Mark,
I don't quite understand what you are describing.
>>I was wondering if anyone can help me with this problem. I have a dataset
structured like this
test1 test2 test3
Doz1
Doz1
Doz2
Doz3
Doz3
Doz4
Doz4
Doz4
So, are you saying that 'Doz1' is person 1, 'Doz2' is person 2? Therefore,
in your example data, person 1 has two sets of scores on tests 1, 2, and 3
but that person 2 has only one set of scores. Would this be true? And, in
the case of persons 1, 3 and 4, the score for test 1 is the score on the
same test. Would this also be true?
>>For every person (case) I do have various results for different tests.
Now, what I would like to have is a correlation matrix that looks like this:
test1
Doz1 Doz2 Doz3 Doz4 Doz5
Doz1
Doz2
Doz3
Doz4
Doz5
For each test I want the correlations between my candidates.
OK. More questions. Correlations are defined across persons. Think about the
formulas for covariance and variance. Given that, how does your request for
a correlation matrix make sense? Are you giving 'correlation matrix' a
different computational meaning? Also, your layout above suggests to me that
you might plan to repeat the computation for test 2 and test 3. Would this
be true? Lastly, some persons have multiple scores for tests 1 thru 3. How
does this fact enter into the above table?
All that said. You could compute the 'association' between persons across
tests (Note that a 'correlation' is the association between variables,
tests, in your case, across persons.) You could do that, I think, if you
first did a casetovars command to restructure your dataset so that some
persons had 6 tests (persons 1 and 3), some had 3 tests (person 2) and some
had 9 tests (person 4). Then FLIP (the command) your dataset to interchange
rows and columns. Then you'd compute correlations between persons as
Correlations doz1 to doz4.
Gene Maguin
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