember that to obtain and analyze the qualitative data, we need a little more time, so you should consider it in your planning.
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