kappa-thematic-analysis

Intercoder reliability for thematic analysis is measured by computing per-code and overall kappa statistics to quantify agreement between independent coders. This process requires a structured codebook and pilot testing to ensure coding reliability.

Cohen's kappa is a statistical measure used in qualitative coding to calculate intercoder agreement beyond chance. It applies to independent coders or mixed agent-human workflows to produce transparent, replicable themes across qualitative datasets.

To calculate kappa statistics for a codebook pilot study, run a pilot coding round on your dataset to measure coder agreement. The resulting disagreement analysis guides codebook refinements before final thematic coding.

Yes, intercoder reliability can be applied to mixed agent-human workflows. It measures agreement across qualitative datasets, ensuring objective coding practices and replicable themes regardless of whether coders are human or AI agents.

To start thematic coding with kappa reliability testing, you need a structured codebook, an agreed coding scheme, and a dataset for pilot testing. These prerequisites allow you to compute kappa statistics and generate final themes.

Reporting thematic analysis with kappa reliability involves generating final themes with prevalence data and transparent documentation. This provides auditability by showing objective agreement measures and codebook refinements from pilot testing.