How to Export Clean Survey Data to SPSS

Exporting survey data to SPSS is not just about downloading a file and opening it in SPSS. The real task is turning survey responses into a dataset that is readable, labeled, consistent and ready for analysis.

A dataset may look fine at first glance, but once it is opened in SPSS, problems quickly become visible:

• variable names are unclear, too long or inconsistent,
• question text has not been carried into Variable Labels,
• answer options appear only as numeric codes,
• those numeric codes do not have clear Value Labels,
• multiple-choice questions are collapsed into one text cell,
• open-ended responses are not handled cleanly,
• unanswered questions and logic-skipped questions are mixed together,
• multilingual survey versions create separate variables instead of one shared structure,
• there is no codebook, making the dataset hard to understand across a team.

This guide explains how to export survey data to SPSS cleanly, with a focus on Variable Labels, Value Labels, Codebook structure, missing values, multiple-choice questions, open-ended responses and multilingual survey exports.

What does clean survey data mean in SPSS?

Clean survey data for SPSS means that every survey question becomes the right variable, every answer option is coded consistently and the research team can understand the dataset without guessing what each column means.

A clean SPSS survey dataset should include:

• one consistent variable name for each question,
• a clear Variable Label for each variable,
• correct Value Labels for closed-ended responses,
• separate text variables for open-ended responses,
• analyzable binary variables for multiple-choice questions,
• clear coding for Branching / Skip Logic gaps,
• readable value labels for demographic variables,
• language variables such as LANGUAGE for multilingual surveys,
• country, segment, channel or campaign variables where relevant,
• a Codebook that documents the dataset.

SPSS is a powerful analysis tool. But if the data entering SPSS is messy, the analysis will be messy too. Data Export should not be treated as a final technical step after the survey is finished. It should be considered from the start of the research design process.

The common mistake: thinking about SPSS export too late

Many teams design a questionnaire, publish it, collect responses and only start thinking about SPSS when the analysis phase begins. That is when the problems appear.

For example:

• Question IDs may have been generated randomly.
• The same question may appear as different variables across language versions.
• Scale options may be inconsistent.
• “Other” text responses may not be separated properly.
• Multiple-choice responses may be stored in one text field.
• Unanswered questions, skipped questions and “prefer not to answer” options may be mixed together.
• No codebook may have been created.

These problems cannot always be fixed at export time. Some require manual cleaning, which increases time, error risk and uncertainty in the analysis.

The better approach is simple: think about SPSS Export while designing the survey, not after collecting the data.

Variable Name, Variable Label and Value Label

Three SPSS concepts matter especially for survey exports:

1. Variable Name
2. Variable Label
3. Value Label

If these are confused, the dataset becomes harder to read and harder to analyze.

What is a Variable Name?

A Variable Name is the short technical name of a variable in SPSS. It works like a column name.

Examples:

GENDER
AGE_GROUP
EDUCATION_LEVEL
SATISFACTION_1
INCOME_BEFORE
INCOME_AFTER

A good Variable Name should be short, consistent and usable in analysis.

A poor example would be:

HOW_SATISFIED_ARE_YOU_WITH_THIS_SERVICE_PLEASE_RATE_FROM_1_TO_5

That is a question text, not a good variable name.

A better version:

SERVICE_SATISFACTION

In PublicOp’s SPSS Export workflow, variable names are generated from the Question ID or a custom SPSS code if the survey designer has defined one. If not, the system automatically generates an ID from the text. To stay compatible with SPSS, special characters and spaces are replaced with underscores, names are converted to uppercase and variable names are limited to 64 characters.

What is a Variable Label?

A Variable Label is the longer description that explains what the variable measures.

Example:

Variable Name:
SERVICE_SATISFACTION

Variable Label:
How satisfied are you with this service overall?

The Variable Name is short and technical. The Variable Label is descriptive.

