1. Overview

The Wide format presents each respondent as a single row, with each survey question represented as a separate column. This format is particularly useful when you want to:

• Run statistical analysis or modeling in packages like SPSS, R, or Python.
• Use dashboards or BI tools that expect one record per respondent.
• Quickly view all answers for a respondent in one row.

2. File Structure & Layout

Each row corresponds to one respondent. Each column corresponds to one question or metadata field.

Example (first 5 columns):

V001_respondent_id	V002_Recent_Restaurant_Visit	V003_Age	V004_Gender	V005_Ethnicity
ps-0097ac15-868c-6608-25fc-c0fe2cd884a8	Yes	35	Female	White
ps-012a5d8b-9dc4-2132-782d-73742be4088f	Yes	21	Female	White
ps-022b6b43-6304-2dc5-0830-10d98bd7dee3	Yes	18	Male	White

3. Key Columns

• V001_respondent_id - Unique identifier for each respondent.
• Question columns (V###_...) - Each prefixed with a variable code (V###) followed by a short label. For example: V002_Recent_Restaurant_Visit
• V003_Age
• V004_Gender

The last three columns are always:

• Weight column (e.g., V171_weight) - The respondent's statistical weight.
• Timing columns (e.g., V172_start_time, V173_end_time) - Timestamps recording when the respondent started and completed the survey.

4. Data Representation

Single-choice questions

Stored as one column per question with the selected answer recorded.

Multi-choice questions

Each option is represented as a separate column. Values indicate whether the option was selected, and may also include rank order (e.g., -1 = not selected, 1 = selected first, 2 = selected second, etc.).

Numeric questions

Stored directly as numeric values (e.g., annual income).

Open-end questions

Stored as free-text responses in their own columns.

⚠️ Note: Recoded variables and coded open ends are not included in the Wide format. They are only available in the Long format.

5. Missing & Special Values

• -1 often denotes an option that was not selected in multi-choice or rank questions.
• Empty cells may indicate a skipped or non-applicable question.
• "Prefer not to say" appears as a standard response category.

6. Weighting

• Apply the weight column when analyzing results to ensure the dataset reflects the target population.

7. Best Practices

• Use variable codes (V###) for merging with codebooks or comparing across formats.
• Treat -1 values consistently as non-selections in analysis.
• For ranked multi-choice questions, filter out -1 and use positive values to analyze the order of selections.
• When comparing across formats, match on reporting_id (long format) to V### codes (wide format).
• Look to the Long format for recoded values and coded open ends, since these are not included in the Wide format.

8. Stacked Exports

The Wide format supports stacked exports, where the data is organized by the tags assigned to each question in the survey editor. In a stacked export, each respondent row is repeated for every tag group, and only the columns belonging to that tag are included alongside the respondent identifier and metadata columns (weight, timing).

This is useful when your survey covers multiple topics or when you want to analyze tagged sections independently. For example, if your survey tags questions as "Demographics," "Brand Awareness," and "Purchase Intent," the stacked export will produce a row for each respondent under each of those tag groups, with only the relevant question columns present in each row.

To generate a stacked export, select the Stacked option in the download dialog. The resulting file will contain a Tag column indicating which tag group each row belongs to.

9. When to Use Wide Format

• For statistical modeling and regressions.
• When using survey data in BI dashboards or visualization tools.
• When analysts want one record per respondent with all answers side by side.
• When using the stacked option, for analyzing tagged question groups independently or feeding structured sections into BI tools.