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Analyzing Qualitative Data
Qualitative analysis is an important tool in psychology, education, business studies, health, human geography and other social sciences, for understanding complex unstructured data.
The analysis of qualitative data involves different ways of reading and interpreting data, to find interesting observations, trends and discoveries that help answer the research questions in a project or write a dissertation based on qualitative research. Because qualitative data varies so much, and because qualitative research questions tend to cover complex topics that do not have easy answers, there is not a common or correct way to interpret and analyse it. As Patton (2002) notes, “Qualitative analysis transforms data into findings. No formula exists for that transformation. Guidance, yes. But no recipe. Direction can and will be offered, but the final destination remains unique for each inquirer”.
Understanding and being familiar with the data sources as a whole is the first step, requiring reading and re-reading the data, until it is clear to the researcher what concepts, issues or topics are common or vary across the corpus of data. Some researchers believe this is the only necessary process, especially with small data sets with an individual researcher. However, there are other methods to adopt.
Some possible approaches include thematic or content analysis, Discourse analysis, Narrative Analysis, Semiotic analysis, In-vivo coding or Interpretive Phenomenological Analysis (IPA). In a sense, these are all different ways of reading the data, looking for different patterns or trends.
In general, most approaches to qualitative analysis work with any type of data, so you can use them for analysing interviews, focus groups, diaries, documents, social media or other sources. Most original research focuses on creating or recording dialogue from respondents, and regardless of whether these sessions are recorded with audio or video, text is usually the main focus of the analysis. Although useful context can be gained by listening to the nuance of how people talk, or watching their gestures, most analysis uses text transcripts to guide the analysis.
In questionnaire research, qualitative analysis is important for understanding the open unstructured text elements of survey questions. This may also be combined with responses to discrete or quantitative metrics, this is referred to as mixed-method research, which combines some elements of both quantitative and qualitative data and analysis.
Coding data is one way to do help with summarising and structuring text data. Coding involves creating a series of categories that describe the important ‘themes’ or topics of interest in the data. Sections of data, or quotations are tagged with these categories. This can be done on paper, often using highlighters, or PostIt notes. However, many do qualitative analysis in Excel or even Word, using a row for each sentence or paragraph, and a column for each code.
There are also software packages specifically designed to analyse qualitative data. These are referred to as qualitative data analysis software tools, Qualitative Data Analysis Software (QDAS) or Computer Assisted Qualitative Data AnalysiS (CAQDAS). Quirkos is one example of these packages, which is visual, easy to use and affordable.
Qualitative analysis is often an experimental process, and many different ways of interpreting the data may be tried simultaneously or sequentially. It is often described as a cyclical or iterative process, where several read throughs or codings of the data will be performed. It’s also common to combine different approaches.
It’s also important to remember that the ultimate aim of the process is to write up research findings, for a thesis, dissertation or research paper. Therefore qualitative analysis should help researchers get to a stage where they understand the data, can summarise the main findings, and quickly and easily find quotes on a particular topic to back up their interpretation. This means that a good analytical approach is really a way of managing data, rather than just a single step in the research process.
Patten 2002, Qualitative Research & Evaluation Methods, Sage, London
Schutt, 2011, Investigating the Social World, Sage, London
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