Missing Data
What Is Missing or Incomplete Data?
Missing
data is an issue in exploratory factor analysis because EFA will analyze
only complete cases, and thus any case with missing data will be deleted.
This can reduce sample size, causing estimates to be more volatile.
If missingness is random, then your estimates should be unbiased.
However, it is unusual for missing data to be completely at random.
Thus, it is likely that missing data is causing bias in the results in
addition to reducing sample size—unless
you deal with the missing data in some appropriate manner. In SAS,
we can see how many cases are missing a response by adding the missing
option on the
TABLE statement of PROC
FREQ (e.g., table variable-names /missing;).
If any data on ...
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