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Конвертировать DTA в SAV

БЫСТРЫЙ ОТВЕТ
Возможно ли конвертировать DTA в SAV?
Да - DTA конвертируется в SAV.

R with the haven package is the most reliable free path: load with read_dta('file.dta') and write with write_sav(df, 'output.sav'). Both variable labels and value labels carry over, which a CSV export would lose.

Также в SAV: SVE · SRM · RDATA

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Протестировано на macOS, Windows и Linux
Последняя проверка: Oct 2026

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Зачем конвертировать DTA в SAV?

Both .dta and .sav store rectangular datasets with variable labels and value labels, so the conversion maps well. Converting to .sav lets colleagues open the dataset directly in SPSS without installing Stata or going through an intermediate CSV that strips metadata.

Способы конвертации DTA в SAV

R + haven OPEN-SOURCE

Install haven, then run read_dta('file.dta') followed by write_sav(df, 'output.sav') to produce an SPSS-compatible file with labels preserved.

PSPP OPEN-SOURCE

Open the .dta via File > Import Data > Stata, then save as SPSS format via File > Save As. For complex datasets with many value labels, R + haven is more reliable.

SPSS APP

Open the .dta via File > Open > Data (set file type to Stata), then save via File > Save As in SPSS format.

Об этих форматах

Качество и на что обратить внимание

  • Variable and value labels carry over when using haven - Stata value labels become SPSS value labels in the output .sav.
  • Stata's extended missing values (.a, .b, ..., .z) become system-missing in SPSS and cannot be mapped to SPSS user-defined missing codes.
  • Newer Stata format files (format 117+, Stata 13 onward) are fully supported by haven; if PSPP rejects a newer file, use R + haven instead.

Часто задаваемые вопросы

Do I need a Stata or SPSS licence to convert DTA to SAV?
No - R + haven and PSPP are both free and handle the full conversion including labels.
Will value labels survive the conversion?
Yes, when using haven - Stata value labels become SPSS value labels in the output .sav.
What happens to Stata's extended missing values?
They become system-missing in SPSS; if your analysis depends on distinguishing .a, .b, etc., resolve them in R before writing the .sav.
Does it work with large datasets?
R + haven reads .dta files row by row and handles large files; PSPP is less tested on very large datasets with many variables.