Guides
Understand the formats
Short, practical guides to SPSS and Stata data files — what they contain, how to open them without the original software, and how they differ.
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.
Read guideHow to open a .dta file
A .dta file is a Stata dataset, and like SPSS's .sav format it is proprietary. If you have received one but do not own Stata, you can still open it, read its labels, and export the data without installing anything.
Read guideSPSS .sav vs Stata .dta
SPSS .sav and Stata .dta files solve the same problem in similar ways: both store a rectangular dataset together with the labels and metadata that give it meaning. The differences are in the details, and they matter when you are converting between them.
Read guideHow to open a .sav file in R
R reads SPSS files well — the modern route is the haven package, which preserves variable labels and value labels as attributes. Here is the short version, what the labelled columns mean, and a no-install fallback when you just need to see the data.
Read guideHow to read a .dta file in Python
Python has two solid readers for Stata files: pandas' built-in read_stata() and the pyreadstat library, which exposes more metadata. Here is how to use each, how to keep the labels, and a browser-based fallback for quick looks.
Read guideHow to open a .sav file in Excel
Excel has no built-in support for SPSS files: double-clicking a .sav shows gibberish or an import error. The practical route is a quick conversion to .xlsx — and if you do it with a tool that understands SPSS metadata, you keep the labels instead of a wall of numeric codes.
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