In PublicOp, Variable Labels are generated automatically from question text. In multilingual surveys, if a Preferred Language is selected during export, Variable Labels are taken from the question text in that language. If no Preferred Language is selected, the survey’s default language is used. To comply with SPSS limits, Variable Labels are truncated at 255 characters.

What is a Value Label?

A Value Label explains what a numeric response code means.

Example:

Variable Name:
GENDER

Values:
1 = Female
2 = Male
3 = Prefer not to say

The numbers 1, 2 and 3 are the values used in analysis. “Female”, “Male” and “Prefer not to say” are the Value Labels.

In PublicOp, answer options are converted into numeric values. If the user has defined an SPSS Code, that code is used. If no code is defined, the system generates a stable numeric code for each Option ID. Option text becomes the Value Label, using the selected export language. Because of SPSS constraints, Value Labels are limited to 120 characters.

Why a real .sav file matters

There are several ways to move survey data into SPSS. CSV and Excel files can be imported, but they often do not preserve the metadata researchers need for efficient analysis. Professional survey analysis usually requires more than a table. It requires labels, codes and structure that SPSS can understand.

PublicOp’s current export architecture generates a real .sav file. It uses the sav-writer library to create a native binary .sav export. This means a separate SPSS syntax file is not required.

PublicOp supports:

• CSV Export,
• Excel Export,
• SPSS Export,
• native .sav file generation.

CSV and Excel are useful when teams want a flat table. SPSS Export is better when researchers need Variable Labels, Value Labels and analysis structure carried into SPSS more cleanly.

How different question types are exported

Different question types should not be represented in SPSS in the same way. The export structure depends on the question type.

Single Choice and Dropdown

Single-choice questions become one variable. The selected answer is stored as a numeric code, while the option text becomes the Value Label.

Example:

EDUCATION_LEVEL
1 = Primary education
2 = High school
3 = University
4 = Graduate degree

Multiple Choice

Multiple-choice questions are exported as separate binary variables, one for each option.

Example:

CHANNEL_TWITTER
CHANNEL_EMAIL
CHANNEL_WEBSITE
CHANNEL_FRIEND

Each column uses:

1 = Selected
0 = Unselected

This structure is useful for frequencies and crosstabs in SPSS.

Likert, Rating and Star questions

Likert, rating and star questions are exported as numeric values. For example, a 1-to-5 satisfaction scale appears as numeric values in SPSS.

Example:

1 = Very dissatisfied
2 = Dissatisfied
3 = Neutral
4 = Satisfied
5 = Very satisfied

The measurement level should still be checked by the researcher. PublicOp currently marks numeric variables as Nominal in SPSS. Researchers should manually change relevant variables to Ordinal or Scale inside SPSS.

Matrix questions

For matrix questions, each row becomes a separate variable.

Example:

MATRIX_SPEED
MATRIX_PRICE
MATRIX_SUPPORT

This allows each row to be analyzed independently.

Short Text and Long Text

Short and long text responses are exported as string variables. However, because of SPSS .sav string limits, text may be truncated at 254 characters. Long narrative responses may therefore be cut.

This is an important limitation. If a study collects long qualitative responses, researchers should check CSV or Excel exports alongside the SPSS export to preserve the full text.

Audio Response

Audio responses collected with AudioRecorder are not exported to SPSS as audio file links. Since the system transcribes the audio, the export includes the transcript text as a string variable.

Example column:

EXPERIENCE_AUDIO_TRANSCRIPT

Example content:

I found the application useful overall, but some screens felt slow.

The audio file itself is not included in SPSS. The transcript text is included as analysis data.

How multiple-choice questions should be handled in SPSS

Multiple-choice questions require special care in SPSS exports.

Example question:

How did you hear about this service?

- Social media
- Email
- Website
- Friend or colleague
- Other

Storing this answer in one cell like this is weak for analysis:

Social media, Email, Website

A better structure creates one binary variable per option:

CHANNEL_SOCIAL_MEDIA     1 = Selected, 0 = Unselected
CHANNEL_EMAIL            1 = Selected, 0 = Unselected
CHANNEL_WEBSITE          1 = Selected, 0 = Unselected
CHANNEL_FRIEND           1 = Selected, 0 = Unselected
CHANNEL_OTHER            1 = Selected, 0 = Unselected

PublicOp exports multiple-choice questions this way: every option becomes its own 0-1 column.

