Qualitative data is where data becomes insights, and insights drive meaningful action. It's what enables qualitative data to shine, bringing context to life from customers eager to share their honest thoughts about your brand.
But without a plan to make sense of qualitative insights, they're at risk of collecting digital dust. That's where qualitative coding comes in.
In this guide, we're going to walk through how to do qualitative data analysis, so you can turn your qualitative data into the goldmine that it is – and then some.
Below, we'll explore:
Various qualitative data analysis methods
Types of qualitative data sources, and effective strategies for data collection
A walkthrough of the best qualitative coding methods by research goal
Let's dive in!
What is qualitative data coding?
Qualitative data coding is the process of analyzing and categorizing qualitative (non-numerical) data, such as interview transcripts, open-ended survey responses, or observational notes to arrive at patterns and themes.
Coding involves assigning descriptive labels or "codes" to segments of the qualitative data, to summarize and condense the information. Coding can be done inductively, where the codes emerge from the data itself, or deductively, where the researcher starts with a pre-determined set of codes based on existing theories or frameworks.
What's the benefit of qualitative data analysis?
Qualitative data dives into the intricacies of human experiences that quantitative data often overlooks. Qualitative research typically provides a deeper, more nuanced understanding of human behaviour, experiences, perceptions, and motivations. It can reveal the "why" and "how" behind the "what" that quantitative data shows.
Qualitative research is generally more flexible, and can be adapted to explore new or unexpected insights that emerge during the research process. It's a great tool that complements, and enhances quantitative research.
Types of qualitative data
Qualitative data comes in various forms. Each offers unique insights into different aspects of the human experience. Understanding the different types of qualitative data is the key to designing effective research methodologies, and strategies for your team to code qualitative data effectively.
Let’s explore some common types of qualitative data:
1. Textual Data
What it is: Written or verbal data in the form of transcripts, interviews, focus group discussions, open-ended survey responses, social media comments, emails, or customer reviews.
Advantages: Provides rich contextual information, sentiments, opinions, and narratives from direct interactions with customers or stakeholders.
2. Visual Data
What it is: Images, videos, diagrams, infographics, or any visual representation that captures non-verbal cues, gestures, emotions, or environmental contexts.
Advantages: Complements textual data by adding visual context and expressions that enhance the depth of qualitative insights.
3. Audio Data
What it is: Recordings of interviews, phone calls, focus group sessions, or any audio-based interactions.
Advantages: Captures tonal variations, emotions, and nuances in verbal communications, providing additional layers of understanding.
4. Observational Data
What it is: Direct observations of behaviours, interactions, or events in real-time settings such as ethnographic studies, field observations, or usability testing.
Advantages: Offers firsthand insights into natural behaviours, decision-making processes, and contextual factors influencing experiences.
5. Contextual Data
What it is: Information about the context, environment, culture, demographics, or situational factors influencing behaviours or perceptions.
Usage: Helps in interpreting qualitative findings within relevant contexts, identifying cultural nuances, and understanding environmental influences.
6. Metadata
Description: Additional data accompanying qualitative sources, such as timestamps, location information, participant demographics, or categorizations.
Advantages: Provides context, aids in organizing and filtering data, and supports comparative analysis across different segments or timeframes.
7. Historical Data
Description: Past records, archival materials, historical documents, or retrospective accounts relevant to the research topic.
Advantages: Offers historical perspectives, longitudinal insights, and continuity in understanding changes, trends, or patterns over time.
8. Digital Data
Description: Data generated from digital interactions, online platforms, websites, social media, digital surveys, or user-generated content.
Advantages: Reflects digital behaviours, user experiences, online sentiments, and interactions in virtual environments.
9. Multi-modal Data
Description: Integration of multiple data types such as textual, visual, audio, and contextual data sources for comprehensive analysis.
Advantages: Enables triangulation of findings, validation of insights across different modalities, and holistic understanding of complex phenomena.
10. Secondary Data
Description: Existing data sources, literature reviews, case studies, or research studies conducted by other researchers or organizations.
Advantages: Supplements primary qualitative data, provides comparative insights, validates findings, or offers historical context to research outcomes.
Understanding when, and how, to use each data type will elevate your overall research efforts. Thanks to the diversity of the data, you can lean on a handful of different forms to arrive at meaningful insights. This flexibility enables you to design robust data strategies that are closely aligned with research objectives.
But it also means, that you'll need a qualitative coding system to analyze the data consistently, to get the most out of your diverse findings.
How to collect qualitative data
Coding qualitative data effectively starts with having the right data to begin with. Here are a few common sources you can turn to to gather qualitative data for your research project:
Interviews: Conducting structured, semi-structured, or unstructured interviews with individuals or groups is a great way to start. With these you can gather in-depth insights about experiences, opinions, and perspectives. Interviews can be face-to-face, over the phone, or done with video calls.
