Top 7 Techniques Which Are Helpful In Analysing Qualitative Data

Qualitative research is a study of different phenomena in natural settings. In this research, the aim is to get insights into a problem and find the best and optimal solution for it. In any field of study, you can go for qualitative research. It is the qualitative research that plays an important role in developing a sense of reality. In this regard, data is the main component of research that makes analysis possible. Without data, you cannot come up with research results. When you collect data, the major focus should be on analysis. For analysing qualitative data, you have to go for particular techniques.

At this point, you must be clear about each analysis technique. The techniques of quantitative data analysis cannot be used for qualitative data analysis. So, are you stuck on finding the best techniques for qualitative data analysis? Here is the solution to your query. This article aims to discuss the top 7 techniques which can work well for analysing qualitative data.


What Are The Top 7 Qualitative Data Analysis Techniques?

The right analysis of qualitative data makes things easy to understand. The raw data cannot provide any beneficial information, but its analysis organises it in a standard way. From that organised data, you can get the big picture of meaningful information. On a broader scale, you need to collect data and understand the impressions. In qualitative research, data is always in subjective form, so you need to code the collected data. When you are done with data coding, now you need to make connections and identify different patterns. Lastly, there should be a conclusion based on data interpretation. Let’s have a detailed discussion of the top 7 techniques for developing a better understanding.


Framework Analysis

Framework analysis is one of the techniques for analysing qualitative data, which is getting fame day by day. There is no limitation for an area of research, but the right use of framework analysis can work well for different fields of study. It is the simplest technique which focuses on data organisation. Furthermore, it follows a systematic path and explores data with the thematic research approach. Therefore, hiring a dissertation writing service UK is much beneficial for this analysis type.


Qualitative Content Analysis

The easiest technique of analysing qualitative data is content analysis. In this, you have to find the domain of data. For example, you need to see what type of data you have. It can be in the form of text, audio or image. If you have one type of data which might be effective, things would be easy to handle. On the other hand, if you have data in multiple forms, it is still manageable. Now you need to find the frequency of an idea and then make interpretations based on frequency.


Narrative Analysis

The narrative analysis helps in developing a better understanding of people and their changing behaviour because of different aspects. This analysis technique plays an important role in connecting different data points and making sense of it. You can use this analysis technique for studies related to humans and their culture. For example, the study of social sciences and ethnography use narrative analysis to highlight humans’ beliefs, norms and values. In this technique of analysing qualitative data, you have to evaluate different stories of respondents. The story must assist you in solving your research problem. So, at the time of collecting data, you need to ask the relevant question. Based on your research problem, it can be related to demographic or socioeconomic information.


Discourse Analysis

For analysing qualitative data, you have to collect data in verbal as well as non-verbal forms. The discourse analysis is best to work on such data. In this analysis technique, you have to link different aspects to generate a meaningful conclusion for the study. You can call discourse analysis as one of the main fields of linguistics. In this field, you have to determine the background of any statement. For example, a respondent has mentioned his viewpoint. Now, the collection of viewpoints is not the only thing, but you need to see the circumstances that have made him state a particular thing. Also, note down the body language that creates meaningful impacts on data.


Thematic Analysis

At the time of analysing qualitative data, when you generate themes, this technique is named as thematic analysis. The themes are made on the basis of data similarities. In this way, you can critically evaluate changes in the trends of data. The generation of theme is somehow tricky, but rest of the technique is easy to handle.


Grounded Theory Analysis(GT)

As the name of technique is grounded theory, so you can remember the main purpose of this technique from its name. In grounded theory analysis, you need to utilise the collected data and analyse it to generate a theory for research. You can collect data through an interview as well as observations. In the theory of research, you need to critically evaluate the working system of a particular system. As data is collected from a targeted audience, so theory also reflects the viewpoint of respondents. If you want to determine a difference between grounded theory analysis and other theories, you just need to remember the point of theory generation. These techniques work well for analysing qualitative data with a large sample size.


Interpretative Phenomenological Analysis (IPA)

The interpretative phenomenological analysis is all about personal life experiences. Based on a rare life event, you can make things subject-centred. For analysing qualitative data, you have to ask the audience for their life experiences. In this analysis technique, it is better to go for small sample size, but it must be precise. There should not be any vague or improper data. The interpretative phenomenological analysis technique demands a high focus on data to generate beneficial conclusions.


Final Thoughts

From the above-mentioned technique of analysing qualitative data, you cannot select one technique randomly. The selection of technique is dependent on the research problem. So, you need to deal with everything systematically. By having accurate information about these techniques, you can get the in-depth attributes of any subjective data.

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