Handling Missing Data
In real-world datasets, missing data is unavoidable. Surveys may be incomplete, data entry may be skipped, or system errors may result in missing values. Understanding how SPSS handles missing data is essential for producing accurate statistical results.
Missing data, if ignored, can reduce sample size, introduce bias, and lead to misleading conclusions. SPSS provides multiple ways to identify and manage missing values.
Types of Missing Data
Before deciding how to handle missing values, it is important to understand why data is missing.
Common types of missing data include:
- System-missing – blank or undefined values
- User-missing – special codes such as -99 or 999
SPSS treats these two types differently, so they must be defined correctly in Variable View.
Example Dataset with Missing Values
Consider the following employee dataset:
| Employee_ID | Age | Monthly_Salary |
|---|---|---|
| 601 | 28 | 42000 |
| 602 | 50000 | |
| 603 | 35 | |
| 604 | 41 | 62000 |
In this dataset, Age and Monthly_Salary contain missing values. SPSS must be instructed on how to treat these cases.
Identifying Missing Data in SPSS
SPSS provides several tools to detect missing values. The most common approach is to use descriptive statistics.
Missing values can be identified by:
- Lower case counts in descriptive output
- Blank cells in Data View
- Frequency tables showing missing categories
Early identification helps decide the best handling strategy.
Methods for Handling Missing Data
There is no single correct way to handle missing data. The choice depends on the amount and pattern of missingness.
Common approaches include:
- Listwise deletion (exclude entire cases)
- Pairwise deletion (exclude values only when needed)
- Replacing missing values with statistics such as mean or median
Each method has advantages and limitations.
Replacing Missing Values Using SPSS
SPSS allows missing values to be replaced using simple or advanced methods.
A common method is replacing missing values with the variable mean.
RECODE Age (SYSMIS = MEAN(Age)).
RECODE Monthly_Salary (SYSMIS = MEAN(Monthly_Salary)).
EXECUTE.
This approach maintains dataset size, but should be used carefully to avoid bias.
When Not to Replace Missing Data
Replacing missing data is not always appropriate. If a large portion of data is missing, replacement can distort results.
In such cases, it may be better to:
- Analyze patterns of missingness
- Exclude problematic variables
- Collect additional data if possible
Thoughtful decision-making is critical when handling missing values.
Quiz 1
What is system-missing data?
Blank or undefined values recognized by SPSS.
Quiz 2
Why is missing data a problem?
It can reduce sample size and bias results.
Quiz 3
What does listwise deletion do?
Removes entire cases with missing values.
Quiz 4
Why should mean replacement be used cautiously?
It can distort variability and bias results.
Quiz 5
Where are user-missing values defined?
In Variable View.
Mini Practice
Create a dataset with:
- Student_ID
- Age
- Test_Score
Leave at least one Age and one Test_Score missing. Identify missing values and apply one appropriate handling method.
Use Descriptives to identify missing values and consider mean replacement for numeric variables.
What’s Next
In the next lesson, you will explore descriptive statistics in SPSS, which summarize data using measures such as mean, median, and standard deviation.