Survey Analytics
Learn how to analyze survey data from basics to advanced techniques
Showing 1–14 of 14 articles
Common Survey Metrics and When to Use Each
NPS, CSAT, and CES are the three general-purpose customer-experience metrics worth knowing before reaching for a field-specific one - this guide covers each one's exact origin, question wording, and scoring method, how to decide between them, and how to set one up as a Rating Scale question in Opionate.
Reading Shelf Test Results: Picks, Share, and First-Pick Position
A Shelf Test report shows which images respondents picked, how many of them picked each one, and where in the sequence people first reached for it. Here's what each number means and what it doesn't.
Reading Click Heatmap Results: Click Order, Hit Rate, and Time to First Click
A Click Heatmap widget hands you every respondent's click overlaid on your stimulus image. Here's what the click-order coloring, the hit/miss shading, and the two headline timing stats actually tell you.
Why Two Views of the Same Report Can Show Different Numbers
You open the same report an hour apart and a number has changed. That's not a bug - reports are live, and here's exactly what that means for how you should treat what you see.
When Your Sample Size Is Too Small to Trust a Result
You've already collected your responses - now the question is how much weight the results deserve. Here's how to gauge that without formal significance testing available.
Reading Pairwise Comparison Results: What the Rankings Actually Mean
A Pairwise Comparison widget hands you a ranked list of items. Here's what that ranking is actually built from, and how to read it without overstating what a head-to-head win rate tells you.
Reading Multi-Select Results: Why Percentages Don't Add to 100%
A multi-select question's answer breakdown can add up to well over 100%. That's correct, not a calculation error - here's why, and how to read these results properly.
Reading Cross-Tab Results: What to Look for Beyond the Percentages
A cross-tab full of percentages invites you to compare numbers directly. Here's how to read the differences responsibly, and what questions to ask before treating a gap between segments as a real finding.
Interpreting NPS and Scale Question Results
A single NPS score or average scale rating is easy to report and easy to misread. Here's how to interpret these results with enough nuance to actually act on them.
Completion Rate and Average Time: What Your Survey Management Page Shows You
Your Survey Management page shows more than a response count. Here's how to read completion rate and average completion time without drawing the wrong conclusion from either.
Comparing Results Across Survey Waves: A Methodology Guide
Tracking a metric over time only works if wave to wave, you're actually comparing like with like. Here's what to keep consistent before you trust a change in the numbers.
Reading Sequence Flow Results: Consensus Routes vs. Averaged Rank
Averaging each item's position independently across respondents throws away the thing a sequencing question actually asks for - the order itself. Here's how Sequence Flow's Route Flow and Route Network views read results instead, and when to trust each one.
Ranking Questions vs. Sequence Flow: When Simple Rank Order Isn't Enough
Both a plain Ranking question and Sequence Flow can ask respondents to put items in order - and both now support asking for just a top subset instead of a full ranking. The real difference is in how the task is built, and how the results are read. Here's how to choose.
How Many Panel Respondents Do You Need for a Reliable Result
The right sample size depends on the same statistical basics as any survey - plus one thing specific to panel recruitment: your target is approved responses, and a real share of the people you recruit won't qualify past your own screening. Here's how to think through both.