Qualitative data analysis refers to the practice of probing into non-numerical data for explanations of the ideas, revelations, and experience represented therein. It involves looking at several qualitative-based sources such as writings, focus group discussions, observation, interviews, and document examination that can help to unearth hidden meanings, patterns and relationships. In the digital world, it involves the tracking and analysis of qualitative user data that can be used to understand and derive patterns of user behavior, which in turn helps in bettering the user experience. In this blog, we will explore qualitative data analysis and its procedure.
What is qualitative data analysis?
Qualitative data analysis refers to a systematic process of scrutinizing different types of textual materials including observations, open ended questions, focus groups and document analysis in order to understand concepts, thoughts, views or experiences that emerged through the analyzed data. Among the crucial elements of qualitative data analysis are:
- The major aim of it is to understand participants’ meaning, experience, and point of view. The key objective is in understanding “how” and “why” as opposed to “how many” or “how much”.
- It involves classifying and categorizing the information such that repeating themes, patterns, and relationships can be found within the data.
- The researcher uses an iterative process of going back and forth collecting and analyzing data in order to achieve further insight.
- It aims at reducing the overload of raw qualitative date into useful and comprehensible information.
- In order to understand what the whole analysis meant, one had to read through various portions of the data in order to interpret them.
The qualitative data analysis process
The following steps are commonly included in the analysis of qualitative data:
- Organizing the data: The first step in this process is ensuring that the original raw qualitative data, including transcripts, interview notes, etc. are organized well enough for quick retrieval, as one will likely need the information at various stages of their work.
- Familiarizing oneself with the data: The researcher reads the transcripts, notes, and documents multiple times so as to comprehend what the general meaning is all about and also to decipher the content.
- Coding the data: Labeling of particular textual or audio parts by appropriate codes which serve to classify the data and identify themes in it.
- Finding themes and patterns: At this phase, the researcher reviews the codes looking for common relationships, trends, and key themes contained in the data.
- Creating themes: Codes are then arranged in clusters form thematic themes. Themes are then stated, named and expanded.
- Interpreting the findings: After having determined the meaning of the themes as a whole, their significance and relatedness to the study question, the investigator infers.
- Data presentation: The results are ordered systematically for the purpose of reporting and are presented in tables, figures, or discussion forms.
Challenges and best practices
Some challenges in the analysis of qualitative data include identifying themes, subjective coding, and handling massive unstructured materials. Some recommended best practices include using software for qualitative data analysis, creating an audit trail, peer-reviewing code, maintaining a codebook, and triangulating results. Best practices and systematic procedures can help achieve rigor and reliability in qualitative data analysis.
Qualitative vs quantitative data analysis refers to interpreting meanings about concepts, user experiences and insights expressed through non-numerical information on data. It comprises of organizing, classifying, categorizing, and interpreting the data with an aim of identifying recurrent themes and patterns in the qualitative data. The above-stated approach can help achieve the in depth understanding of the results obtained on the basis of qualitative research.