Find responses that need a closer look.

The single most useful quality signal is time. A person who genuinely reads and answers a ten-minute survey cannot finish it in ninety seconds; someone chasing a payout can. Record the duration of every response and you can see the racers as a cluster far below everyone else.

Fixed thresholds are fragile because they depend on your survey, so compute the cutoff from your own data: take the median completion time of real responses and flag anything below roughly a third of it. Plumeform does this automatically once a handful of timed responses are in, and marks the speeders for you.

Someone may choose the same point for every row of a rating matrix. This pattern is called straight-lining, and it is invisible in a summary chart because the answers are perfectly valid values.

You catch it by looking at the variance within a single response. Repeated answers across a long battery deserve review, especially when items differ in meaning. They can also reflect someone’s views, so check the wording and other quality signals before excluding the response.

Automated and repeat submissions cluster. The same option pattern, the same suspiciously round timing, often the same referrer, arriving in a burst. Open-text answers are the giveaway: generic, off-topic, or copied verbatim across “different” people.

An instructed-response item asks someone to select a named answer, such as Disagree. Use clear instructions and review a failure alongside timing and answer content.

Attention checks pair with everything above: a response that speeds, straight-lines, and fails a check deserves closer review. Our attention-check guide explains how to write and use these items.

Before collecting data, decide your exclusion criteria before you see the results. Keep the original data, mark the flagged rows, and apply those rules consistently. Changing exclusions to suit a result can bias the analysis.

In Plumeform every response carries its quality flags, and a single filter hides the flagged ones so you can see your results clean and dirty side by side. If the story is the same either way, you can report it with confidence; if it changes, that is worth knowing before you present it.