How to Analyze Pre and Post Survey Data

In short: Reporting pre and post means side by side hides who actually changed. To analyze pre/mid/post properly, join every wave on a persistent stakeholder ID so you follow the same person across time, report the trajectory and the % who sustained their gain, and flag anyone missing a wave. One prompt does all of this and grades the result green, amber, or red.

This is exactly Kirkpatrick Level 2 (Learning) measurement: the pre-to-post gain per participant, tracked on one persistent ID. Level 2 asks a single question — did each person actually learn the knowledge or skill the training set out to build — and you answer it by comparing that person's own before-and-after score on the same record, not by comparing two separate group averages.

Analyze Level 2 (Learning) for [COHORT]: compute the pre-to-post change on the knowledge or skills assessment per participant on the same ID, report the average gain and the share who crossed the mastery threshold, and flag participants with no measurable gain for follow-up.

Once you have proven learning at Level 2, the next question is whether people actually applied it on the job — that is Kirkpatrick Level 3 (Behavior), measured on the same participant ID 60–90 days later.

1 · Set up the assistant over your data

Point the Sopact Sense Assistant at your dataset so it works from clean records with persistent contact IDs, then have it load your Decision Brief first. The persistent ID is what makes a real join possible — without it, pre and post are two separate piles of averages, not one person's path.

You are the Sopact Sense Assistant working over the DEMO-03 · Workforce Cohort dataset (clean data + persistent contact IDs). Load my Decision Brief (decision, audience, outcomes, indicators, evidence standard) first, then wait for my task.

2 · Write the prompt

For the target metric in the workforce cohort, join pre/mid/post on stakeholder ID: trajectory, means, % sustained; flag missing waves. Grade green/amber/red.

Five elements make this prompt work: it runs over your dataset; it does a real join on ID so each row is one person across waves; it reports % sustained rather than just a mean shift; it flags missing waves instead of silently dropping them; and it ends with a grade of green, amber, or red.

3 · Read what Sense produces

Run on the Workforce Cohort dataset (DEMO-03) already loaded in Sopact Sense.

GRADE: green | Sustained improver | up and held; amber | Partial improver | up then slipped; red | Missing-wave ID | can't join

A green stakeholder rose and held the gain across waves. An amber one improved on only one wave or gained then slipped — real, but not durable. A red ID is missing a pre, mid, or post response, so their trajectory cannot be joined at all.

4 · Turn a weak link green

Take the lowest-graded element above and fix it using only what the program could realistically measure. Show the before → after grade and the single indicator/edit that moves it to green.

5 · Make the report and share it

Create a 'missing & incomplete' report from this analysis in Sopact branding [or paste your website URL / brand guideline to apply your own]. List every element graded amber or red, what is missing, and the one input that fixes each. Lead with the decision this report informs.

Create a shareable link for this report and open it in a new tab.

Tricks, tips, and troubleshooting

Join on the person, not the wave. Averaging pre and averaging post separately lets dropouts masquerade as change. The only honest comparison follows the same ID from one wave to the next.

Report % sustained, not just the mean. A rising average can hide that half the cohort slipped back. The share who held their gain is the number a funder actually cares about.

Flag missing waves loudly. A stakeholder with no mid or post response is not a zero — they are incomplete. Marking them keeps the denominator honest.

Watch for late joiners. Someone who entered at mid has no pre to compare against; treat them as a separate cohort rather than forcing a trajectory.

List every stakeholder ID missing a pre, mid, or post wave, and tell me which single wave I should chase first to complete the most trajectories.

Frequently asked questions

How do you analyze pre and post survey data?

Join the waves on a persistent stakeholder ID so each record is one person tracked over time, then report the trajectory, the means, and the percentage who sustained their gain. Flag anyone missing a wave rather than dropping them silently.

Why join on an ID instead of comparing group averages?

Group averages can rise even as individuals slip, because dropouts and new joiners change who is in each pile. Joining on the ID shows the actual path of the same people, which is what change really means.

What should I do with stakeholders missing a wave?

Flag them as incomplete rather than counting them as no-change. The one-fix prompt will tell you which single missing wave, once chased, completes the most trajectories.