How to open a .sav file
You have been sent a .sav file and you do not have SPSS. This is the single most common question people ask about statistical data files, and the good news is you can read the whole thing — data, variable labels, and value labels — in your browser right now.
What a .sav file is
A .sav file is the native data format of IBM SPSS Statistics. It stores a rectangular dataset: one row per case (a person, a survey response, an observation) and one column per variable. Crucially, it also stores metadata that a plain spreadsheet cannot: a descriptive label for each variable, a dictionary of value labels (so a column of 1s and 2s can mean "Male" and "Female"), declared missing-value codes, and display formats for dates and numbers.
Because the format is proprietary, double-clicking a .sav file does nothing useful unless SPSS is installed. But the format has been thoroughly documented and reimplemented, which is why a browser-based reader can open it faithfully.
Open it in your browser (no install)
The fastest route is the statfile.tools viewer. Drag the .sav file onto the page and it renders as a familiar data grid, with a separate variable view that lists every variable, its label, its type, and its value labels — the same two-pane layout SPSS itself uses.
Nothing is uploaded. The file is decoded locally by JavaScript, so even sensitive or restricted-use data stays on your machine. When you are done, you can export to CSV, Excel, or JSON, or generate a printable codebook.
Other ways to open a .sav file
If you prefer desktop software, PSPP is a free, open-source program that reads and writes .sav files and mirrors much of SPSS's functionality. In R, the haven package (read_sav) or the older foreign package will import a .sav into a data frame. In Python, pandas.read_spss (backed by pyreadstat) does the same. Each of these is excellent but requires installing software and, in the case of R and Python, writing a few lines of code.
For a one-off file, or when you simply need to see what is inside and pull the data into a spreadsheet, the browser viewer is usually the quickest path.