The .sav file.
Also .zsav, .por, which are read the same way.
- Carries
- Variable labels, value labels and declared missing codes
- Variable name limit
- 64 characters in modern files
- Missing values
- Declared per variable, often 99, 998 or blank
- Variants
- .zsav compressed, .por portable
- Read here as
- Direct. A 5 stays "Strongly agree" and a 99 stays missing.
- Status
- Ready
- Fidelity
- Read directly
- Largest file
- 25 MB free, 250 MB once a project is bought
What it actually is
Variable labels, value labels and missing-value codes are all read, so a 5 stays “Strongly agree” and a 99 stays missing.
An SPSS data file stores the values and the codebook together. Each variable has a label describing what the question actually asked, each coded value has a label describing what the code means, and specific values are declared as missing rather than merely being absent.
This is the difference between a column called Q7 holding a 5, and a column called "How satisfied were you with the response time" holding "Strongly agree". Both are in the file. Only one survives an export to CSV.
Compressed .zsav and portable .por files are the same data in different containers, and fold into this page. Stata's .dta carries the same kind of metadata and has its own page.
Upload one
The analysis runs before there is anything to pay for.
You see what it found, and the evidence behind it, first.
No account needed to start. You only pay when you like what you see.
What goes wrong, and what is done about it
3 named failures, each with the repair.
Missing codes averaged as data
A survey coding "prefer not to say" as 99 and "not applicable" as 98 produces a mean satisfaction score of 14 on a five-point scale if those codes are treated as numbers. The error is enormous, obvious in hindsight, and extremely common in files that were exported to CSV first.
What is done: Declared missing values are honoured as missing. A column whose distribution shows the signature of undeclared sentinel codes is flagged even where the file does not declare them.
Reverse-coded items averaged with the rest
A scale mixing "The service was fast" with "I had to wait too long" needs the second recoded before anything is averaged, or the two cancel each other out and the scale measures noise.
What is done: Nothing here infers polarity. A reversed item is read exactly as it is coded, so recode it in the source before uploading, or keep the reversed items out of any scale that gets averaged.
Multiple response sets that are not a single variable
A select-all question is stored as one binary variable per option. Treated as separate questions, the percentages do not sum to anything meaningful and every option looks like a minority view.
What is done: Nothing detects a multiple-response set. Each binary variable arrives as its own column and every percentage is per column, so the base of respondents is yours to establish before a share is quoted.
If you have the choice, send something else
Send the .sav itself rather than an export. A CSV of the same data has lost the labels, the missing codes and the measurement level, and re-typing a codebook is both slow and where errors enter.
Questions people ask
About this format, not about the product.
Do I need SPSS installed?
No. The .sav file is read directly, including its value labels and its missing-value codes, so a 5 stays “Strongly agree” and a 99 stays missing: none of which survives an export to CSV. The variable labels are not applied, though: a column called Q7 stays called Q7 rather than becoming the question it asked.
What about partial responses?
They are kept at the last answered question by default, because discarding them silently removes the people most likely to be unhappy. How they were handled is stated, and reversing the decision is one instruction.
Will it tell me a difference is not significant?
Yes. A finding here is allowed to lose: a difference is tested, and one that does not clear the test after the number of segments compared is taken into account is reported as not significant rather than swapped for a better-looking one.
Keep reading
The jobs people do with this file, and the formats beside it.