SPSS .sav to CSV/JSON Converter

Convert SPSS .sav data files to CSV or JSON. Preserves variable labels, value labels and metadata. No SPSS license needed.

1.0.0
Version
Auth
Batch

About SPSS .sav to CSV/JSON Converter

SPSS .sav is the dominant data format in academic social science, survey research and clinical trial analysis. It's also a binary proprietary format that nothing outside IBM SPSS or PSPP natively reads. So if you receive a dataset from a collaborator, an open-data repository (ICPSR, OpenICPSR, the European Social Survey) or a university course, your options to actually load it are: install SPSS (paid), install PSPP (works but old-school CLI), install R with haven or Python with pyreadstat (works but requires the language toolchain), or convert it once.

This SPSS .sav converter handles the third path without the toolchain. Upload your .sav, get CSV (Excel-ready) or JSON (for scripts) back, with variable labels and value labels preserved alongside the numeric data. Variable labels are the human-readable descriptions ("Age at first child birth") attached to terse variable names (AGEFCB); value labels are the categorical mappings (1 = "Strongly agree", 2 = "Agree", ...) for coded responses. Without these, a survey dataset is a wall of integers; with them, the CSV is immediately readable. Optional include_labels toggle controls whether the export uses the labels (great for reading) or the underlying codes (great for re-importing into a statistical pipeline). Files up to 50 MB.

Use it to load a survey dataset into Excel without buying SPSS, share an open-data .sav with a collaborator who uses pandas or R but not SPSS, audit a coded dataset's value-label mappings, prep a CSV for a Tableau dashboard, or just open one .sav someone forwarded without installing PSPP. Files are processed in a stateless serverless function and discarded immediately after the response.

SPSS .sav to CSV/JSON Converter Use Cases

  • Graduate students opening an SPSS dataset for coursework without buying SPSS
  • Researchers sharing ICPSR / OpenICPSR datasets with pandas or R collaborators
  • Auditing value-label mappings in a coded survey before regression analysis
  • Loading social-science survey CSVs into Tableau or Power BI dashboards
  • Migrating from SPSS to a modern Python or R statistical pipeline
  • Quick decoder for a .sav a colleague emailed without firing up PSPP
  • Survey ops teams converting field-data .sav files for downstream ETL pipelines

SPSS .sav to CSV/JSON Converter Features

  • Reads SPSS .sav (PSPP-compatible) data files and emits CSV or JSON in one pass
  • Variable labels preserved — the human-readable descriptions attached to terse variable names like AGEFCB
  • Value labels preserved — categorical mappings like 1='Strongly agree', 2='Agree' for coded responses
  • include_labels toggle — emit human labels for readability or underlying codes for statistical pipeline re-import
  • CSV output uses UTF-8 BOM so Excel renders non-ASCII variable names and value labels correctly on double-click
  • JSON output mirrors the structure for downstream pandas, R or any statistical pipeline that expects structured data
  • Files up to 50 MB processed in a stateless serverless function and discarded immediately after the response

How to Use SPSS .sav to CSV/JSON Converter

Upload your .sav file

Drag-and-drop or click to select an SPSS .sav data file (up to 50 MB). Files saved by SPSS 14 onwards, PSPP and third-party exporters that follow the .sav spec all work.

Pick CSV or JSON

CSV opens straight in Excel, Sheets, LibreOffice, R via read.csv, pandas via read_csv. JSON is the right choice for direct script loading or for tools that prefer structured input.

Decide on labels

Tick include_labels (default) to emit human-readable value labels — '1' becomes 'Strongly agree', '2' becomes 'Agree'. Untick to keep the underlying integer codes — required when the destination is a statistical pipeline that expects raw codes.

Click Convert

The serverless parser reads the .sav header, extracts variable metadata (name, type, label, value labels) and iterates over the data records. Typical 100-variable 10000-respondent survey converts in under a few seconds.

Download and load

CSV: open in Excel, or pandas.read_csv('file.csv') in Python, or read.csv('file.csv') in R. JSON: pandas.read_json or json.load(). Variable labels show as the first comment row in the CSV or as a metadata block in the JSON.

SPSS .sav to CSV/JSON Converter FAQ

No. The file is uploaded to a stateless serverless function, parsed, and discarded immediately after the response. Nothing is logged to durable storage. For highly confidential clinical-trial data, the same conversion runs locally with pyreadstat or haven in a one-line script — that's the safer path when transit is a concern.

Yes. Variable labels (the human-readable description attached to each variable name) and value labels (the categorical mapping for coded responses) are both read from the .sav header. With include_labels on (default), the CSV uses the labels — '1' becomes 'Strongly agree'. With include_labels off, raw codes are emitted, which is what you want for re-importing into another statistical pipeline.

Files saved by SPSS 14 onwards, PSPP at any version, and third-party exporters that follow the published .sav specification. The .sav format has been stable since the early 2000s, so the vast majority of files in the wild parse cleanly. Very old (pre-SPSS-14) files may need conversion through PSPP first.

50 MB. This covers most survey research datasets — a 100-variable 10,000-respondent dataset is usually well under 5 MB, and 50 MB easily fits a longitudinal panel study with thousands of variables. Very large institutional datasets may need chunking or local pyreadstat scripting.

Yes. String variables come through with their original values and encoding (UTF-8 in the output regardless of the .sav's internal encoding). Missing-value codes (SPSS user-defined missing and system missing) are preserved as empty cells in CSV (Excel convention) and as null in JSON, which is what pandas, R and most downstream tools expect.

pyreadstat (Python) and haven (R) are the right answer when you're building a statistical pipeline and want to read .sav directly. This is the bridge tool for everyone else — open the .sav once, get a CSV or JSON, use that. Particularly useful when collaborators don't have Python or R set up, or when the destination is Excel rather than a script.

No — this only handles .sav data files. SPSS syntax .sps is a code file, not data, and converting it to another language's syntax (R, Python) requires semantic translation. The closest path is to read your .sav into pandas or R and rewrite the analysis steps in that language.

Upload your SPSS .sav file

.sav format • Max 50MB

Requires login • 1 credit

SPSS .sav to CSV/JSON Converter Tutorial

What is SPSS .sav?

SPSS (Statistical Package for the Social Sciences) .sav is a binary data format used by IBM SPSS Statistics. It's widely used in academic research, social sciences, market research, and healthcare. The format stores data along with variable labels, value labels, and metadata that plain CSV cannot capture.

Why Convert?

  • Open SPSS data without an SPSS license ($99+/month)
  • Import into Excel, Google Sheets, R, or Python
  • Share data with colleagues who don't have SPSS
  • Archive research data in a universal format

What Gets Extracted?

  • All data rows and columns
  • Variable labels — human-readable descriptions of column names
  • Value labels — coded values mapped to labels (e.g. 1→"Male", 2→"Female")
  • Data types — numeric, string, date
  • Missing value counts

CSV vs JSON Output

  • CSV — Best for Excel/Sheets. Flat table, easy to open anywhere.
  • JSON — Includes full metadata (variable labels, value labels, file info). Best for programmatic use.

Value Labels Option

"Apply labels" replaces coded numbers with their labels (1→"Strongly Agree"). "Keep raw codes" preserves the original numeric values for statistical analysis.