A food frequency questionnaire is the workhorse of nutritional epidemiology, and the fastest way to produce a dataset that cannot answer your research question.
Match the instrument to the question
An FFQ built to rank participants by usual intake of a single nutrient looks different from one intended to capture whole dietary patterns. Decide which you need before drafting a single item.
Build the food list from local data
Borrowing an FFQ validated in another population is a common shortcut and a common source of error. The food list must reflect what your population actually eats, in the portions they actually eat.
Keep the recall period realistic
Asking about the past year gives usual intake but invites recall error. Asking about the past month reduces error and captures seasonality poorly. Both are defensible; the choice needs justifying in your methods.
Portion sizes need anchors
Photographs, household measures or standard portions all work. Free-text estimation does not.
Validate before you rely on it
Compare against multiple 24-hour recalls or weighed records in a subsample, report correlation coefficients by nutrient, and be transparent about which nutrients your instrument measures poorly. Every FFQ measures something poorly.
Pilot with the real population
A pilot with twenty participants from your target group will find problems that months of internal review will not: confusing item wording, missing staple foods, and the point at which people stop concentrating.
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