When we need to understand people’s opinions or needs or explain changes in metrics, we always turn our research to qualitative data. Compared to quantitative data, which is structured and relatively more essay to analyze, qualitative data is unstructured and is profound. Qualitative data can answer all of your questions with the reasons and conditions behind it and help formulate a hypothesis and build understanding.
Quantitative data can be analyzed much more easily as several tools are available to do the task, such as Excel and Tableau. But unfortunately, qualitative data is unlike. Analyzing qualitative data is difficult because there are no such mainstream tools for qualitative data. It requires to be done manually.
But there have been two things that are transforming qualitative data analysis. First is the advancement in Natural language Processing (NPL), focused on understanding human language, and the other is an explosion of user-friendly software designed to automate qualitative data analysis.
But you do not have to worry if you do not have access to the mentioned techniques. Here is your guide to analyzing your qualitative data.
WHAT IS QUALITATIVE DATA ANALYSIS
- Qualitative data analysis is the process of gathering, structuring, and interpreting data to understand what it is presenting.
- It is unstructured and non-numerical.
- It generally refers to text, open-ended responses like taking a survey and knowing people’s opinions in detail, including pictures, videos, and audio.
- Example – Businesses usually conduct qualitative data analysis to know customer feedback through chatrooms, complaints, reviews, call centers, or social media reviews.
METHODS FOR QUALITATIVE DATA ANALYSIS
Once you have successfully collected your data, there are a variety of techniques that you can adopt to analyze your data. Your specific type of research will also determine the choice and the type of data collected.
- CONTENT ANALYSIS
Content analysis is a popular approach for qualitative data analysis. Still, it does so many other techniques also get fit in the sphere of content analysis – thematic analysis is also part of content analysis. Content analysis is done by grouping content into words, themes, and concepts. It is useful in identifying the relationship between grouped content.
- NARRATIVE ANALYSIS
It focuses on the story of the people. It is specifically useful in determining people’s insights and getting a deeper knowledge of their thoughts on any specific issue. A narrative analysis might enable us to abridge the consequences of an intensive case study.
- THEMATIC ANALYSIS
It is used to deduce the meaning behind people’s words, achieved by discovering repeating themes in the text. The thematic analysis results are a coding frame that captures themes in terms of codes, also called categories. So, the process of thematic analysis can also be referred to as coding. The most common use of thematic analysis is determining customer feedback cases.
- DISCOURSE ANALYSIS
Discourse analysis is particularly used to understand political, cultural, and power dynamics that exist in any specific situation. The main focus is to understand people’s opinions in a different social context.
- GROUNDED THEORY
The ground can be a useful approach when you have little knowledge about the topic. Ground theory initiates by formulating a single data case that induces that the theory is grounded. The ground theory is based on actual data and cannot be hypothetical. Then additional cases are determined to check if they are relevant enough to be added to the original theory.
GUIDE TO ANALYZE QUALITATIVE DATA
However, the researchers are carried out on extensive levels, so the data needs to be gathered and analyzed to get the best results out of them (dissertationwritinghelp, 2021)
- GATHER YOUR DATA
The most basic step is data collection. Data collection refers to gathering all of your concerning data in one place, especially when you are collecting data from various resources. The main methods include conducting interviews, surveys, and attending focus groups. The data is generally stored in databases, CRMs, documents, or knowledge bases.
- ORGANIZE YOUR DATA
Now that you have all of your data, that data is unstructured. It needs to be organized in order to get analyzed correctly. The best way to organize your data is to go back to your interview guide. Identify and differentiate between the topics of the questions you are trying to answer (O’CONNOR, H. & GIBSON, NANCY, 2003). Like when writing the dissertation proposal, it is important to summarize your whole research; if data is easily attainable in one place, it is easier to summarize your results
So now, when all of your gathered data is organized in one place, either in your documents or spreadsheets, the next step is to code your data so that you can extract the results you wanted from that data. Coding might sound difficult, but in actuality, coding is just labeling and organizing your data and establishing relationships between these themes.
- CONVERT YOUR DATA TO MEANINGFUL INSIGHTS
The main purpose of gathering and analyzing is to get meaningful insights about a specific topic. This is the point where all of our questions will get answered. The task of finding insights is to polish through the codes that arise from the data and entice meaningful links from them. You can also create sub-codes manually to improve the quality of insights.
- MAKE A REPORT
At this step, the task is to create the report, and your focus needs to be on narrating your findings to the audience. All of your codes are properly developed, and all of your data is properly analyzed by this point. Sometimes not properly concluding your finding leaves your research in the middle of the air without answering the actual question.
If facing difficulty in shaping your finding into a concluding result, taking help with dissertation UK will be a practical step. A coherent outline of your research, findings, and results derived from them is important for readers to discuss and know the aim behind the research.
Whatever topic you choose, it is vital to understand it thoroughly and its conditions. Quantitative research might not be that detailed, but qualitative research is deep and requires so much analysis to answer your research question. It might seem not easy to you at once, but following the steps religiously will help you do your research systematically.
O’Connor, H. & Gibson, Nancy. (2003). A Step-By-Step Guide To Qualitative Data Analysis. Pimatisiwin: A Journal of Aboriginal and Indigenous Community Health. 1. 63-90.
DWH., (2022). Best Research data Collection Tools. Online Available at <https://dissertationwritinghelp.uk/best-research-data-collection-tools/> [Accessed on 10th February 2022]