Filter
Top Products
285
More about Missing Data
Of course, no sensible person deletes data that have already been collected. In order for
this example to make sense, imagine this pattern of missing data arising in the
following circumstances.
Suppose that vocab is the best vocabulary test you know of. It is highly reliable and
valid, and it is the vocabulary test that you want to use. Unfortunately, it is an
expensive test to administer. Maybe it takes a long time to give the test, maybe it has
to be administered on an individual basis, or maybe it has to be scored by a highly
trained person. V_short is not as good a vocabulary test, but it is short, inexpensive,
and easy to administer to a large number of people at once. You administer the cheap
test, v_short, to 40 young and 40 old subjects. Then you randomly pick 10 people from
each group and ask them to take the expensive test, vocab.
Suppose the purpose of the research is to:
Estimate the average vocab test score in the population of young people.
Estimate the average vocab score in the population of old people.
Test the hypothesis that young people and old people have the same average vocab
score.
In this scenario, you are not interested in the average v_short score. However, as will
be demonstrated below, the v_short scores are still useful because they contain
information that can be used to estimate and test hypotheses about vocab scores.
The fact that missing values are missing by design does not affect the method of
analysis. Two models will be fitted to the data. In both models, means, variances, and
the covariance between the two vocabulary tests will be estimated for young people
and also for old people. In Model A, there will be no constraints requiring parameter
estimates to be equal across groups. In Model B, vocab will be required to have the
same mean in both groups.
Model A
To estimate means, variances, and the covariance between vocab and v_short, set up a
two-group model for the young and old groups.
E Draw a path diagram in which vocab and v_short appear as two rectangles connected
by a double-headed arrow.
E From the menus, choose View > Analysis Properties.