Qualitative Analysis Overview

Qualitative analysis turns unstructured data — interview transcripts, open-ended responses, field notes — into defensible findings about how and why something happens. This module teaches it as a craft you can watch unfold, using one running example that travels through every lesson.

The pipeline

Most approaches move data through the same broad arc (Saldaña, 2021). Click a stage to see what happens there and jump to its lesson:

The lessons make each arrow concrete — on the same running example, analyzed two ways.

Two traditions (we teach both, as equals)

A real fault line runs through the literature, and it changes how you work:

• Codebook / coding-reliability. A team develops a shared codebook, codes independently, measures agreement (inter-rater reliability, IRR), and reconciles. Rigor comes from systematicity and reliability (Hsieh & Shannon, 2005) (O'Connor & Joffe, 2020).
• Reflexive thematic analysis (RTA). A researcher engages deeply and reflexively; themes are analytic outputs of interpretation, not pre-existing buckets. Here, IRR is considered conceptually inappropriate (Braun & Clarke, 2019) (Braun & Clarke, 2021).

Our running example

Throughout, we analyze a small set of interview excerpts from intro-CS students describing a debugging experience — getting stuck, deciding whether to ask for help, and improvising fixes. You’ll watch the same words get coded, consolidated, built into a codebook, coded by a second person, and assembled into themes — twice, once each way. Reaching different but equally valid theme sets from identical data is the whole point.

Vocabulary

Term	Meaning
Code	a short label on a chunk of data
Codebook	the agreed set of codes and their definitions
Category	a cluster of related codes
Theme	a higher-level pattern across categories
IRR	inter-rater reliability — agreement between independent coders