The Analysis Waits on One Person Who Knows the Stats
Survey rounds export from KoBo, ODK, or SurveyCTO, program indicators live in an MIS, and trial and experiment data sit in spreadsheets, while baseline and endline never share a shape. Every question stalls at the one colleague who can drive SPSS or Stata, and so findings arrive late.
The tests that separate a real effect from noise get skipped under deadline. QuantumLayers merges those sources, runs the group-difference and correlation tests automatically with multiple-comparison control, explains them in plain language, and reruns the whole thing each round, then keeps checking for drift on its own.
From Exports to One Analysis-Ready Dataset
Four stages take you from scattered exports to one dataset ready for testing across arms, sites, and rounds.
1. Connect program & survey data
Point the SQL connector at your MIS (MySQL, PostgreSQL, or SQL Server) to pull program indicators, pull sheets from Google Sheets, and sync survey-platform CSV exports from SFTP. Pre-built query templates cover common patterns, and if you’d rather not write SQL, QL-Agent writes the query from a plain-English description against your real schema.
2. Pull in rounds & external data
Connect any REST API that returns JSON to bring in additional rounds, administrative data, or external indicators alongside your own collection. JSON structure is auto-detected, and CSV exports match with wildcard filenames so each round’s latest file lands on its own.
3. Merge into one view
Join on respondent, site, round, or arm. Schema alignment and duplicate-column handling are automatic, lining up baseline, midline, and endline into a single dataset where every response connects to its site, round, and treatment arm.
4. Analyze, then keep watching
Group tests, correlation, and outlier detection run with FDR correction and plain-language findings, and the AI writes each one with a recommended next step. Monitors then re-check on a schedule and flag when an indicator shifts between rounds.
Under ten minutes to set up
No warehouse to build, no statistician on standby, and every connection stays read-only against your sources. Set it up once, and the analysis, monitoring, and reporting run on their own from then on. Start free.
What You Can Report With Confidence
The findings that appear on their own once survey, program, and trial data sit in one dataset, from a wave of raw responses to a draft you can edit, not start.
Group & Arm Comparisons
t-test and ANOVA compare treatment against control, site against site, and round against round, telling you which differences are statistically real, ranked by size and explained plainly.
Indicator Trends Across Rounds
Trend analysis measures how each indicator moved from baseline to endline, and whether that movement is significant, so change is tested rather than eyeballed.
Driver Correlations
Correlation and regression surface which factors move with your outcome, tested rather than assumed, so the story behind a number holds up to review.
Response & Data-Quality Flags
Outlier detection flags enumerators, sites, and response patterns that break from the rest, surfacing the data-quality checks that are easy to skip before they reach the report.
Multiple-Comparison Control
Dozens of indicators are tested at once and FDR-corrected, so you are not chasing false positives, and what survives is worth reporting.
Repeatable Round Reports
Each round, scheduled reports rerun the same tests, charts, and written findings automatically, so a new wave means a draft to edit rather than an analysis to rebuild.
Waiting on the Analysis vs. Findings as Data Lands
The difference between a bottleneck and a repeatable pipeline.
| Before | After |
|---|---|
| Survey, MIS, and trial data live in separate exports | One merged dataset lines up respondents across arms, sites, and rounds |
| Analysis waits on the one person who knows SPSS | The common tests run automatically and are explained in plain language |
| “The program group did better,” untested | “The difference is significant (p < 0.05),” ranked and corrected |
| Testing many indicators invites false positives | FDR correction keeps only what holds up across comparisons |
| Data-quality issues surface after the report ships | Outlier tests flag enumerator and site anomalies as data lands |
| Every round’s analysis rebuilt by hand | Scheduled reports rerun the same tests and findings each round |
Frequently Asked Questions
Yes. CSV exports on an SFTP server are pulled in automatically with wildcard filenames, sheets connect from Google Sheets, and any REST API that returns JSON connects directly.
Group-difference tests (t-tests, ANOVA), correlation and regression, trend analysis, and outlier detection, ranked by significance and effect size, with multiple-comparison correction applied.
Yes. When many indicators are tested at once, findings are FDR-corrected so you are not reporting noise that happened to clear a threshold.
It automates and explains the common tests most M&E and applied research relies on. For bespoke modelling you would still reach for a full statistics package, but for standard comparisons and reporting it removes the bottleneck.
In your own sources. Connections are read-only and the raw data stays where it is; QuantumLayers reads it to run the analysis rather than copying it into a separate warehouse.
Explore Further
How the rest of the platform works, and where the analysis comes from.