## data collection


## Introduction to Qualitative Analysis

**Looking for Patterns and Meaning**

Qualitative analysis is about looking closely at the words and meanings in your data to identify patterns, themes, and insights. Rather than just reading through and jotting down ideas, following a clear analysis approach helps you structure your thinking and produce robust, thoughtful findings.

**What Counts as Qualitative Analysis?**

It’s important to remember that not all research with text is truly qualitative. Qualitative analysis is based on principles of meaning, context, and interpretation. It goes beyond surface-level coding or simply counting words.

**The Aims of Qualitative Analysis**

All qualitative analysis shares a common aim: to explore meaning. This involves recognising patterns in language, paying attention to context, and valuing participants’ perspectives. The process is both creative and interpretative — not just technical.

**Different Approaches, Different Purposes**

There are many ways to analyse qualitative data, each with its own assumptions, aims, and procedures. Choosing the right approach depends on your research question and the kind of knowledge you want to produce.

**From Data to Insight**

Qualitative analysis helps you move from raw data to meaningful understanding. By selecting a clear, principled approach, you set yourself up to create findings that are thoughtful, insightful, and valuable.

In this section, we will explore several different forms of qualitative analysis. Each approach has its own aims, follows a particular process, and is suited to certain kinds of research questions. By comparing these methods, you’ll see how different analytic choices shape the kind of knowledge that can be produced, and you’ll gain insight into which approaches may be most appropriate for your own project.

### Content Analysis

  
One of the simplest and most flexible methods of qualitative analysis is content analysis. It offers a clear, structured way of mapping what’s in your data. If you studied A-level Psychology, you may already have seen content analysis in action. If not, don’t worry — this video will introduce you to the method, explain how it works, and show you when it’s most useful.

  
Content analysis helps you describe and organise data in a systematic way, especially when working with large datasets or comparing patterns across groups or over time. Because it focuses on surface-level content and frequency, it aligns more closely with quantitative traditions. This makes it less suited to exploring deeper meaning, personal experience, or the interpretive processes that are central to other qualitative approaches.

### Thematic Analysis

  
Thematic Analysis (TA) is one of the most widely used approaches in qualitative research. It’s flexible, practical, and accessible — making it a popular choice for student projects. In this video, you’ll learn how TA works and why it’s so effective for exploring patterns of meaning.

  
Thematic Analysis helps you identify and report themes across your data, producing clear, useful findings. Its strength lies in flexibility — but it requires careful, thoughtful use to go beyond description. 

### Interpretative Phenomenological Analysis (IPA)

  
Sometimes research is about more than patterns — it’s about understanding individual experiences in depth. Interpretative Phenomenological Analysis (IPA) is designed for just that. This video introduces IPA and shows how it helps researchers explore lived experience.

  
IPA allows you to dig deeply into how people experience and interpret important events in their lives. It’s rich, and detailed — but it requires small samples and careful, interpretative work. 

### Grounded Theory

  
Some research isn’t just about describing what’s happening — it’s about developing explanations of how and why. Grounded Theory (GT) is a method for building theory directly from your data, making it especially useful for studying processes and social influences. This video introduces GT and its distinctive approach.

  
Grounded Theory is an iterative, systematic process for generating explanations grounded in real data. It’s powerful for studying social processes, but also demanding because it requires repeated cycles of coding, constant comparison across data, memo-writing, and ongoing collection of new material as ideas develop. This makes it both time-consuming and complex, as analysis and data collection happen side by side and evolve together.