Most survey analysis tools hand you charts and leave the statistics to you. Wavefield computes the layer research shops bill days for — raking weights, crosstabs with significance letters, AI-coded open ends, margins of error on honest bases — automatically, on every study, from $99.
Free to build · Analysis included from $99 per study · No subscription
Any tool can draw a bar chart of your responses. The questions that decide whether the numbers survive a boardroom are statistical: was the sample corrected for its skews, is the difference between segments real or noise, and what is the honest margin on that headline number. Survey data analysis software that skips those questions is a charting tool with ambitions.
Here the statistics are the default, not an add-on: weighting, significance testing on effective bases, and quality screening run on every study. The method behind each is documented openly in survey weighting, explained and how to analyze survey results.
| Basic (A) | Standard (B) | Pro (C) | |
|---|---|---|---|
| Very satisfied | 38% | 51% A | 35% |
| Somewhat satisfied | 40% | 35% | 41% |
| Dissatisfied | 22% B | 14% | 24% B |
Letters mark columns significantly higher at 95%, tested on Kish effective bases after weighting. No letter, no claim — that is the whole discipline.
Define population targets — age, gender, region, past vote — and the raking engine iterates to convergence, trims extreme weights, and computes the Kish effective base. Every report pairs weighted percentages with unweighted counts, the convention that keeps weighted results defensible.
Cut any question by any banner — demographics, plan, embedded fields — and columns are lettered, with each cell marked against the columns it significantly beats at 95%. Small bases are flagged instead of quietly reported.
The agent proposes a codeframe from your verbatims (or applies yours), assigns themes to every response, and you review the assignments. Days of manual coding become minutes, and coded themes flow into crosstabs like any closed question.
Speeders, straightliners, duplicates, and failed attention checks are flagged for review with one-click exclusion. Excluded rows stay in raw exports, labeled — the audit trail survives every decision.
Weighted distributions for every question with the margin of error computed on the effective base, not the flattering raw count. Field dates, base definitions, and completion metrics included — the report a methodologist would write.
SPSS .sav with weights registered and value labels intact, crosstab banner books in Excel, editable Word toplines, and clean CSV. Whatever your client or analyst opens next, the file is ready.
The analysis engines run on studies built and fielded on Wavefield — the instrument structure is what makes automatic weighting, validation, and coding possible. If you need to analyze an arbitrary spreadsheet from somewhere else, a general statistics tool serves you better; if you can re-field the study here, the whole layer comes free with it.
Margins of error on quota samples are industry convention, not probability theory — ours are computed on effective bases and labeled for what they are. Software that prints a MoE without that caveat is flattering you.
The open-end coder drafts the codeframe and assignments; you review and adjust. Analysis judgment — what to weight on, which segments matter, what the numbers mean for the decision — stays with the researcher. This is analysis software, not an analyst.
No live query builders, no SQL, no custom visualization canvas. The outputs are the research deliverables — toplines, crosstabs, trends, exports — done to research-grade convention. Teams wanting exploratory BI pipe the SPSS or CSV export into their own stack.
Follow the order research shops use: clean first (flag speeders, straightliners, failed attention checks), weight to population targets, read the topline start to finish, then crosstab by the segments that matter, testing differences for significance on effective bases — and only then write conclusions. Most analysis errors come from skipping cleaning or celebrating differences that never cleared the noise floor. Our full walkthrough covers each step in detail.
Professional shops traditionally combine SPSS or R for statistics with specialized crosstab tools for banner books, plus manual coding for open ends. Modern platforms collapse that stack by computing weighting, crosstabs, and significance automatically at fielding time. One disambiguation: if you searched this looking for land surveying software, that is a different field entirely — this page is about questionnaire research.
For the standard deliverables — weighted toplines, significance-tested crosstabs, banner books, coded open ends — yes, those are computed automatically on every study. For specialized modeling (regression, conjoint analysis, segmentation clustering), export the .sav file with weights registered and run the model in SPSS or R. The platform does the 90% every study needs; the export keeps the 10% possible.
Standalone analysis tools commonly run $50 to several hundred dollars per month, and SPSS licenses run roughly $100 per user per month. Wavefield prices per study instead: analysis is included with fielding at $99 to $299 per project, with no subscription between studies. If you run occasional studies rather than continuous analysis, per-study pricing is usually a fraction of the license math.
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Brief the agent, field the study, open the reports. Weighted, tested, coded, exportable — free to build, from $99 to field.
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