Quick Answer
How do you clean questionnaire data before SPSS analysis?
Import the dataset, check that every variable has the correct type and coding, review frequency tables for impossible values, define missing values consistently, identify duplicates, inspect outliers and verify reverse-coded items before creating scale scores or running statistical tests.
Prepare Your Survey File Before Importing
Start with one row per respondent and one column per variable. Use short variable names without spaces, keep the first row for variable names, and avoid merged cells, totals or notes inside the dataset. If the survey was exported from Google Forms, Qualtrics or another platform, save an untouched original copy before editing anything.
Create a separate codebook showing the variable name, question wording, response options and numeric coding. For example, study-level categories should have clear codes, while Likert items might use a consistent scale such as 1 to 5. A codebook reduces mistakes later when SPSS output contains short variable names.
Import Excel or CSV Data Into SPSS
Open the import option
In SPSS, choose the option to open data from Excel, CSV or another supported file.
Select the correct sheet
If the workbook has several sheets, choose the sheet containing the raw respondent-level data.
Use the first row as variable names
Check that SPSS recognises the header row correctly rather than treating it as a respondent.
Inspect Data View
Confirm that rows and columns imported as expected and numeric values were not converted to text.
Save an SPSS copy
Save the imported file as an SPSS data file before making substantial cleaning changes.
If a column contains both numbers and text, SPSS may assign an unexpected variable type. Fix the source data or recode the variable carefully rather than continuing with a mixed-format field.
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Check Variable View Before Analysis
| Setting | What to check | Why it matters |
|---|---|---|
| Type | Numeric, string, date or another suitable format | Statistical procedures need appropriate data types |
| Label | A clear description of the question or construct | Makes output easier to interpret |
| Values | Labels for category codes | Prevents confusion in tables |
| Missing | Special missing codes such as 99 or -999, if used | Stops special codes being treated as real scores |
| Measure | Nominal, ordinal or scale as appropriate | Improves organisation and some SPSS dialogs |
The Measure setting does not automatically decide which statistical test is correct, but entering it accurately helps keep the dataset organised.
Use Frequencies to Find Coding Errors
Run frequency tables for categorical and Likert variables before calculating means or reliability. Look for values outside the permitted range. If a five-point item contains a 7, determine whether it is a data-entry error, a special code or a response created by an incorrect export.
Check minimum and maximum values for continuous variables such as age, income or study hours. An age of 222 or a negative number of weekly study hours should be investigated. Do not simply delete unusual values without checking the original response or explaining the cleaning rule.
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Check Missing Values and Duplicate Cases
Distinguish true missing cells from special numeric codes used to represent missing answers. If 99 means “no response,” define or recode it so SPSS does not include 99 when calculating a mean. Review the amount and pattern of missing data before deciding how to handle it.
Also inspect duplicate IDs, timestamps or other identifiers where appropriate. A duplicate may represent a repeated submission, but two respondents can legitimately share similar characteristics. Use evidence from the original survey system before removing a case.
Verify Reverse-Coded Items Before Creating Scales
If a questionnaire contains negatively worded items, check whether they must be reverse-scored before calculating a total or average scale score. For a 1-to-5 item, reverse coding normally changes 1 to 5, 2 to 4, 3 to 3, 4 to 2 and 5 to 1.
Create a new variable rather than overwriting the original response. Then compare frequencies of the original and recoded versions. Incorrect reverse coding can reduce reliability and change the direction of relationships in later analyses.
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Keep a Data-Cleaning Log
Record every important decision: which cases were removed, which values were corrected, which missing codes were defined and which items were reverse-scored. The log can be a simple spreadsheet with the date, variable, issue, action and reason.
This record helps you write the methodology chapter and protects you from making undocumented changes. Keep the raw file, cleaned file and analysis file as separate versions instead of repeatedly overwriting one dataset.
Common SPSS Data-Cleaning Mistakes
- Running regression or reliability before checking the imported variables.
- Leaving missing-value codes such as 99 inside scale calculations.
- Deleting outliers automatically because they look extreme.
- Recoding an item without keeping the original variable.
- Using unclear variable names and no codebook.
- Changing data directly without documenting what changed and why.
Key Takeaways
- Keep an untouched copy of the raw survey export.
- Use one row per respondent and one column per variable.
- Check Variable View, value labels and missing-value codes before analysis.
- Run frequencies and descriptive checks to find impossible values.
- Verify reverse-scored items before creating scale totals.
- Document every cleaning decision in a simple audit log.