Survey data exports.
Ready for your analysis.
Use coded responses, a codebook and import scripts to take authorized survey data into SPSS, Stata, R or Python.
Wide data · Long data · Exact labels · Recorded review decisionsWhat the analysis package includes
Open an authorized survey’s Report area, choose the intended response view, then select Download analysis package in the data downloads. Start with README.txt; it records the scope and explains the files. Membership and account permissions determine availability.
| File | Use it for |
|---|---|
responses.xlsx | Inspect coded responses, the long answer table, codebook and export details in one workbook. |
responses_wide.csv | One row per response, with stable coded column names for statistical analysis. |
answers_long.csv | One row per answer or matrix item when a long structure is more useful. |
codebook.csv | Match question wording, variable labels and answer codes to the data. |
responses.json | Retain the original exported answer values, including text protected when opened as CSV. |
responses_wide.json | Supply exact typed values to the native-format conversion script. |
| Import and conversion scripts | Open the data in SPSS, Stata or R, or create native files locally with Python and its listed dependencies. |
Multiple selections use separate 0/1 columns; matrices use columns for rows or row-and-option combinations. A blank means no answer was recorded and can represent an unanswered or unshown question. The collected record does not distinguish those two situations. Check the codebook before treating blanks as zero.
How to import survey responses into SPSS
- Extract the package and keep its files together. Inspect the response count, selected view and codebook.
- Open
import_spss.spsin SPSS. Set SPSS’s working directory to the extracted folder soresponses_wide.csvcan be found. - Review the variable definitions and labels in the syntax. Run the import, then verify several known answers against the CSV and codebook.
- The syntax saves
survey_data.sav. Check numeric values, multiple-selection indicators, non-English text and missing values before starting an analysis.
The download contains an SPSS import script. It does not supply a ready-made native SAV file directly from the website. SPSS can create the SAV file by running the syntax, or the included Python converter can create it using the documented local dependencies.
Stata, R, Python and Parquet
import_stata.do reads the wide CSV, applies variable and value labels, and saves survey_data.dta. Run it from the extracted folder and review the labels and data types after import.
import_r.R reads the CSV and codebook and saves an RDS file. Its optional Parquet step needs the R arrow package. The Python script, make_native_formats.py, uses pandas, pyreadstat and pyarrow to produce SAV, DTA and Parquet files from exact typed values. Read and approve scripts under your organization’s normal software process before running them.
Opening a file successfully is only the first check. Reconcile row counts, labels, special characters and a small set of expected values. If a receiving tool has a format or variable-name restriction, document the conversion instead of silently changing the measure.
Agree these checks with the receiving analyst
- Scope: survey version, export date, selected view, filters and response count.
- Definitions: exact wording, scale direction, code labels, matrix structure and multiple-selection rules.
- Missing answers: which records have no answer and how the analysis will treat them.
- Quality review: the package retains excluded records with review status, decision reasons and flag explanations. Apply the intended inclusion rule explicitly.
- Privacy: confirm who may receive individual responses, whether consent permits the handoff and how the file will be protected.
- Reconciliation: check a known question’s counts against the dashboard using the same scope and review decisions.
CSV text starting with a spreadsheet formula character is protected with an apostrophe for safer opening. Original text is retained in JSON. Use the original values where exact text is required and inspect them through an appropriate data tool.
Download the plain-text survey data handoff checklist to record these decisions. It contains no respondent data and can be adapted for your project.
Common questions
Is an analysis package the same as an Excel report?
No. An Excel summary or table book presents aggregated question tables. The authorized analysis package contains individual response rows and a codebook for further work. Confirm which output the recipient needs.
Do quality flags automatically remove a response from the package?
No. The raw package retains original records and their recorded review status. A flag is a prompt for review. The receiving analyst should apply and document the agreed inclusion rule.
Can I calculate a percentage immediately from the imported columns?
First check which people were eligible to answer and how blanks are represented. A multiple-selection percentage usually uses people as its base, while long data can contain several rows per person. Our denominator guide explains those differences.
Where are the product instructions?
The analysis-package Help article describes the product steps. For a client presentation, see the editable PowerPoint report workflow. Use an analysis plan to agree the measures before collecting responses.
Product workflow reviewed 26 October 2026 by the Survey Studio team. Examples describe file handling; they do not establish research findings or customer outcomes.
From collection to a clear handoff.
Try fictional sample results, review current plans or arrange a walkthrough with your research task.