How to code open-ended survey responses
Survey coding assigns categories to written answers so researchers can compare patterns without losing the original text. LabelOwl supports a codebook-based workflow: you define the categories, AI proposes labels, and you review the decisions.
1. Define your codebook
Give each category a stable code, a definition, inclusion and exclusion rules, and examples. Decide whether multiple labels are allowed and which categories are mutually exclusive. LabelOwl supports up to three hierarchy levels and keeps codebook versions separate.
2. Keep the question and context
Import Excel or CSV responses, keep respondent identifiers, and select the open-answer columns. Add the actual question and relevant study context. A short answer can refer to information in an earlier question; isolated keywords may be misleading.
3. Pilot before scaling
Review a small set containing typical, ambiguous and no-match answers. Inspect extra and missing labels, refine unclear rules, and save a new codebook version when rules change. AI agreement with a reference does not by itself establish research validity.
4. Review, confirm and export
Keep AI suggestions distinct from human-confirmed decisions. Review the original response, adjust labels and record notes. Export Excel or CSV with the intended coding version and review-status filter. Multi-label category percentages can total more than 100%.
Does LabelOwl replace qualitative analysis?
No. It helps apply an existing framework and organize review. Researchers remain responsible for category design, interpretation and methodological choices. It does not establish a universal accuracy level or automatically validate a theory.
Can I code Chinese responses?
The interface supports English and Chinese and preserves the original response text. Interface language support is not evidence of coding quality in that language. Validate a sample in your own research language before a larger run.