Focus Groups: This involves bringing together a small group of participants to engage in discussions facilitated by a moderator. Focus groups allow researchers to explore group dynamics, shared experiences, and diverse viewpoints.
Surveys: Design open-ended survey questions to capture qualitative responses from respondents. Surveys can be distributed through email, online platforms, or in-person interviews to gather large volumes of qualitative data.
Observations: Arranging sessions to systematically observe and record behaviours in a particular setting is a great qualitative data source. Observations can be participant-based (the researcher actively participates) or non-participant (the researcher observes without interference).
Document Analysis: You can review existing documents, texts, artifacts, or media sources to extract qualitative insights from them. Documents could be written reports, social media posts, customer reviews, historical records, among other things.
Diaries or Journals: Ask participants to maintain personal diaries or journals to record their thoughts, experiences, and reflections over a specific period. Diaries provide rich, real-time qualitative data about daily life and emotions.
Ethnography: Immersing yourself in participants' natural environments or cultural contexts to observe social behaviours or norms. Ethnographic studies aim to gain deep cultural insights from a particular group.
Each insight collection method offers unique advantages and challenges when it comes to your research objectives.
The key in picking your method, is to align data types and collection with your research goals as much as possible to ensure the data is rich, and will remain relevant to your research questions.
What are the different types of coding?
Before we dive into the specifics around different methods to code qualitative data, let's start with the most basic understanding of research approaches. In general, there are two: inductive and deductive coding.
Inductive coding is ideal for exploratory research, when the goal is to develop new theories, ideas or concepts. It allows the data to speak for itself.
Deductive coding, on the other hand, is better suited when the researcher has a pre-determined structure or framework they need to fit the data into, such as in program evaluation or content analysis studies.
The key difference between these two approaches is that with deductive coding, you start with a framework of pre-established codes, which you use to label all the data that comes through your research project.
Deductive coding example
Say a researcher wanted to determine the answer to the research question –– what are the main factors that influence customer satisfaction with an e-commerce website?
Using deductive coding, you would develop a set of pre-determined codes based on existing theories and research on customer satisfaction with e-commerce websites. They might include, "website usability," "pricing," "product selection," or "customer service."
The researcher then collects the qualitative data, like customer interviews or open-ended survey responses about their experiences using the e-commerce website. The pre-defined codes provide a guide with which you would systematically categorize the data according to the most relevant category.
Once all the data is coded, you can analyze the frequency and relationships between the different codes to identify the key factors influencing customer satisfaction. You may find, for example, that website usability and shipping/delivery are the most prominent factors driving satisfaction.
This deductive approach helps in testing existing theories and frameworks around e-commerce customer satisfaction. It provides a structured way to analyze the data, and answer the research question.
Inductive coding example
Inductive coding operates with a different mindset when it comes to qualitative data analysis. Instead of starting with a pre-defined set of codes, the researcher reads through interview transcripts and begins to identify emerging themes and patterns in the data. This is distinct from the 'bottom-up' deductive approach.
Let's say your research question is –– what are the key factors that influence job satisfaction among software engineers?
With this approach, you could collect your qualitative data through interviews with software engineers to hear about their experiences and perceptions about job satisfaction. As you analyze your qualitative data, you start to identify pattern and themes from the data itself, capturing them into codes. These might be "work-life balance," "career development," or "team culture".
With inductive coding, the codes you use are grounded in the actual language and perspectives of the participants. The advantage here is that the data guides the analysis, rather than trying to fit the data into pre-existing assumptions or frameworks. This typically leads to better research outcomes, as real-world experiences and perspectives of the participants ground the insights.
Qualitative coding methods
Now that we know the main ways of assigning codes, let's dive a bit deeper to understand more granular methods.
When it comes to choosing a method to structure and analyze your data, your first criteria should be to align the method with your research goals. It's also worth noting that using multiple complementary methods (triangulation) can provide more robust analysis.
In this section, let's explore a range of qualitative coding methods. Each offers unique perspectives to help you unlock the most meaning from your qualitative data.
Thematic Analysis Coding
Thematic analysis coding is your go-to method when you want to uncover recurring patterns and themes across your qualitative data.
Imagine you're knee-deep in interview transcripts from customer feedback sessions. You start noticing phrases like "user-friendly interface" or "quick issue resolution" popping up frequently. These phrases are your themes. By coding them under relevant categories like "Ease of Use" or "Efficient Support," you're essentially organizing your data in a way that makes sense. This method works wonders when you have a large volume of qualitative data and need to distill it into manageable themes for deeper analysis.
Pattern Coding
Pattern coding is all about spotting and grouping similarly coded excerpts under one overarching code to describe a pattern.
