Qualitative Data Analysis
Qualitative data analysis focuses on examining non-numerical information to uncover patterns, themes, and insights about users' experiences, behaviors, and attitudes.
This data includes interview transcripts, documents, open-ended survey responses, and interpretations of images and videos. It is not limited to text but encompasses various forms of information.
Methods for qualitative data analysis:
Empathy mapping
Representing and synthesizing qualitative data about users' thoughts, feelings, behaviors, and experiences.
Collect data.
Set up the framework.
Populate the Empathy Map.
Analyze and Synthesize.
Validate.
Whiteboards and sticky notes, Miro, MURAL, Lucidchart, Google Drawings, Figma.
Coding cycles
Breaking down data into manageable chunks and assigning labels (codes) to these segments based on their content.
First cycle: the first pass at coding your data. Second cycle: further categorizing, consolidating, and filtering your data.
NVivo, Atlas.ti, Dedoose, spreadsheets
Thematic analysis
Identifying, analyzing, and reporting patterns (themes) within data.
Familiarize yourself with the data.
Generate initial codes.
Search for themes among the codes.
Review and refine themes.
Define and name themes.
Write up the analysis.
NVivo, Atlas.ti, spreadsheets
Affinity mapping
Organizing ideas and data points into clusters based on their natural relationships.
Write data points or quotes on sticky notes.
Group related notes together.
Label each cluster to identify themes or patterns.
Physical sticky notes, digital tools like Miro, MURAL
Content analysis
Systematically categorizing verbal or behavioral data to summarize the content in a meaningful way.
Define categories based on research questions.
Code the data according to these categories.
Count the frequency of different codes.
Analyze patterns and trends.
NVivo, Atlas.ti, manual coding
Grounded theory
Generating a theory grounded in the data collected through an iterative process of data collection and analysis.
Open coding: Breaking down data into discrete parts.
Axial coding: Reassembling data into new ways.
Selective coding: Identifying core categories.
Theory development: Building a theory based on the data.
NVivo, Atlas.ti
Narrative analysis
Examining the stories people tell and how they tell them to understand their experiences.
Identify and extract narratives from the data.
Analyze the structure, content, and context of the narratives.
Interpret the meaning and implications of the narratives.
Manual analysis, NVivo, Atlas.ti
Discourse analysis
Analyzing written or spoken language in relation to its social context.
Transcribe and read through the data.
Identify different ways of talking about a topic.
Analyze language use, sentence structure, and rhetorical strategies.
Contextualize findings within the broader social context.
Manual analysis, NVivo, Atlas.ti
Empathy map
Empathy map is a tool used in UX design to better understand and empathise with users by visualising their experiences, thoughts, and feelings. It became so popular and so easy to understand by anyone who knows nothing about user interviews, biases and so on, that often is the only type of analysis that people in digital startups apply in doing qualitative analysis, while instead it should be a complementary element.
We will dive into details of using and creating empathy maps in the second project.
Coding Cycles
First cycle coding method
The first cycle of coding involves breaking down data into smaller segments and assigning initial codes. This stage focuses on identifying basic concepts and patterns within the data.
While we read the answers of the interviewees we highlight that portions that are of our interests according to the object of the research.
For example, if we aim to extract emotions, we are going to use “emotion coding values” and we will research, within the interviewee answers all that part that highlight an emotive status.
Second cycle coding method
The second cycle of coding involves refining and categorizing the initial codes from the first cycle into broader themes and patterns. This stage focuses on deeper analysis and synthesis of the data.
The process of highlighting the text, or copy/pasting it in another file, or transcribing it in a post to stick on the wall, it’s called “coding”.
Thematic Analysis
Thematic analysis is a qualitative data analysis method used to identify, analyze, and report patterns (themes) within data. It is a flexible and widely used approach that provides a detailed and nuanced account of data, making it particularly valuable for understanding complex phenomena in user experiences and behaviors.
This other approach relies on identifying themes by extracting them through a coding process. There are different types of theme, each one fit a different purpose according to the object of the research.
Steps to conduct thematic analysis:
Familiarization: get to know your data. Immerse yourself in the data to become deeply familiar with its content.
Read and re-read the data transcripts or notes.
Listen to audio recordings if available.
Make initial notes on key points and ideas that emerge.
Generating initial codes: start coding the data. Systematically code interesting features of the data.
Identify meaningful segments of data.
Assign labels (codes) to these segments that summarize their content.
Searching for themes: group codes into themes. Group codes into potential themes.
Examine codes to find broader patterns of meaning.
Organize codes into theme groups that represent different aspects of the data.
Reviewing themes: check if themes fit the data. Refine themes to ensure they accurately represent the data.
Review coded data extracts for each theme to ensure consistency.
Check if themes work in relation to the entire data set.
Split, combine, or discard themes as necessary to better capture the data.
Defining and naming themes: clearly define what each theme represents and give it a name.
Write detailed descriptions for each theme, explaining what it covers and why it is important.
Develop concise and descriptive names for each theme that capture its essence.
Ensure themes are distinct and non-overlapping.
Writing up: report your findings. Produce a final report that tells the story of the data.
Provide a coherent and compelling narrative of the themes.
Include direct quotes and excerpts from the data to illustrate key points.
Discuss the implications of the findings and how they answer the research questions.
Thematic analysis is a powerful method for analyzing qualitative data, providing deep insights into user experiences and behaviors. By systematically coding data, identifying themes, and interpreting their meanings, researchers can uncover valuable patterns that inform design decisions and improve user experiences.
Further resources:
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