What is missing data?[edit | edit source]
Survey responses may have missing data because:
- a respondent did not answer the question
- the answer provided does not follow the requested format
- more than two answers are provided
- a response is illegible
In addition, missing data can be introduced, usually unintentionally, if the:
- Data entry person does not enter the data
- Data analyst deletes data
- For numeric variables, the default for missing data is a decimal point or full-stop in the cell, which will appear automatically. Alternatively a specific value, such as -1 or 99, can be used and specified in the Variable View to indicate missing values.
- For string variables, missing data is indicated by a blank cell
Missing data in a cell will mean that a variable and a case will each have some missing data.
Dealing with missing data[edit | edit source]
The presence of missing data should be identified through data screening.
Strategies for dealing with missing data should be decided prior to data analysis.
One strategy for dealing with missing data is listwise. This means that all cases with even a single piece of missing data (for the variables in an analysis) will not be used e.g.,:
DESCRIPTIVES VARIABLES=VAR00001 /STATISTICS=MEAN STDDEV MIN MAX /MISSING=LISTWISE.
In other words, to be used in the analysis, a case must have no missing data.
Alternatively, missing data can be dealt with pairwise. This means that all available data is used, even from cases with some missing data.
Other approaches involve imputation. This involves predicting or "filling in" the missing data. The simplest form of imputation is mean replacement (i.e., replace the missing data with the mean score for other cases for the same variable). More sophisticated imputation uses regression-based prediction (using scores on other related variables to predict the missing value).
See also[edit | edit source]
- Missing data (Wikipedia)