Most program evaluation plan templates get you to a clean question and a data collection checklist, then stop. The part that decides whether a funder's evaluator trusts the result, naming exactly what else could have produced each finding and how much, gets left as a line about limitations at the end. This pack builds the evaluation around that line instead. Every finding in the Findings Report carries a paired row in the Limitation Register: a named mechanism and, wherever the data allows it, a number bounding how much of the finding it could explain.
In the worked example, Cedar Line Community Health evaluates the support calls between sessions in its six-session diabetes course. Of 331 enrollees, 214 complete and hold a paired knowledge-check score, averaging a 4.6-point gain of 20. Split by calls received, the gain rises from 3.1 points to 4.4 to 6.0, a pattern that looks like the calls are working. There is no comparison group, though, and under the What Works Clearinghouse's own group design standards, a single group measured before and after cannot establish what would have happened otherwise.
The gap is not proof, and the same design that produced it can check it: staff assign calls informally to whoever seems to be struggling, so the highest-call group started 4.1 points behind the lowest-call group before session one. By post-test that gap had closed by 70.7%, which is what regression to the mean predicts on its own whenever a group is selected for an extreme baseline. The Limitation Register states both numbers next to the finding, which is what turns outcome data from a well-run collection into something a skeptical reader can check.
Outcome Analysis