There is one important limitation. PublicOp does not automatically define these columns as SPSS Multiple Response Sets. The researcher should manually use “Define Multiple Response Sets” in SPSS.

The distinction is important:

• PublicOp generates analyzable binary columns.
• The SPSS Multiple Response Set definition is handled manually by the researcher.

How to handle “Other” responses

“Other, please specify” is common in survey data. But if it is not structured properly, it creates problems during analysis.

In PublicOp, the “Other” option usually appears with the numeric code 99999. The text entered by the respondent is stored as a separate or combined string column.

Example:

CHANNEL
1 = Social media
2 = Email
3 = Website
99999 = Other

CHANNEL_OTHER_TEXT
"I saw it through a local association announcement."

This structure allows both closed-ended analysis and separate review of open-ended “Other” responses.

How open-ended responses should be stored in SPSS

Open-ended responses are different from numeric survey answers. They are usually exported as string variables.

Examples:

OPEN_FEEDBACK
FINAL_COMMENT
EXPERIENCE_NARRATIVE
OTHER_EXPLANATION

For open-ended responses, researchers should remember:

• text is exported as string data,
• SPSS .sav limits strings to 254 characters,
• multilingual open-ended responses remain in the language used by the respondent,
• no automatic translation is applied,
• automatic thematic coding is not included in the export,
• sentiment analysis is not included as ready-coded export data.

This is especially important for long qualitative responses. If a study collects narrative answers, SPSS export alone may not be enough. CSV or Excel export should also be checked to preserve longer text.

PublicOp carries open-ended answers as raw data. Turning them into themes, categories or qualitative codes remains part of the researcher’s analysis plan.

Missing values and Branching / Skip Logic

Missing values are unavoidable in survey data. But not every blank cell means the same thing.

Three cases should be separated:

1. The respondent saw the question but did not answer.
2. The respondent never saw the question because of Branching / Skip Logic.
3. The respondent selected a valid option such as “prefer not to answer” or “don’t know”.

If these are treated the same way, the analysis can become misleading.

PublicOp makes a useful distinction:

-98 = Unanswered
The respondent saw the question but left it unanswered.

-99 = System Missing
The respondent did not see the question because of Branching / Skip Logic.

This distinction is especially valuable in surveys with complex routing. For example, if respondents without children are not shown child-related questions, those blanks do not mean refusal or nonresponse. The questions were not applicable to them.

There is one limitation. PublicOp does not automatically define -98 and -99 as user-missing values inside SPSS. After opening the file, the researcher should manually define these values as missing in SPSS where appropriate.

This should be part of the post-export quality check.

Are “Prefer not to answer” and “Don’t know” missing values?

Not always. These options can be valid response categories or values that the researcher chooses to treat as missing. The decision depends on the research design.

Example:

1 = Yes
2 = No
8 = Don’t know
9 = Prefer not to answer

Here, 8 and 9 are technically answered values. The researcher may report them as separate categories or exclude them from certain analyses.

PublicOp does not automatically mark these options as user-missing. This is appropriate, because “don’t know” does not mean the same thing in every study.

The researcher should decide:

• Is “Don’t know” a meaningful opinion category?
• Should “Prefer not to answer” be reported separately for sensitive questions?
• Should these values be excluded from analysis?
• Should they be defined as missing?

This decision should be documented in the Codebook.

SPSS export for multilingual surveys

SPSS export becomes more sensitive when a survey is multilingual. The same question appears in multiple languages, but it should still be analyzed as one variable.

Example:

EN: How satisfied are you with this service?
TR: Bu hizmetten ne kadar memnunsunuz?
DE: Wie zufrieden sind Sie mit diesem Service?
FR: Dans quelle mesure êtes-vous satisfait de ce service ?

