Match records without silently dropping learners

Match records without silently dropping learners — supplied practice

Matching preserves which two scores belong to the same person. A smaller matched sample is a documented subset, not a replacement for the original cohort.

Fictional learning program: eight enrolled aliases P1–P8 have baseline scores. Four post-test items score from 0 to 4; the exercise threshold is at least3. Completion means the supplied attendance/submission conditions were met, independently of quiz or project result. P1–P4 completed; their post scores are4,4,3,3 and pre scores2,3,1,2. P5–P8 did not complete and have missing post scores; their pre scores are2,1,0,1. A four-criterion project rubric was reviewed for P1–P4: P1/P3/P4 meet all criteria; P2 needs revision. No comparison group, random assignment or real participant data exists.

1. 1. Create an enrollment index

   Use P1–P8 as the unique cohort key and check one baseline score per ID.

   You should see: Eight IDs and eight baselines; baseline total 12.

   An index prevents rows from silently becoming people.

2. 2. Join the post records

   Match the four post records to P1–P4 by ID, keeping P5–P8 as missing follow-up.

   You should see: Matched subset P1–P4 contains four learners.

   A blank follow-up remains an enrolled row.

3. 3. Isolate an unmatched record

   Add hypothetical post ID P9 score 4 to a separate exception list; do not insert it into the cohort without enrollment evidence.

   You should see: P9 is unmatched and excluded from the paired/cohort calculation pending clarification.

   A score alone does not prove membership or a corresponding baseline.

4. 4. Check duplicates and conflicts

   If two P2 post rows disagree, hold that score for an authorized record check instead of picking the larger value.

   You should see: One authoritative score per matched ID is required.

   Selecting a favorable conflicting entry biases the result.