Missing Value Analysis in SPSS

1. Introduction

Missing data is a pervasive issue in statistical analysis that, if not properly addressed, can lead to biased results, reduced statistical power, and invalid conclusions. Researchers often encounter datasets with incomplete information due to nonresponses in surveys, data entry errors, or dropouts in longitudinal studies.

To maintain the quality and reliability of statistical outcomes, handling missing values appropriately is critical. SPSS provides a dedicated tool called Missing Value Analysis (MVA), designed to assess and manage missing data systematically.

This blog post introduces you to MVA in SPSS, explains its estimation methods, and walks through a detailed example to help you perform and interpret this analysis effectively.

2. What is Missing Data?

Missing data refers to the absence of data points for certain variables in a dataset. In SPSS, missing values may appear as system-missing (denoted by a dot “.”) or user-defined missing values (e.g., coded as -99 or 999).

Missingness can be classified by its mechanism:

• MCAR (Missing Completely at Random): The probability of missingness is unrelated to any observed or unobserved data.

• MAR (Missing at Random): Missingness depends on observed data but not the missing values themselves.

• MNAR (Missing Not at Random): Missingness is related to the unobserved data.

Understanding the type of missingness is essential for choosing the correct method of analysis and imputation.

3. Why Missing Data Analysis Is Important in Statistics?

Analyzing missing data is not just a technical step—it’s a statistical necessity. Here’s why:

• Avoids Bias: Improper handling of missing data can skew parameter estimates and lead to incorrect inferences.

• Preserves Sample Size: Rather than deleting entire rows, appropriate handling (e.g., imputation) retains more data for analysis.

• Validates Assumptions: Understanding the pattern and mechanism of missingness helps ensure that subsequent statistical techniques are valid.

Ignoring missing data can compromise both the internal and external validity of a study. Thus, performing a missing data diagnosis is a fundamental prerequisite to any statistical analysis.

4. What is Missing Value Analysis in SPSS?

Missing Value Analysis (MVA) is a specialized procedure in SPSS (found under Analyze > Missing Value Analysis) used to:

• Identify the extent and pattern of missing data

• Test whether data are MCAR using Little’s MCAR test

• Estimate means and covariances with advanced methods such as Expectation-Maximization (EM) or regression

• Provide graphical summaries of missingness patterns

It is especially useful in the early stages of data cleaning, allowing researchers to decide whether listwise deletion, imputation, or more advanced modeling is appropriate.

5. What is Used for Missing Value Analysis?

MVA in SPSS utilizes several diagnostic and estimation tools, including:

• Descriptive Statistics: Number and percentage of missing values for each variable

• Missing Value Patterns Table: Visualizes whether missingness follows a monotonic or arbitrary pattern

• Little’s MCAR Test: A chi-square test to assess whether missing data is MCAR

• Estimation Methods: EM and regression can be used to estimate missing values

• Graphical Displays: Such as bar charts and heatmaps of missingness

These tools help assess the randomness, volume, and structure of missing data—vital for choosing the right imputation or deletion strategy.

6. What are Estimation Methods in Missing Values Analysis in SPSS? 

SPSS provides four main estimation methods within MVA:

• Listwise Deletion: Removes all cases (rows) that have any missing value. It is straightforward but may significantly reduce your sample size and introduce bias if data is not MCAR.
• Pairwise Deletion: Uses all available data for each pair of variables. This method retains more data but may lead to inconsistent matrices and model instability.
• Expectation-Maximisation (EM): An iterative maximum likelihood method that estimates means and covariances from incomplete data under the MAR assumption.

• Regression Estimation: Predicts missing values using a regression equation based on observed values of other variables. Can be deterministic (single value) or stochastic (adds random error).

These estimation methods allow for a more nuanced and statistically grounded handling of missing values than deletion alone.

7. An Example for Missing Value Analysis in SPSS

Consider a dataset containing variables such as Age, Income, and Education Level for 300 respondents. Suppose 15% of income values and 10% of education values are missing.

Using the Missing Value Analysis function in SPSS, you can assess the extent and randomness of missingness, apply the Expectation-Maximization (EM) algorithm to estimate missing values, and generate a summary of missing patterns.

