Stack question groups
Reshape a multi-item grid so each item becomes its own row. Stacking lets you analyse the items as a single variable instead of comparing N parallel columns.
What stacking is #
Many surveys ask the same thing about several items. A grid like "How likely are you to recommend each of these brands?" produces N parallel variables — one per brand. The data is wide: one row per respondent, N columns for the brands.
Stacking reshapes the data so each item becomes its own row. Instead of N columns, you get one likelihood column plus a new "brand" variable that says which item the row is about. The data is long: N rows per respondent.
When to stack #
- You want to compare the items as a single variable — for example "average likelihood across all brands, broken out by age group".
- You want to filter or weight on the item — for example "only show responses about brands the respondent had heard of".
- You want to model item-level effects (regression, clustering, etc.) where each item is an observation.
When to leave as-is #
- You're studying combinations or cross-patterns — for example "who picked brand A and brand B together?". Stacking flattens that relationship away.
- The items aren't really parallel. If three columns measure different things and only happen to share a prefix, stacking would smush incompatible data.
- You only need the per-item summary tables Recense already builds for the wide form.
How to stack in Recense #
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Look for stacking suggestions on the Datasets tab.
After import, Smart prep scans your question groups and surfaces "Prep suggestion" cards for groups that look stackable.
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Review the proposed reshape.
Each card shows what the new long form will look like — predicted row count, the new item variable, and any helper variables.
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Apply or dismiss.
Apply turns the suggestion into a derived stacked dataset. Dismiss hides the suggestion if the items are not really parallel. You can always re-detect later.