On Tue, 26 May 2020 22:22:40 -0700 (PDT), Scarlett Brown
<
1998browns@gmail.com> wrote:
Observations and implied questions.
"Cronbach's alpha," from the Subject line, is a measure of
the within-scale similarity of items, for somehting that is
considered to have one "latent factor" or meaningful dimension.
It is typically measured at one point of time. It is computed
from variance terms, in several versions, and the usual version
is a transformation of the average correlation of the several items.
im creating a three way repeated measures ANOVA.
I'm not sure how that is relevant. However, I do not see "3-way"
in what is described below. There are two time periods (as I read
it) and three variables.
I would probably be most interested in the alpha and correlations
among the three as measured at Pre. If Treatment has a strong
effect, the correlations are apt to change.
I have two intervals, for before the condition is applied and after.
I have three dependent variables that have been reverse coded,
- if they are all scored in the same direction, that is not needed.
and have checked the matrices to make sure theyre correct.
? "matrices" ? I would look at one correlation matrix.
I would look at Frequences and Crosstabs, to see that the
scores have been Reversed correctly, especially when there
are other results that need explaning.
My scale has been collapsed down to a three point scale, ranging from positive to negative. -1, 0, 1.
I assume that means what I would write as, "My scaling on items
has been collapsed to three points...." I would reserve the bare term
"scale" for the composite score you may compute from the three
items.
You are throwing away detail by collapsing -- some reviewer
would demand justification for that. Collapsing to 3 points also
reduces the size of the correlations that you should expect, owing
to reduced precision. A correlation of 0.5 is nearing the limit of
reliability among dichotomous preference items, comparable to
0.7 (my guess here) for 4-point likert-type items.
I do not understand why my data is weakly correlated as they are being tested on the same dimensions.
The possibilities for apparent data-error include data that were
recorded wrong, read wrong into SPSS, transformed wrong,
interpreted wrong. Scores that seem to share a dimension
judged by face value should be correlated, or be explained.
One thing that happens -- especially with over-collapsing of
scores -- is that you can get extremely skewed distributions.
Those can give you correlations beginners won't expect until
they have run into them, or considered technical computations.
For instance, when two raters Agree on 98 diagnoses out of 100,
the table (98, 1; 1, 0) will produce a negative r despite the 98%
"agreement." And if one rater is 100% Yes or No, you can't
compute a correlation at all.
Every reliability figure is a measure of a test IN some sample.
Occasionally, the samples are odd. Always, we should look at
and keep in mind what variability (including skewness) exists
and is available to be anaylzed.
--
Rich Ulrich
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