Free online XLS to SAV converter

A legacy workbook may need conversion when its records must be analyzed in IBM SPSS Statistics or another application that requires an .sav dataset. The conversion transfers spreadsheet values into a statistical format, but spreadsheet layout and formulas require separate checks.

What the XLS format is

.xls is the binary Microsoft Excel workbook format used by Excel 97–2003 and compatible applications such as LibreOffice Calc and Apache OpenOffice Calc. It can contain multiple worksheets, formulas, formatting, charts, merged cells, and mixed data types.

XLS files remain common in older survey exports, business reports, laboratory records, and document archives. A workbook is not automatically a clean statistical dataset: title rows, notes, merged cells, blank spacer rows, and presentation formatting may need to be removed before import.

What the SAV format is

.sav is the native data-file format of IBM SPSS Statistics. It stores cases as rows and variables as columns, together with variable names, variable labels, value labels, user-defined missing values, display formats, and measurement levels.

SAV is suited to statistical analysis in IBM SPSS Statistics and can also be opened by GNU PSPP and imported by several other statistical programs. Its metadata makes it more suitable than CSV when categorical labels, missing-value definitions, and variable properties must accompany the data.

Convert with IBM SPSS Statistics

1. Open IBM SPSS Statistics and choose File → Open → Data.
2. Set the file type to Excel, select the .xls workbook, and open it.
3. In the import dialog, select the worksheet containing the dataset.
4. Enable Read variable names from the first row of data only when the first row contains valid column names. Disable it when the first row is a data record.
5. Inspect the imported columns in Variable View. Correct variable names, data types, labels, date formats, measurement levels, and missing-value definitions.
6. Choose File → Save As, select SPSS Statistics (*.sav), enter a filename such as survey_data.sav, and save.

For repeated imports, SPSS syntax can perform the conversion:

GET DATA
  /TYPE=XLS
  /FILE='C:\data\survey.xls'
  /SHEET=name 'Sheet1'
  /READNAMES=ON.
EXECUTE.
SAVE OUTFILE='C:\data\survey.sav'.

Change the worksheet name, paths, and /READNAMES setting to match the workbook. Use an appropriate path format on macOS or Linux.

Free and commercial alternatives

Using R

R can read legacy Excel files with readxl and write genuine SAV files with haven. The older foreign::write.foreign() function does not directly create a normal SAV file; it produces a data file plus an SPSS syntax file, so it is not the appropriate export function here.

install.packages(c("readxl", "haven"))
library(readxl)
library(haven)

data <- read_excel("survey.xls", sheet = "Sheet1")
write_sav(data, "survey.sav")

R imports the selected worksheet only. Add or correct labels, missing-value codes, and measurement properties in R before writing the file if the workbook does not contain that metadata.

Using Stat/Transfer

Stat/Transfer is a commercial conversion utility designed for spreadsheet and statistical formats. Select Excel as the source, choose the workbook and worksheet, select SPSS as the destination, and run the transfer. It is useful for batch work and format mapping, but inspect the resulting variables before analysis.

Using GNU PSPP

GNU PSPP can import spreadsheet data in versions and builds that provide spreadsheet import support. Open the workbook through File → Open or File → Import Data, verify the variables, then use File → Save As and select SAV. Compatibility with older XLS features varies; IBM SPSS Statistics is the safer choice for complex workbooks and detailed metadata.

Quality and compatibility checks

• One worksheet produces one dataset. Select and convert each required worksheet separately; sheets are not automatically combined.
• Excel formulas generally import as their stored results, not editable formulas. Recalculate the workbook in Excel before conversion if results may be stale.
• A mixed-type column may become a string variable, or incompatible values may become missing. Check numeric and string types after import.
• Verify dates and times. Excel date serials, cell formats, and the workbook's 1900 or 1904 date system can affect interpretation; time-zone information is not reliably represented in XLS.
• Blank cells usually become system-missing values in numeric variables and blank values in string variables. They are not automatically changed to study-specific codes such as 99.
• SPSS may alter Excel column names to satisfy variable-name rules. Check for invalid characters, truncation, reserved names, and unintended duplicates.
• Charts, formatting, merged cells, hidden rows, and page layout do not belong to the SAV data table. Remove report material and confirm whether hidden rows should be included.
• Compare row counts, column counts, totals, date ranges, and selected values between the XLS source and SAV output before analysis.