An AI coding demo handles a regional expression correctly. That tells you something about one example. Before a US agency uses the workflow for a Latin American study, it needs evidence about the questions, codes and markets in its own scope, along with the analyst work needed to reach an accepted file.
Write the test protocol before reviewing results. This article proposes a method for evaluating Spanish-language survey coding with a fixed codebook. Every survey example is synthetic. It presents no SurveyCoder benchmark, measured time saving or universal accuracy claim.
For broader context on preserving meaning across markets, see our guide to coding Spanish responses in Latin America and Spain. If Spain is included in your pilot, report it as an additional market outside Latin America.
Define the decision the pilot must support
Name the intended use: first-pass coding for analyst review, a tracking-wave workflow, or a narrowly defined recurring question. Specify the countries, question types, languages and reporting codes in scope. Confirm market information from study records rather than classifying participants by vocabulary.
Write acceptance criteria before the run. These might include a ceiling on missed high-priority codes, a limit on unsupported amount conversions and an acceptable review workload. Set thresholds with the research owner according to the consequences of error. There is no single percentage that makes every survey-coding workflow acceptable.
Keep the scope explicit. Evaluating assignments against an approved codebook is a different question from evaluating whether automatic codebook generation discovers useful themes. You can investigate both, but report them separately.
Separate development responses from the final test
Use a development set to refine definitions and instructions, and reserve a held-out set for evaluation. Keep duplicates, near-duplicates and related responses from the same participant together when separating them could leak information. Save the selection method, record IDs and random seed if one was used.
Include an ordinary production-like sample as well as a separate set of difficult cases. Deliberately oversampling rare themes helps diagnose them, but changes the mix. Report that sample separately rather than presenting its overall score as typical production performance. Show the number of responses and reference-positive cases in each market slice.
The scikit-learn guidance on data leakage explains the general risk of using test information to make development choices. Applied here, the rule is simple: keep held-out responses out of codebook discovery, instruction generation and refinement. Once their errors inform a change, treat them as development evidence and use a fresh reserved set to evaluate the revised workflow.
SurveyCoder's coding guide says automatic codebook discovery reads all responses and AI-suggested instructions use a response sample. For this fixed-codebook test, develop those materials separately, then use the documented own-list import route with the frozen codebook and prewritten guidance. Do not run discovery or instruction suggestions on the held-out batch.
Build an independent human reference
Have two qualified reviewers code the reference sample independently with the same frozen definitions and question context. Include expertise in the relevant varieties of Spanish and the study topic. Keep them blind to the AI assignments while creating the reference.
Resolve disagreements through documented adjudication. Retain both original reviewer decisions and the final reference. The Cross-Cultural Survey Guidelines on data processing describe independent coding verification and monitoring at the code level. This protocol extends that approach to a comparison with AI-assisted coding.
Record unresolved ambiguity. Decide in advance whether those cases receive a review-required outcome, are analyzed separately or are excluded from a particular metric. Report their number and the resulting scoring denominator. A high score on only the easiest cases cannot describe the entire workload.
Design contrast cases around meaning
Use synthetic examples to test specific boundaries. For “Why did you decide against buying?”, compare:
- “No me alcanza este mes” (“I cannot afford it this month”): a budget constraint.
- “Me alcanza, pero está demasiado caro” (“I can afford it, but it is too expensive”): a high-price judgment with sufficient funds explicitly stated.
- “Está caro y no me alcanza” (“It is expensive, and I cannot afford it”): both reasons when the codebook permits multiple assignments.
Then change the context. In a sweater question, “La lana es suave” (“The wool is soft”) concerns a material, so a money code is unsupported. Our guide to coding money talk develops these distinctions further.
Include negation, short answers, spelling variation and multiple themes where they matter to the study. Try holding a short answer constant while changing the question to examine whether the workflow uses context appropriately.
Keep these diagnostic cases separate from the production-like sample. They can explain a failure; their results alone cannot estimate performance across real studies or regional varieties.
Freeze the run before scoring
Record the dataset version, codebook, guidance, settings, run date and any available model or workflow identifiers. Include the allowed number of codes per response and the treatment of residual categories. Our multi-country codebook framework explains how to preserve definitions and release history.
Save the untouched first-pass output before reviewers correct it or apply refinement suggestions. If the workflow uses previous corrections, document that starting state and keep test labels out of it. Strict codebook settings do not replace this record.
For SurveyCoder, this matters because the coding guide says saved corrections inform later runs through project Memory. Complete first-pass scoring before entering held-out reference decisions as product corrections. Describe any later re-run as a separate run with a changed history.
Count the errors by code and market
For each code, compare its presence or absence with the adjudicated reference:
- True positives: assignments supported by the reference
- False positives: assignments the reference does not support
- False negatives: reference-supported assignments the workflow missed
Precision is true positives divided by all assignments for that code. Recall is true positives divided by all reference-positive cases for it. Report the underlying counts beside each percentage. If a denominator is zero, mark the ratio undefined and explain the convention instead of presenting an apparently successful score. The scikit-learn metric definitions provide a technical reference.
For multi-code answers, score each code separately. You can also report exact agreement on the complete code set, clearly labeled. State how uncoded answers and missing outputs are handled; never silently remove them from the evaluation. Report unresolved cases and execution failures alongside the coding metrics.
Break results out by confirmed market and important question type where the sample supports it. Show denominators and avoid strong comparisons from sparse slices. A pooled result may hide missed rare themes or differences in sample composition. Inspect unflagged answers too: review flags cannot establish that everything else is correct.
Measure the work needed to reach an accepted file
Time setup, first-pass execution, analyst review, adjudication, corrections and export checks separately. Record analyst minutes and elapsed time. If comparing with manual coding, use equivalent tasks and acceptance standards, and prevent familiarity with the answers from giving one method an unfair advantage.
Keep the first-pass evaluation and the reviewed deliverable distinct. Report what the system produced initially, how much work changed it, and what remained unresolved at handoff. Include the work of building the reference in the pilot's evaluation cost, rather than silently treating it as free production labor.
Finish with a decision log: accepted scope, remaining failure modes, required human checks and a retest trigger, such as a new market, changed question or revised codebook. The conclusion should say where the workflow is ready to use and where more evidence is needed.
Run a bounded pilot
Before starting, confirm that you have a frozen codebook, separate development and test responses, independent reviewers and written acceptance criteria. Use only study data you are authorized to upload.
Follow the step-by-step SurveyCoder coding guide, or start a SurveyCoder account. Review and export an accepted study file, then make the adoption decision from the observed errors and total effort. Keep the conclusion bounded to the questions and markets you actually tested.