Qualitative data analysis with SCRINTAL || part 2 - focused codes and themes

Qualitative thematic analysis in SCRINTAL (used for organizing work and mind mapping) hinges on one practical step: turning messy, overlapping initial codes into a cleaner, more focused coding structure without losing the ability to trace every claim back to the original interview quotes. After setting up initial coding in an earlier workflow, this stage focuses on reorganizing codes into broader groups and removing duplicates—while preserving provenance so evidence retrieval stays intact.

The workflow starts by grouping similar codes together under general categories tied to the same kinds of experiences. In the example study—multicultural crews on fishing boats—codes were colored by transcript source (e.g., yellow and green) to keep track of where each coded excerpt came from. As coding progresses, duplicates naturally appear when similar ideas get coded multiple times under slightly different names. The transcript gives a clear example: “language challenges,” “linguistic challenges,” and “initial linguistic challenges” all refer to the same underlying idea, but were created separately because stopping to search for existing codes would slow the process.

Instead of deleting the duplicates, the recommended cleanup approach is to create a new consolidated code card (e.g., “linguistic challenges”) and then link the older duplicate cards to this consolidated card. The color logic matters: colors indicate which transcript each original code came from, so removing the originals would break the traceability needed for later reporting. By linking, the consolidated code becomes the “future” label used for focused coding and eventual themes, while the original cards remain available as evidence trail markers. When a linked code is opened, SCRINTAL shows where it appeared (such as “interview one”) and allows the coder to review the coded excerpts, including how the excerpt was coded.

This duplicate-cleaning and relabeling process repeats across the code set. The transcript notes that the same logic applies even when deciding to merge broader categories—for instance, consolidating multiple communication-related challenges (verbal and non-verbal) into a more inclusive label like “communication challenges,” or creating a combined “cultural communication” category if that better fits the data.

Once the focused coding structure is cleaned—meaning duplicates are consolidated and everything is clearly labeled—the workflow moves to the next stage: building a thematic framework. At that point, the process becomes a repetition of the same mechanics: create new cards (often with different colors to mark them as final outputs), group the older codes under overarching themes (such as “challenges and benefits”), and keep the linked evidence accessible. The transcript emphasizes that the deeper rationale for what themes to choose is a separate topic, but the software mechanics for moving from focused codes to themes stay consistent: consolidate, link for provenance, then regroup into higher-level thematic structures.