http://spssx-discussion.165.s1.nabble.com/Difference-Score-as-a-dependent-variable-tp5725496p5725506.html
If you want to know how Ya and Yb relate to X, you can look at the
canonical correlation equations; potentially, these give you coefficients
that go in both directions. The weights for Ya and Yb, predicting X,
will show whether the best *statistical* combination is a sum or a
difference, in what proportions. If you only want to see the F-test, you
can put this up as the simple multiple regression, placing X in the usual
position of the IV. This gives a good test, because the multiple R says
everything there is to know about the magnitude of that inter-relation.
For the matrix-notated, Y= X + E, and if you look at the regression analog,
you have the two Y terms being predicted by several X terms. This is
sometimes called "multivariate multiple regression". If you solve, instead,
what I described above, X= Y + E, you get slightly different coefficients. An
example of MMR using GLM is at
www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm
You might try MR for the test, and then MMR in each direction, to figure
out what is being reported by MMR.
Canonical correlation is the generic term that can include MANOVA and
multiple-group discriminant function, with multiple regression and
ANOVA as simple cases that have a single variable (1 d.f.) as outcome.
CONFUSION.
In my subscription to the list, I see 3 threads following this original post.
On Nabble, I see several posts by the OP that are not yet "accepted by the list."
In the discussion, I see comments directed to "change scores,"
which are a specific and largely irrelevant sort of difference. The critiques
of change scores are seldom applicable to simple composite scores that
happen to be differences.
I see "media use" which turns out to be "frequency of watching
Reality TV" ... which seems to be an odd choice of labeling, since
I would have assumed that this particular term invoked something
about "Facebook, etc., participation."
I see the use of "self-concept" in the description of the variables.
Self-concept has always been a tricky and nasty category to play with.
This study applies the term to *views* of what is important, rather than
to self-regard with its own components. My conclusion is that the study's
conclusions will be hard for readers to accept, whatever they are, owing to
loose conceptualization of the terms. And loose relation of the terms to
the actual items collected.
--
Rich Ulrich
> Date: Wed, 16 Apr 2014 16:11:17 -0700
> From:
[hidden email]> Subject: Difference Score as a dependent variable
> To:
[hidden email]>
> Hi,
>
> I am working on a study in which we test the effect of a behavioral variable
> X on a difference score variable Y. The difference score is build out of two
> distinct factors (Ya - Yb). However I know scholars such as Edwards strongly
> recommend to not just compute in SPPS the dependent variable as Ya-Yb.
> I found several alternatives for using difference scores such as polynomial
> regression. But from my understanding, techniques such as polynomial
> regression can only be used when the difference score is your independent
> variable.
>
> Besides testing the effect of X on Ya and Yb separatly, I was wondering
> whether anyone has other suggestions to address the difference score? (which
> can be performed in SPSS or AMOS)?
>
> Thanks!
>
> Laura
>
>
>
> --
> View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Difference-Score-as-a-dependent-variable-tp5725496.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
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