These should not create four separate variables. They should be treated as language versions of the same variable.

In PublicOp, multilingual surveys are managed under the SurveyTemplate structure, so responses from different language versions are exported into the same .sav dataset. The respondent’s survey language is included in the export as a LANGUAGE column.

If the researcher selects a Preferred Language during export, Variable Labels and Value Labels are generated in that language. If no Preferred Language is selected, the survey’s default language is used.

This supports:

• language-level frequencies,
• crosstabs by language group,
• country and language comparisons,
• multilingual sample balance checks,
• analysis of the whole study in one dataset.

The key point is this: label language can change, but the structural ID logic remains the same.

Why measurement level should be checked manually

In SPSS, variables are usually treated as one of three measurement levels:

• Nominal,
• Ordinal,
• Scale.

This matters for survey data.

Examples:

Gender: Nominal
Education level: Ordinal
Age: Scale
Likert scale: often Ordinal, sometimes treated as Scale depending on the analysis
Income bracket: Ordinal
Country: Nominal

PublicOp currently marks numeric variables as Nominal when generating .sav files. This is a known limitation. Researchers should manually adjust relevant variables to Ordinal or Scale inside SPSS.

This is especially important for Likert scales, age, income, scores, ratings and numeric input questions.

Practical advice:

• After opening the file, check Variable View in SPSS.
• Mark Likert and ordered categorical variables as Ordinal where appropriate.
• Mark age, duration, score and numeric input variables as Scale where appropriate.
• Keep true categorical variables as Nominal.

Why a Codebook is essential

A Codebook is the user manual for the dataset.

A good Codebook should include:

• Variable Name,
• Variable Label,
• Question ID,
• Question Type,
• Value Labels,
• Missing Values,
• multiple-choice variables,
• open-ended response fields,
• Branching / Skip Logic information,
• language information for multilingual surveys,
• data collection date and version information.

Example Codebook entry:

Variable Name: SERVICE_SATISFACTION
Variable Label: How satisfied are you with this service overall?
Question Type: Likert
Values:
1 = Very dissatisfied
2 = Dissatisfied
3 = Neutral
4 = Satisfied
5 = Very satisfied
Missing:
-98 = Saw the question but did not answer
-99 = Not shown because of logic
Measure: Should be set as Ordinal

PublicOp includes a CodebookGenerator in the export infrastructure. It provides information about which question maps to which ID and data type during export. This makes the dataset easier to understand and share within a team.

Still, a Codebook is not only a technical file. It should also document methodological decisions. For example, whether “Don’t know” should be analyzed as a valid response or excluded as missing is a research decision, not just a software setting.

What to check after exporting to SPSS

Do not start analysis immediately after downloading the SPSS file. Run a quick quality check first.

Check Variable View

In SPSS Variable View, check:

• Are variable names readable?
• Are Variable Labels correct?
• Are Value Labels visible?
• Are string variables imported correctly?
• Do measurement levels need manual correction?
• Should missing values be defined manually?

Check Data View

In SPSS Data View, check:

• Does each row represent one respondent?
• Do multiple-choice columns use the 0-1 structure?
• Are open-ended responses in the correct columns?
• Is the LANGUAGE column present?
• Do -98 and -99 appear where expected?
• Do test responses match the original survey answers?

Run basic frequency tests

Start with simple frequencies for:

• gender,
• age group,
• language,
• key outcome questions,
• multiple-choice options,
• variables containing missing values.

This quickly reveals major coding, label or mapping problems.

How PublicOp fits into the SPSS export workflow

PublicOp treats survey data export as part of research operations, not just as a raw response table. The goal is to move responses into a structure that is easier to analyze.

