Qualitative Data Analysis

Qualitative Data Analysis: Understand the principles and methods of qualitative data analysis with practical examples.

If you have just conducted a qualitative study involving Interviews, Focus Groups, Observations, Document or artefact analysis, Journal notes or reflections, you will need to do qualitative data analysis (similar to how you have various statistical tests for quantitative data).

Related: The complete research process explained

Getting Ready for Data Analysis

All Qualitative data analysis involves these same essential steps:

1. Raw data management: Cleaning the data
2. Data reduction by using thechniques such as ‘chunking’, ‘coding’. Organize the content into sub-groupings (chunking). Organize sub-groups into clusters of similar things that belong together (clusters, codes)
3. Data interpretation: ‘coding’, ‘clustering’
4. Data representation: How do the groups make work together? ‘telling the story’, ‘making sense of the data for others’

Preparing Raw Data for Qualitative Data Analysis

You must first prepare and organize raw data into meaningful units of analysis:

• Text or audio data should be transformed into transcripts
• Image data should be transformed into videos, photos, charts
• Review your data to see if something is not usable or relevant to your study

Data Reduction

• Read that several times to get a holistic sense of the data.
• Develop an initial sense of usable data and the general categories you will create
• Observe which sections of data are broadly similar and which ‘chunks’ can be clustered together into units that share similar meanings or qualities, to relate to a broad coding scheme.
• Create initial code list or master code book

A Note about Coding:

Data Analysis: Steps

Step 1: Familiarisation and immersion

• Listen to your recordings, and take notes. Try to take as many notes as possible. (Go over your field notes, review your online sessions… etc depending on your data collection method).
• If you have 12 interviews one hour each, this means listening to all your recordings in two days (6 hours a day).

Step 2: Transcribe and prepare data

• If you can, best to transcribe all the interviews, but if not, you only transcribe the most interesting passages (you do this thanks to your notes).
• This represents a good 4 days of work.
• Once you have all the transcripts, you read and reread them.
• Comments in the margins about the key patterns, themes and issues in the data.

Step 3: Transcribe and prepare data

• Open: Break down, compare, and categorize data
• Axial: Make connections between categories after open coding
• Selective Select the core category, relate it to other categories and confirm and explain those relationships

Step 4: Identify patterns and themes

• Axial Make connections between categories after open coding
• Selective Select the core category, relate it to other categories and confirm and explain those relationships

Code For patterns

• Similarity (things happen the same way)
• Difference (they happen in predictably different ways)
• Frequency (they happen often or seldom)
• Sequence (they happen in a certain order)
• Correspondence (they happen in relation to other activities or events)
• Causation (one appears to cause another)

Memo Writing

• Memos are a conversation with yourself
• Memos record your ideas (as they happen)
• Memos linked to coding
• Record the related code or text and time and date
• Forms a part of the analysis
• Keeps a track of analysis
• Allows you to see how your ideas developed
• Allows you to re-examine ideas later
• Can note further questions or ideas to follow up later

Step 4: Interpretation (telling the story)

• What are my key findings/themes?
• How do they connect to one another?
• What is my overall point/argument?
• What information do I need to provide to support this?
• Does my evidence support this finding?

Qualitative Data Analysis: Methods

Researcher can choose from different methods of Qualitative data analysis based on the approach and philosophy that has been used:

Content analysis

You start with some ideas about hypotheses or themes that might emerge, and look for them in the data that you have collected. You might, for example, use a colour-coding or numbering system to identify text about the different themes, grouping together ideas and gathering evidence about views on each theme.

Read more on content analysis

Thematic Analysis (TA)

Thematic analysis is one of the most common forms of analysis in qualitative research. It emphasizes, pinpointing, examining, and recording patterns (or “themes”) within data. Themes are patterns across data sets that are important to the description of a phenomenon and are associated to a specific research question.

It is “a method for identifying, analysing, and reporting patterns (themes) within data” (Braun & Clarke, 2006: 79).

The paper by Braun & Clarke (2006) is offered as one way of specifying steps for conducting thematic analysis.

