Missing Values

Many data files contain a certain amount of missing data. A wide variety of factors can result in missing data. For example, survey respondents may not answer every question, certain variables may not be applicable to some cases, and coding errors may result in some values being thrown out.

There are two kinds of missing values in IBM® SPSS® Statistics:

• User-missing. Values defined as containing missing data. Value labels can be assigned to these values to identify why the data are missing (such as a code of 99 and a value label of Not Applicable for pregnancy in males).
• System-missing. If no value is present for a numeric variable, it is assigned the system-missing value. This is indicated by a period in the Data View of the Data Editor.

There are a number of facilities that can help to compensate for the effects of missing data and even analyze patterns in missing data. This section, however, has a much simpler goal: to describe how Custom Tables handles missing data and how missing data affect the computation of summary statistics.

Sample Data File

The examples in this section use the data file missing_values.sav. See the topic Sample Files for more information. This is a very simple, completely artificial data file, with only one variable and ten cases, designed to illustrate basic concepts about missing values.