As part of my desire and goals to enhance research literacy and knowledge amongst peers and colleagues, from time to time I will share useful information and tools that I come across. This tool was provided courtesy of Queen’s University, MScHQ program.
Qualitative data analysis is a vital part of all qualitative research. Every research starts off with the collection of quality information, aka data. The collected information is then organized and analyzed to draw conclusions on the theme of the research. This process of organizing and analyzing the information collected during research is what is commonly known as data analysis in the world of research.
Analyzing qualitative data can be very confusing due to its unstructured nature. However, data analysis in whatever form can be easily carried out using the right methodology. It’s important for you to know that the validity of your research rests heavily on your data analysis. In this post, we’re going to show you how to effectively carry out a qualitative data analysis. It’s a really enjoyable process if you get started on the right foot.
The right foot for us is understanding exactly what ‘qualitative data’ and ‘qualitative data analysis’ really mean. Knowing what these terms mean will help you know if you’re doing the right thing.
What is Qualitative Data?
Qualitative data which is also known as descriptive data is a non-numerical data that captures concepts and opinions. Some examples of qualitative data include transcripts from interviews, audio/video recordings and notes from an observation.
What is a Qualitative Data Analysis?
Qualitative data analysis is simply the process of examining qualitative data to derive an explanation for a specific phenomenon. Qualitative data analysis gives you an understanding of your research objective by revealing patterns and themes in your data.
What is the Purpose of Qualitative Data Analysis?
- Data organization
- Data interpretation
- Pattern Identification
- Ties field data to research objective(s)
- Forms the basis for informed and verifiable conclusions
How Qualitative Data Analysis Differs from Quantitative Data Analysis
For people who are new to research methods, an understanding of the difference between qualitative and quantitative data analysis is very important. You don’t want to mistake one for another and veer off your proposed research methodology. The simple distinction between these two methods is that qualitative data analysis deals with the analysis of subjective and non-numerical data while quantitative data analysis focuses on analyzing data through a numerical or statistical means.
How To Analyze Qualitative Data
If you’ve taken the time to research on how to analyze qualitative data, it’s very possible that you’ve come across several different steps and rules. It’s also possible that you’re wondering which of these steps are right for you. The truth is that any of those steps could be right for you! This is because qualitative analysis, though based on certain ground rules, does not follow a rigid process. According to NSF, it’s not guided by any ground rules and is highly dependent on the researcher and the context of the study.
This knowledge is vital for your data analysis because you don’t want to find yourself in a corner as a result of following a rigid set of rules. However, as we mentioned earlier, there are ground rules for qualitative analysis. Understanding these ground rules begins with knowing the two main approaches to qualitative analysis.
Two Main Approaches to Qualitative Analysis
1. Deductive Approach
The deductive approach to qualitative data analysis involves analyzing data based on a structure predetermined by the researcher. In this case, you can use your research questions as a guide for grouping and analyzing your data. This is a quick and easy approach to qualitative data analysis and can be used when you as a researcher have an idea of likely responses from your sample population.
2. Inductive Approach
The inductive approach on the other hand, is not based on a structured or predetermined framework. This is a more thorough and time consuming approach to qualitative data analysis. This approach is often used when the researcher knows very little of their research phenomenon.
Steps to Effectively Analyzing Qualitative Data
Whether you’re looking for how to analyze qualitative data from an interview or how to analyze qualitative data from a questionnaire, these simple steps in qualitative data analysis will ensure a robust data analysis.
Transcribe all Data
After you’ve collected data from the field, it’s largely unstructured and sometimes makes no sense. It is therefore, your duty as the researcher to make sense out of field data though transcription. The first step of analyzing you data is to transcribe all data. Transcription simply means converting all data into textual form. Technology has made it very easy for you to transcribe data. You can choose out of the many computer-assisted qualitative data analysis software (CAQDAS) to transcribe your data. Using tools, like ATLAS.ti, NVivo, and of course our favorite, EvaSys, you can transcribe data effectively and a faster rate than when done manually.
Organize Your Data
After transcribing you data, you’ll most likely be left with large amounts of information all over the place. A lot of new researchers get confused and frustrated at this point. However, you can get back on track by simply organizing your data. You must resist the temptation of working with unorganized data because it will only make your data analysis more difficult. One great way to organize your research data is by going back to your research objectives or questions and then organizing the collected data according to these objectives/questions. You have to make sure to organize your data in a visually clear way. You can achieve this by using tables. Input your research objectives into the table and assign data according to each objective. You can also use any of the research software in Step 1 to simplify your data organization process.
Code Your Data
Coding is the best way to compress your data into easily understandable concepts for a more efficient data analysis process. Coding in qualitative analysis simply involves categorizing your data into concepts, properties and patterns. Coding is a vital step in any qualitative data analysis and helps the researcher give meaning to data collected from the field. You can derive the codes for your analysis from the data you’ve collected (observation will help you identify these), from theories, from relevant research findings or from your research objectives. Some popular coding terms include:
Descriptive coding: Summarizing the central theme of your data
In-Vivo Coding: Using the language of your respondents to code
Pattern Coding: Finding patterns in your data and using them as the basis of your coding
After coding your data, you can then begin to build on the themes or patterns to gain deeper insight into the meaning of the data.
Data validation is one of the pillars of successful research. Since data is at the heart of research, it becomes extremely vital to ensure that it is not flawed. You should note here that data validation isn’t just a step in qualitative data analysis; it’s something you do all through your data analysis process. It has been listed as a step here to just highlight its importance. There are two sides to data validation. First is validity which is all about the accuracy of your design/methods and the second is reliability which is the extent to which your procedures produced consistent and dependable results.
Conclusion of Data Analysis
Conclusion here simply means stating your findings and research outcomes based on the research objectives. While concluding your research, you have to find a valid link between the analyzed data and your research questions/objective. The next vital step in concluding your data analysis is presenting your data analysis as a final report. Your report has to state the processes and methods of your research, pros and cons of your research, and of course study limitations. In the final report, you should also state the implications of your findings and areas of future research.
10 Tips for Effective Qualitative Data Analysis
- Make notes always
- Ask questions and find answers to them
- Start out with existing secondary data while waiting for interview or questionnaire data
- Surround yourself with relevant literature and refer to them all through your analysis
- Watch out for patterns and themes during your data analysis
- Compare your findings to that of other relevant studies in your niche to find relationships
- Always get a second opinion from an expert
- Always work with organized data or you might be overwhelmed
- Ensure you have resources for the project before starting
- Don’t use computer software randomly just because they’re free, go only for the best or you could end up with invalid findings.
Qualitative data analysis can be very frustrating especially when you’re dealing with large amounts of data but following the right steps and processes can help you not just carry out your qualitative data analysis effectively, you’d also have fun doing it. If you’re new to qualitative data research we advice that you seek the help of an expert to guide you during your data analysis. Finally, don’t forget that good software can make your research ten times easier and save you lots of money.