kResearch guide · 7 min read
From research question to Google Form without losing metadata
How to move from protocol language to a usable form while preserving variable names, response values and analysis context.
Forms are interfaces, but datasets need contracts
A Google Form can look clean while still producing a confusing dataset. The form title, section headings and respondent-facing labels are only one layer. The analysis layer needs stable variable names, response values, scale membership and scoring notes.
When those details are decided late, teams often patch spreadsheets by hand. That works once, then becomes fragile when the form is edited, translated, duplicated or shared with another collaborator.
Design the variable map before the form layout
Before generating questions, define the study modules and the fields each module will produce. A demographic item, a validated scale item and a consent field should not be treated as the same kind of metadata.
Good variable names are boring: short, stable and meaningful. They should survive export, statistical analysis and codebook review. If the form needs friendly text, keep that in the display label, not in the machine-readable identifier.
Preserve response values explicitly
Likert options are easy to display and easy to mishandle. If a response is shown as “Several days,” the dataset still needs to know whether that maps to 1, 2 or another scoring value. The same is true for reverse-coded items and custom demographic categories.
kResearch treats form generation as an export of a structured study, not as a blank form builder. The goal is that a generated Google Sheet still carries the assumptions needed for analysis.
Review exports as part of form QA
Form review should include both respondent experience and downstream data inspection. Open the preview, then inspect the expected columns, codebook entries and scoring notes before inviting participants.
This habit catches problems early: duplicate labels, missing consent fields, unclear section breaks and variables that are too ambiguous to analyze later.
Build the structure before the survey.
kResearch helps turn these research operations habits into a repeatable workflow: study draft, scale battery, generated forms and exportable documentation.
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