A result is reproducible when the underlying data, configuration, and documented procedure produce the same number independently.
This page lists the available outputs, what each contains, and what to check when re-deriving a reported figure.
Respondent-level data
| Output | Contents |
|---|---|
| SPSS export | Full .sav with its Excel codebook as a ZIP bundle, in wide or stacked, coded or uncoded form |
| Wide Excel | One row per respondent. Raw wide exports include non-complete respondents |
| Long Excel | Stacked format, one row per response |
| CSV | Raw or stacked, for direct load into any analysis environment |
Coded exports return recoded categories; uncoded exports return the underlying response values. Use those when checking how a reported category was constructed. Where a report nets or recategorizes responses, comparing the uncoded export against the report is how you confirm the mapping.
Because raw wide exports include respondents who did not complete, they also support checking terminations and break-off directly rather than inferring them from the achieved sample.
Verifying the sample itself
Respondent reconciliation downloads, scoped to the selected dataset, reconcile respondents against the respondent source — the basis for checking delivery against invoice, and for confirming which respondents were excluded and at which stage. Read alongside the live incidence breakdown of completed, in progress, terminated, and poor quality per source described in Respondent sourcing methodology, this makes the quality pipeline's exclusions visible rather than merely asserted. The pipeline itself is documented in Data quality methodology.
Respondent transcripts export the interview as the respondent experienced it, the fastest way to check that routing, piping, and question wording behaved as specified.
Verifying the analysis
Weighting. The diagnostics report the efficiency ratio and the distribution of weights, and the export carries the respondent-level weights themselves, so weighted results can be recomputed independently from the raw responses. Which stages ran, on which universe, against which targets, is part of the configuration rather than a black box. See Weighting methodology.
Discrete choice. For conjoint and MaxDiff, individual-level utility scores and raw Hierarchical Bayes draws support offline analysis. Match the question, dataset, calibration method, retained draws, reporting weights and cell population to the report being checked. Use draws from the same calibrated or uncalibrated model as the report.
Recompute each transformation within each respondent and draw: normalize attribute ranges for importance, normalize item utilities for simulated share, and count utilities strictly above zero for Probability above anchor. Marginal Purchase Lift additionally needs the purchase-intercept draws and the same empirical profile counts; giving every profile equal weight changes what the result measures. You need those inputs and the individual draws to reproduce the calculation; mean utilities alone are insufficient. See Utility and simulated share methodology for the formulas.
Lift. The report exposes its own specification: which question or tag is the group indicator, which values count as treated, each outcome's calculation and cut-by source, the matching questions, the number of matching iterations, and the p-value threshold. With the respondent-level export and that specification, the exposed and untreated bases and the unmatched outcome rates can be recomputed directly. Matched-control selection is deterministic, so the same respondent data, configuration, and reporting implementation reproduce the platform's matched estimate. An independent implementation must reproduce the platform's matching procedure as well as its inputs to obtain the exact result. See Lift measurement methodology.
Significance. Match the uncertainty procedure to the output: ordinary cross-tab comparisons, posterior-draw summaries for discrete choice, or mean Fisher p-values across matched Lift iterations. Check the available base sizes and uncertainty measures with the configured comparison or filter. In Lift details, use the mean p-value; do not interpret a 0.95 p-value threshold as 95% confidence. See Understanding stat testing and Sample size and precision.
Programmatic access
Surveys, structure, and cross-tab reports are available through a public API with OAuth authentication and through an MCP integration for agentic analysis, so verification can be automated or run outside the interface. Feasibility is exposed the same way. See Public API for reporting and agentic analysis and MCP and OpenAPI integration.
What to check first
If you are auditing a single reported figure, the fastest route is usually this order: confirm the base the figure rests on and whether it is weighted or unweighted; pull the uncoded export and confirm the response values behind any netted or recategorized number; check the weights and the efficiency ratio; then recompute the statistic and compare against the reported margin of error rather than against the point estimate alone.
Most discrepancies resolve at the first or second step, and they are usually definitional (a different base, or a different treatment of partial completes) rather than computational.
Scope
Exports contain what the study collected. They do not contain the respondent-source-side data the platform never receives, such as a sample provider's own profiling history or recruitment records; where a study needs that, it has to be agreed with the provider directly. Personal data in exports is whatever the study chose to collect: MX8 Labs always stores a hashed IP address and a unique respondent identifier, and anything beyond that is the study's decision. See Privacy compliance and, for current certification status, trust.mx8labs.com.