Let's say you're analyzing customer reviews of a new mobile app. You notice phrases like "love the design but slow loading times" or "great features, needs smoother navigation." These phrases share a common thread—the balance between design and functionality. By creating a pattern code like "Design-Functionality Balance," you capture the essence of these comments without losing their individual insights. This method helps you identify trends or issues that might go unnoticed otherwise.
Focused/Selective Coding
Focused or selective coding comes into play when you've completed an initial round of "open coding" and need to refine your codes further.
Picture yourself swimming in a sea of codes derived from open-ended survey responses. You've identified several themes but want to narrow them down to the most relevant ones. Focused coding helps you create a finalized set of codes and categories based on your research objectives. This method is like streamlining your focus, ensuring that every code you use aligns directly with your study's purpose.
Axial Coding
Axial coding is your tool for connecting the dots between codes or categories, unveiling relationships and links within your data.
Imagine you've coded various customer sentiments about a product launch. Some codes relate to pricing satisfaction, while others focus on feature preferences. Axial coding helps you see how these codes intersect—are customers who like certain features more forgiving about pricing, or vice versa? This method dives deep into understanding the interconnectedness of different aspects of your qualitative data.
Theoretical Coding
Theoretical coding lets you build a conceptual framework by structuring codes and categories around emerging theories or concepts.
Imagine you're studying employee satisfaction in a company undergoing digital transformation. Your codes reveal sentiments about adapting to new tools, workload changes, and management support. Theoretical coding helps you map these codes to existing theories like Herzberg's Two-Factor Theory or Maslow's Hierarchy of Needs, adding layers of theoretical understanding to your qualitative analysis.
Elaborative Coding
Elaborative coding is about applying previous research theories or frameworks to your current data and observing how they align or differ.
Let's say your study on customer loyalty echoes findings from established loyalty models like the Loyalty Pyramid. Elaborative coding helps you validate these connections or identify nuances that existing models might overlook. It's like having a conversation between your data and established theories, enriching your analysis with broader industry perspectives.
Longitudinal Coding
Longitudinal coding is crucial when you're tracking changes or developments in qualitative data over time.
Imagine you're studying consumer perceptions of a brand across multiple years. Longitudinal coding allows you to compare sentiments, identify shifts in customer preferences, and track the impact of marketing campaigns or product changes. This method provides a dynamic view of your data's evolution, helping you stay current and adaptive in your research insights.
In Vivo Coding
In vivo coding involves summarizing passages into single words or phrases directly extracted from the data itself.
Say you're analyzing focus group transcripts about online shopping experiences. Participants mention phrases like "cart abandonment blues" or "scroll fatigue." In vivo coding captures the essence of these experiences using participants' own language. It's about letting your data speak for itself, preserving the authenticity and nuances of participants' voices.
Process Coding
Process coding uses gerund codes to describe actions or processes within your qualitative data.
For example, let's say you're studying customer support interactions. Your codes highlight actions like "resolving complaints," "escalating issues," or "navigating knowledge bases." Process coding helps you dissect complex interactions into actionable steps, making it easier to analyze workflows, identify bottlenecks, or pinpoint areas for improvement.
Open Coding
Open coding kicks off your qualitative analysis journey by allowing loose and tentative coding to identify emerging concepts or themes.
Imagine you're starting interviews for a market research project. Open qualitative coding lets you tag responses with codes like "price concerns," "product satisfaction," or "brand loyalty." It's like casting a wide net to capture diverse customer insights, setting the stage for more focused coding and deeper analysis down the road.
Qualitative data software tools
When it comes to qualitative research and doing qualitative data analysis, having the right tools can make all the difference.
There are a plethora of qualitative data analysis software available to help make interpretation a lot easier –– using both deductive and inductive coding techniques. The choice of your tools depends on the specific needs of your research project, your familiarity to navigate it, and the level of complexity required. Keep in mind that many researchers find it beneficial to use a combination of tools at different stages of the research process.
Below are some factors to consider when deciding on a tool:
Ability to code and categorize data (both inductively and deductively)
Tools for identifying themes, patterns, and relationships in the data
Visualization capabilities to help explore and present findings
Support for diverse data types (text, audio, video, images)
Collaboration and reporting capabilities
Ease of use and intuitive interface
In summary
Qualitative data coding is not just about assigning labels, it's about uncovering stories, emotions, and valuable insights hidden within your qualitative research data. By using a blend of the coding methods such as thematic analysis, pattern coding, and in vivo coding, your can get to the heart of your customers' narrative, and unearth ways to serve them better.
Ready to unlock the full potential of your qualitative research journey? Get the tools, techniques, and strategies you need with Kapiche –– eliminate costly manual coding, and achieve meaningful, inductive insights fast. Check out a demo of Kapiche today to explore how it can help.