Step by Step: Running Missing Value Analysis in SPSS Statistics

Let’s embark on a step-by-step guide on performing the MVA using SPSS

1. Step: Open the Dataset
Load your dataset in SPSS. Make sure that missing values are either system-missing (denoted by a dot “.”) or defined as user-missing in Variable View (e.g., 999).

2.Step : Navigate to the Missing Value Analysis Tool
Go to the top menu and select:
Analyze → Missing Value Analysis…

3. Step: Select Your Variables
In the Missing Value Analysis dialog box, move the variables you want to analyze (e.g., Age, Income, Education_Level) into the “Quantitative” or “Categorical” variable boxes, depending on their type.

4. Step: Choose Estimation Method

Under the “Estimation” tab, select an appropriate method:

• EM (Expectation-Maximization) for iterative maximum likelihood estimation

• Regression to predict missing values based on linear models

5. Step: Choose Display Options
Tick the following for a comprehensive analysis:

• Descriptive statistics

• Patterns of missing values

• Little’s MCAR test (if applicable)

• EM means and covariances (if EM selected)

6. Step: Run the Procedure
Click OK. SPSS will generate several output tables, including variable summaries, pattern diagnostics, estimation results, and MCAR test results.

Note: Conducting MVA in SPSS provides a robust foundation for understanding the key features of your data. Always ensure that you consult the documentation corresponding to your SPSS version, as steps might slightly differ based on the software version in use.

This guide is tailored for SPSS version 25, and for any variations, it’s recommended to refer to the software’s documentation for accurate and updated instructions.

10. SPSS Output for Missing Value Analysis

After running the Missing Value Analysis in SPSS, the output window provides several important tables and diagnostic results. Here’s what each key section includes:

Variable Summary Table

This table lists each variable involved in the analysis along with:

• The number of valid (non-missing) cases

• The number and percentage of missing cases

• Summary statistics (mean, standard deviation, min/max)

This table helps identify which variables have the highest proportion of missing data and may require targeted handling.

Descriptive Statistics Table

For numeric variables, this table presents:

• Means and standard deviations of observed values

• Estimates of means and standard deviations from the EM algorithm (if selected)

• Comparison between observed and estimated values

This is useful for evaluating how imputation may affect central tendency and variability.

Patterns of Missing Data Table

This table shows all unique combinations of missing and non-missing data across variables (e.g., Case 1: missing Income only; Case 2: missing Education and Income). It highlights whether the pattern is:

• Monotone: Missingness progresses in a consistent order (e.g., if one variable is missing, subsequent ones are too)

• Arbitrary: No clear structure in the pattern of missingness

Monotone patterns are easier to handle in techniques like multiple imputation; arbitrary patterns require more flexible approaches like FCS (Fully Conditional Specification).

Little’s MCAR Test

This chi-square test evaluates whether the data are Missing Completely At Random (MCAR). The test provides:

• Chi-square statistic

• Degrees of freedom (df)

• p-value

A non-significant result (p > .05) suggests that the data are MCAR and that methods like listwise deletion or EM may be appropriate.

EM Means and Covariances Table

If the EM method is selected, SPSS estimates:

• Variable means and standard deviations

• Covariance matrix among the variables

These estimates are based on the full data structure, not just the observed cases, which provides more accurate input for subsequent modeling or imputation.

11. How to Report Results of Missing Value Analysis in APA

• MCAR Test (Little’s):

  • p > .05: Data likely MCAR. Listwise or EM imputation acceptable.

  • p < .05: Data likely MAR or MNAR. Multiple imputation or model-based approaches preferred.

• Missing Data Pattern:

  • Monotone: Simpler for modeling

  • Arbitrary: More complex but still manageable

• EM Estimates:

  • Can be used for further modeling or to assess the bias introduced by missing values

• Descriptive Statistics:

  • Helps identify variables with high missingness (e.g., >20%) that may need exclusion or careful handling

Careful interpretation ensures appropriate decisions about whether to impute, exclude, or model missing data directly.

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For more information about Missing Value Analysis, please visit IBM SPSS