Qualitative research methods are one of the most common research methods, especially in social sciences, medical research, psychology, and behavioural sciences. Students can achieve a wide variety of goals by choosing the right method of data collection and analysis. This article is all about describing all data analysis methods that a qualitative researcher can select to achieve his/her research goals.
Qualitative research methods- A brief introduction:
Qualitative research is a method that helps researchers in studying the nature of a phenomenon. It is the ideal method for studies aimed to draw conclusions based on the experiences and opinions of experts by asking open-ended questions. Additionally, studies which aim to work for improvements in an existing situation or assess complex multi-component interventions can also be conducted by qualitative research methods. Fortunately, it offers a number of tools to gather responses, opinions, and experiences, including surveys, questionnaires, case studies, focus groups, observations, and interviews. No matter which tools seem you the best for grabbing all important information, the following are some data analysis techniques that can help in finding patterns to reach a conclusion.
Different data analysis methods in qualitative research:
Qualitative content analysis:
This is probably the most common data analysis technique in qualitative research methods. Simply, content analysis is important to unveil the hidden patterns within a piece of content and across information collected from multiple secondary sources. In this method of analysis, you can achieve the following goals:
- Evaluating patterns (how many times a certain term is used throughout the text)
- Finding frequency (number of times a phenomenon is discussed in different studies)
- Qualitatively establishing between variables
Apart from all, you need to make very specific questions if you want to analyse the data via content analysis. It is applicable to different types of research, but still, it has some limitations as well. It takes a longer time to analyse patterns, and there are bright chances of losing some important nuances while analysing data via qualitative content analysis.
As indicated by its name, it is all about listening to the participant’s stories and using the data to make inferences about the matter under discussion. In narrative analysis, you must follow two tasks, recording the information provided by participants and interpreting data to make it useful for solving a research problem. To conduct this analysis, you can follow six simple steps:
- Prepare and organise the data
- Generally interpret the sense of the recorded stories
- Assign codes
- Make themes and categories
- Interpret the data and try to relate it to your thesis claim
- Report results
However, narrative data analysis in the qualitative research method also has some disadvantages. Listening to stories and interpreting results are time-consuming processes that make narrative analysis incompatible to use in large-scale studies.
Discourse is a more sophisticated term used for ‘language’ and ‘debate’. In this context, you must analyse the language and words of participants used in their responses, in discourse analysis. It seems similar to content analysis as both focus on the content, but the distinguishing feature between the two is that discourse analysis focuses on language in the social and cultural context. Thus, the main goals of this type of analysis are to find how culture, history as well as power influenced the spoken or written words of participants. Steps important to conduct discourse analysis include:
- Defining research question
- Finding theories and cultural norms common in the research setting
- Collecting information and analysing it in the light of identifying cultural and social norms.
- Finding and reporting results.
Like other methods, discourse analysis in the qualitative research method also has some drawbacks. Among many, the most important is that results cannot be generalised or applied in other settings as cultural and social norms or briefs are different in different regions of the world.
Furthermore, thematic analysis is a method of analysing qualitative data collected by secondary sources of information to identify, analyse, and report repeated patterns. However, Grounded theory (GT) and Interpretive phenomenological analysis are also important data analysis methods common in qualitative research. The first aims to develop theories after conducting repeated research, while the second helps solve scientific mysteries by taking into account the experiences of participants who lived through a particular process. To know more about this method of analysis, you can seek dissertation help online.
Data analysis is the act of analysing, organising, and summarising information to get deep insights into a situation under study. In qualitative research methods, narrative, discourse, thematic, content, grounded theory, and phenomenological methods are the most sought-after data analysis processes. Remember, you must first define the scope of your study to select the most appropriate method of analysis.