How do you handle missing data in SPSS when analyzing your dataset?

1 Identify Missing

Before addressing missing data, you need to identify where and how data is missing in your dataset. SPSS marks missing values with a period (.), but you should also look for non-standard missing values, which might be coded differently, such as "999" or "0" when these values are not valid data points. You can use the MISSING VALUES command to define these as missing. It's essential to distinguish between Missing Completely at Random (MCAR), Missing at Random (MAR), and Not Missing at Random (NMAR), as each type requires a different approach.

2 Listwise Deletion

Listwise deletion, also known as complete case analysis, is the simplest method for handling missing data in SPSS. This approach excludes any case with at least one missing value from the analysis. To perform listwise deletion, you can use the SELECT IF command to filter out cases with missing values. While easy to implement, listwise deletion can lead to a significant loss of data and potential bias if the missing data is not MCAR.

3 Pairwise Deletion

Pairwise deletion is an alternative to listwise deletion that uses all available data by including cases with missing values in the calculations when possible. This method can result in different sample sizes being used for different analyses. In SPSS, pairwise deletion is often the default setting in correlation and regression procedures. However, it can lead to inconsistencies in your results if the assumption that the missing data is MAR is violated.

4 Imputation Methods

Imputation replaces missing values with plausible estimates, allowing for the retention of all cases in your analysis. Simple imputation methods include mean substitution, where missing values are replaced with the variable's mean, and regression imputation, where missing values are predicted based on other variables. SPSS has a REGRESSION command with an option for imputation. More advanced techniques like multiple imputation create several imputed datasets to reflect the uncertainty around the missing values.

5 Weighting Adjustments

Weighting is a technique used to adjust the analysis to account for the missing data without actually imputing values. In SPSS, you can apply sample weights that reflect the inverse probability of a case being missing. This method assumes that the data is MAR and requires a model to estimate the probabilities of missingness. The WEIGHT BY command is used to apply the weights in your analysis.

6 Sensitivity Analysis

Sensitivity analysis involves conducting the same analysis using different methods for handling missing data to assess how sensitive your results are to the method chosen. In SPSS, you might compare the outcomes of listwise deletion, imputation, and weighting to see if your conclusions remain consistent. This practice gives you confidence in the robustness of your results and can be particularly informative when presenting your findings.

7 Here’s what else to consider

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