Relevant PublicOp SPSS Export features include:

• CSV, Excel and real .sav export,
• native binary .sav generation,
• Variable Name generation based on Question ID or custom SPSS code,
• SPSS-compatible variable name sanitization,
• Variable Labels generated from question text,
• Value Labels generated from answer options,
• different export behavior for single choice, multiple choice, Likert, matrix and text questions,
• 0-1 binary columns for multiple-choice questions,
• transcript text for AudioRecorder responses,
• -98 and -99 distinction for unanswered and system-missing cases,
• one .sav dataset for multilingual surveys,
• LANGUAGE column,
• Preferred Language selection for label language,
• CodebookGenerator support.

It is important to be precise. PublicOp does not make methodological decisions for the researcher. The researcher still decides which variables should be Ordinal or Scale, which values should be treated as user-missing, how Multiple Response Sets should be defined and how open-ended responses should be coded.

PublicOp’s role is to make those decisions easier to carry into a cleaner, more analyzable data structure.

Pre-export checklist for SPSS

Use this checklist before exporting survey data to SPSS.

Variable structure

• Is the correct variable generated for every question?
• Are Question IDs or custom SPSS codes consistent?
• Are variable names within the 64-character limit?
• Have spaces and special characters been sanitized?
• Do matrix rows become separate variables?

Labels

• Does every variable have a Variable Label?
• Do closed-ended questions have correct Value Labels?
• Was the correct Preferred Language selected?
• In multilingual surveys, is the label language as expected?
• Did any 120-character Value Label limit affect meaning?

Missing values

• Is -98 used correctly for unanswered questions?
• Is -99 used correctly for system-missing cases?
• Were -98 and -99 manually defined as user-missing in SPSS?
• Have “Don’t know” and “Prefer not to answer” been handled according to the research design?

Multiple-choice questions

• Does every option appear as a separate binary variable?
• Is the 1 = Selected, 0 = Unselected structure correct?
• Were SPSS Multiple Response Sets defined manually?
• Was “Other” text checked separately?

Open-ended and audio responses

• Are open-ended responses exported as string variables?
• Is there a risk of truncation because of the 254-character limit?
• Were CSV or Excel exports checked for long text responses?
• Are AudioRecorder transcripts in the correct column?
• Is it clear that audio file links are not exported to SPSS?

Multilingual structure

• Are all languages combined into one .sav dataset?
• Is the LANGUAGE column present?
• Was the correct Preferred Language used?
• Does the same question map to the same variable across languages?
• Is language-level analysis possible?

Post-export test

• Does the file open correctly in SPSS?
• Do frequency tables look correct?
• Are Value Labels visible?
• Has Variable View been checked?
• Have measurement levels been manually corrected?
• Does the Codebook match the dataset?

Mistakes to avoid when exporting survey data to SPSS

Avoid these common SPSS export mistakes:

• using full question text as variable names,
• exporting only numeric codes without Value Labels,
• storing multiple-choice answers in one text cell,
• assuming Multiple Response Sets are automatically defined,
• forgetting the 254-character limit for open-ended string variables,
• failing to define -98 and -99 as missing values in SPSS where appropriate,
• treating logic-skipped questions as normal unanswered questions,
• splitting multilingual surveys into separate variable structures,
• assuming measurement levels are automatically correct,
• starting analysis without a Codebook,
• treating SPSS export as separate from survey design.

These mistakes do not only create technical inconvenience. They can also lead to misleading findings.

Conclusion

Preparing clean survey data for SPSS is much more than pressing an export button. A strong SPSS Export starts during survey design and continues through data quality checks before analysis.

The safest workflow is:

1. Plan variable names early.
2. Use Question IDs and custom SPSS codes consistently.
3. Treat question text as Variable Labels.
4. Define closed-ended response options as Value Labels.
5. Export multiple-choice questions as binary variables.
6. Consider string limits for open-ended and audio transcript responses.
7. Interpret Branching / Skip Logic gaps using the -98 and -99 distinction.
8. Preserve one dataset and a LANGUAGE variable for multilingual surveys.
9. Manually check measurement levels and missing value settings in SPSS.
10. Do not begin analysis without a Codebook.

Clean data is the foundation of good analysis. SPSS is powerful, but it can only produce reliable results from data that is structured well.