Long Excel Format

The Long format is designed for detailed, question-by-question analysis. It includes all the data stored in the platform and is identical to the data used internally for reporting. Each row represents a single respondent's answer to a single survey question. This format is particularly useful when you want to:

• Explore how different questions were answered across respondents.

• Pivot, filter, and group responses in tools like Excel, R, or Python.

• Work with multi-choice and grid questions without needing to manage many columns.

Each row corresponds to one answer to one option of one question from a respondent. Respondents therefore have multiple rows, one for each question they encountered.

• respondent_id – Unique identifier for each participant.

• status – Final survey status (e.g., Completed, Terminated).

• question – Full wording of the question asked.

• reporting_id – The labelled identifier for the question as set in the dashboard (e.g., Age, Gender).

• line_number – The line number of the question in the survey script.

• type – Type of question (NumericQuestion, MultiChoiceQuestion, OpenEnd, etc.).

• response – The recoded, human-readable response category (e.g., 35-44).

• raw_response – The raw value stored (e.g., 36).

• responded – Indicates whether and in what order the respondent selected the option. 0 = not selected, 1 = selected first, 2 = selected second, and so on.

• timestamp – Time when the answer was submitted.

• weight – Weighting factor applied to this respondent’s answers for statistical adjustment.

Stored as one row with responded=1.

Stored as multiple rows per respondent per option. The chosen options have responded>0, with the number indicating the order in which the options were chosen. Unchosen options have responded=0.

Question: Which of the following fruits do you like? (Select all that apply)

Here, the respondent chose Apple first, Orange second, and did not select Banana.

Both response (bucketed/cleaned category, e.g. 35-44) and raw_response (e.g. 36) are provided.

The full text appears in response and raw_response.

• Non-responses may appear with responded=0 and empty raw_response.

• "Prefer not to say" or similar options appear as normal response categories.

• Terminated respondents may have partial rows depending on where they dropped out.

• Apply the weight column in analysis to ensure results reflect population targets.

• Use pivot tables (Excel) or groupby (Python/Pandas) to aggregate responses.

• For multi-choice questions, include all rows where responded>0 to capture all selected options. Use the order number if you need to analyze sequence of selection.

• When comparing across formats, match on reporting_id (long) to variable codes (wide/SPSS).

• For deep exploratory analysis.

• When handling multi-select or grid questions where wide format becomes cumbersome.

• When exporting data into R/Python for custom cleaning, text analysis, or advanced visualization.