On Fri, 20 Apr 2001 13:11:02 -0400, "William Levine"
<[EMAIL PROTECTED]> wrote:
...
> A study was conducted to assess whether there were age differences in memory
> for order independent of memory for items. Two preexisting groups (younger
> and older adults - let's call this variable A) were tested for memory for
> order information (Y). These groups were also tested for item memory (X).
>
> Two ways of analyzing these data came to mind. One was to perform an ANCOVA
> treating X as a covariate. But the two groups differ with respect to X,
> which would make interpretation of the ANCOVA difficult. Thus, an ANCOVA did
> not seem like the correct analysis.
- "potentially problematic" - but not always wrong.
> A second analysis option (suggested by a friend) is to perform a sequential
> regression, entering X first and A second to
> test if there is significant leftover variance explained by A.
[ snip ... suggestions? ]
Yes, you are right, that is exactly the same as the ANCOVA.
What can you do? What can you conclude?
That depends on
- how much you know and trust the *scaling* of the X measure,
- how much overlap there is between the groups, and
- how much correlation there is, X and Y.
You probably want to start by plotting the data. When you use
different symbols for Age, what do you see about X and Y? and Age?
Here's a quick example of hard choices when groups don't match.
Assume:
group A improves, on the average, from a mean
score of 4, to 2. Assume group B improves from 10 to 5
Then:
a) A is definitely better in "simple outcome" at 2 vs. 5;
b) B is definitely better in "points of improvement" at 5 vs. 2;
c) A and B fared exactly as well, in terms of "50% improvement"
(dropping towards a 0 that is apparently meaningful).
I would probably opt for that 3rd interpretation, given this set of
numbers, since the 3rd answer preserves a null hypothesis.
With another single set of numbers in hand, I would lean towards
*whatever* preserves the null. But here is where experience is
supposed to be a teacher -- If you have dozens of numbers,
eventually you have to read them with consistency, instead of
bending an interpretation to fit the latest set. But if you do have
masses of data on hand, then you should have extra evidence
about correlations, and about additive or multiplicative scaling.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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