Thematic Analysis: Advantages

• Relatively easy and quick method to learn and do.
• Accessible to researchers with little or no experience of qualitative research.
• Can highlight similarities and differences across the data set.
• Can generate unanticipated insights.

Thematic Analysis: Key Decisions

These decisions should be explicitly considered before analysis (and sometimes before data collection).

There needs to be an ongoing reflexive dialogue on the part of the researcher with regards to these issues throughout the analytic process.

Key decisions:

• What counts as a theme?
  A theme captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set
• What ‘size’ does a theme need to be?
  More instances do not necessarily mean the theme itself is more crucial than other themes
• Inductive or theoretical TA?
  Option A: Inductive or ‘bottom up’
  
  • Themes identified are strongly linked to the data themselves.
  • May bear little relation to the specific questions that were asked of the participants.
  • Themes may not be driven by researcher’s theoretical interest in the area or topic.
  • Involves a process of coding without trying to fit it to a pre-existing coding framework – data-driven.
  • Specific research question evolves through coding process.
  Option B: Theoretical (deductive) or ‘top down’
  
  • Driven by researcher’s theoretical or analytic interest – analyst-driven.
  • Tends to provide less a rich description of the data overall and more a detailed description of some aspect of the data.
  • Coding for specific research question(s).

Grounded analysis

This is similar to content analysis, in that it uses similar techniques for coding. However, in grounded analysis, you do not start from a defined point. Instead, you allow the data to guide you, with themes emerging from the discussions and conversations. In practice, this may be much harder to achieve because it requires you to put aside what you have read and simply concentrate on the data. Some people, such as Myers-Briggs ‘P’ types, may find this form of analysis much easier to achieve than others.

Social network analysis

This form of analysis examines the links between individuals as a way of understanding what motivates behaviour.

It has been used, for example, as a way of understanding why some people are more successful at work than others, and why some children were more likely to run away from home. This type of analysis may be most useful in combination with other methods, for example after some kind of content or grounded analysis to identify common themes about relationships. It’s often helpful to use a visual approach to this kind of analysis to generate a network diagram showing the relationships between members of a network.

Discourse analysis

This approach not only analyses conversation, but also takes into account the social context in which the conversation occurs, including previous conversations, power relationships and the concept of individual identity. It may also include analysis of written sources, such as emails or letters, and body language to give a rich source of data surrounding the actual words used.

Narrative analysis

This looks at the way in which stories are told within an organisation or society to try to understand more about the way in which people think and are organised within groups.

Main types of narrative:

• bureaucratic, which is highly structured and logical, and often about imposing control;
• quest, where the ambition is to have the most compelling story and lead others to success;
• chaos, where the story is lived, rather than told; and
• postmodern, which is rather like chaos narratives, in that it is lived, but where the ‘narrator’ is aware of the story and what they are trying

Conversation analysis

This is largely used in ethnographic research. It assumes that conversations are all governed by rules and patterns which remain the same whoever is talking. It also assumes that what is said can only be understood by looking at what went before and after.

Conversation analysis requires a detailed examination of the data, including exactly which words are used, in what order, whether speakers overlap their speech, and where the emphasis is placed. There are therefore detailed conventions used in transcribing for conversation analysis.

More Things to Consider

Things to consider in Qualitative Research:

• Is the data coded correctly?
• Has the situation been captured in a realistic manner?
• Has the context been described in sufficient detail?
• Has the researcher managed to see the world through the eyes of the participants?
• Is the approach flexible and able to change?

Final Questions Towards End of Analysis

Try to go beyond the ‘surface’ of the data:

• What does this theme mean?
• What are the assumptions underpinning it?
• What are the implications of this theme?
• What conditions are likely to have given rise to it?
• Why do people talk about this thing in this particular way (as opposed to other ways)?
• What is the overall story the different themes reveal about the topic?

Connecting to the Literature

• Your research does not exist in isolation
• Which literature has shaped your ideas?
• Which literature does your research: Support, Extend, Contradict
• You need to discuss this in your report
• Be clear on what are your findings and what is from the literature

Resources

• Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77-101