Missing values are one of the most common problems students encounter when working with SPSS. Whether your dataset comes from a survey, experiment, online form, or university database, it almost always contains blanks, empty cells, or incorrectly coded values.
If missing values are not handled correctly, your results can become:
- Inaccurate
- Biased
- Unable to run statistical tests
- Misleading in your final assignment or dissertation
This guide will teach you exactly how to identify, replace, and handle missing values in SPSS, even if you are a beginner.
If you need expert help with SPSS assignments, data cleaning, or dissertation analysis, you can request support through SPSS Dissertation Help, SPSS Help for Students, or SPSS Output Interpretation Support.
What Are Missing Values in SPSS?
Missing values refer to data that is not available, blank, or coded incorrectly. Examples include:
- Empty cells
- Responses coded as 99, 999, or -1
- Participants who skipped a question
- Errors during data collection
- Invalid values (e.g., age = 300)
SPSS does not automatically know which values are “missing” unless you define them.
Before you run regression, correlation, ANOVA, or any statistical test, you must clean your missing values first.
Why Handling Missing Values Is Important
If you do not replace or correctly define missing values, SPSS may:
- Remove entire cases (casewise deletion)
- Produce errors like “No valid cases”
- Produce biased averages
- Create incorrect regression or ANOVA results
- Reduce sample size dramatically
Proper handling increases the accuracy and reliability of your results.
How to Identify Missing Values in SPSS
Before replacing missing values, you must identify them.
Method 1: Use Frequencies
Steps:
- Click Analyze
- Select Descriptive Statistics
- Choose Frequencies
- Move your variable(s) into the analysis
- Click OK
You will see a table showing valid and missing cases
Method 2: Use Explore
This method is helpful for continuous variables.
Steps:
- Analyze
- Descriptive statistics
- Explore
- Put your variable into Dependent List
- Click OK
SPSS will show:
- Missing values
- Valid values
- Outliers
Method 3: Visual Inspection in Data View
Empty cells or weird codes (e.g., 99, -1, 999) often represent missing data.
Define Missing Values in SPSS
Before replacing missing values, SPSS must know which values count as “missing.”
This is the step most students forget.
Steps:
- Go to Variable View
- Find your variable
- Click on the Missing column
- Select Discrete missing values
- Enter values like 99, 999, or -1
- Click OK
How to Replace Missing Values in SPSS
Now that SPSS knows where the missing data is, you can choose how to replace it.
Method 1: Replace Missing Values Manually
Best for small assignments or when only a few values are missing.
Steps:
- Go to Data View
- Click on the missing cell
- Type a new value
- Press Enter
Typical replacements include:
- Replacing blanks with the mean
- Replacing blanks with median
- Using 0 (only when appropriate)
- Using previous value (for time series data)
When Manual Replacement Is NOT recommended:
- Large datasets
- More than 10 missing values
- Survey data from hundreds of respondents
- Continuous variables requiring accurate means
Method 2: Replace Missing Values Using SPSS Automatic Recode
SPSS has a tool specifically for replacing missing data automatically.
Steps:
- Transform
- Replace Missing Values
- Select your variable
- Choose replacement method:
Available methods:
- Series mean
- Median
- Linear interpolation
- Random value
- Previous value
- Click OK
SPSS will create a new variable named:
Method 3: Multiple Imputation
This is the statistically correct way to replace missing values when you have:
- Large datasets
- Survey research
- Missing at random (MAR)
- Complex statistical models
Multiple imputation creates several possible values, estimates the best one, and reduces bias.
Steps:
- Analyze
- Multiple Imputation
- Impute Missing Data Values
- Select your variables
- Choose number of imputations (5 is common)
- Click OK
Check If Missing Values Were Replaced Correctly
Run Frequencies or Descriptive Statistics again:
Steps:
- Analyze
- Descriptive Statistics
- Frequencies
- Click OK
You should now see:
- No missing values
- Updated sample size
- Correct mean and median
- No “system missing” cases
Report Missing Value Replacement in APA Style
For assignments, projects, or dissertations, you must state how you handled missing data.
Example APA report:
Missing values were examined using frequency tables and defined as discrete missing values (coded as 99). Missing values were replaced using the series mean method through SPSS’s Replace Missing Values procedure. The final dataset contained 310 valid cases with no missing values.
More advanced APA example:
Missing values were identified and handled using multiple imputation with five imputations under the MAR assumption. This approach was selected because of the proportion and pattern of missingness in the dataset.
Common SPSS Errors When Replacing Missing Values
- “No Valid Cases”
Your missing values were not defined properly.
- “Cannot Compute Because Independent Variable Has No Valid Data”
Your dataset uses values like 999 that must be declared missing.
- “Output shows incorrect mean/median”
Your missing value replacement was not applied to all variables.
- “Imputation fails”
Too many variables selected, or dataset has non-numeric columns.
We can fix these errors through SPSS Homework Help or SPSS Help Online.
When NOT to Replace Missing Values
Do not replace missing values when:
- Missingness is intentional
- Data is missing for an entire group
- Your instructor requires listwise deletion
- Only 1–2% of values are missing (better to leave them)
When You SHOULD Replace Missing Values
- 5–30% missing data
- Survey questions with skipped answers
- Continuous scales (Likert surveys)
- Time series with occasional gaps
- Regression, ANOVA, or correlation requires consistent sample size
Conclusion
Replacing missing values improves:
- Accuracy
- Validity
- Reliability
- Statistical power
Failing to clean missing data leads to incorrect grades or rejected research work.
If you want expert support, you can request:
