# If the SUM() and MEAN() functions keep cases with missing values in SPSS

Statistical functions in SPSS, such as `SUM()`, `MEAN()`, and `SD()`, perform calculations using all available cases. SPSS will not automatically drop observations with missing values, but instead it will exclude cases with missing values from the calculations. SPSS will correctly estimate the mean with the `MEAN()` function by using all non-missing values.

However, problems can arise when trying to exclude missing cases and estimate results based only on observations with complete information. For example, suppose two variables (`v1` and `v2`) sum to create an index variable (`v3`). While `v1` has ten valid cases with no missing values, `v2` has eight valid cases and two missing values. Use the following syntax to add the two variables and create an index, `v3`:

```COMPUTE V3 = SUM(V1, V2).
EXECUTE .
```

The resulting index variable `v3` has ten cases and no missing values. When SPSS encounters a missing value in any of the `v2` cases, it ignores it and sets `v3` equal to `v1`. Essentially, SPSS treats the missing values of `v2` as zeroes. The results can potentially be misleading.

To ensure that `v3` is equal to the sum of `v1` and `v2` and that all missing cases are dropped rather than ignored, specify the minimum number of valid cases that SPSS should use to calculate a given function. For example, to create an index variable `v3` using only observations without missing values, execute the following syntax:

```COMPUTE V3 = SUM.2(V1, V2).
EXECUTE .
```

The `.2` appended to the end of the `SUM` function in the above example can be any integer. Use it to indicate the minimum number of valid cases necessary to perform a given calculation.

If you have questions about using statistical and mathematical software at Indiana University, contact the UITS Research Applications and Deep Learning team.

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