Thanks
I can help you if you can send me your data set.
Frank
Look at the correlation matrix and SEE what is negative.
While you are at it, making sure that you know what data
you have on hand, look at Frequencies on all the variables,
and make sure that your MISSING are all coded as Missing,
and that there are no oddball scores.
If correlations of good data scatter randomly around zero,
then you apparently do not have a scale with internal
reliability worth mentioning. (That ordinarily would
seem to indicate that you have totally *misread* your
input data, or you are using the wrong variables.)
Of course, if reversing scoring *everything* was a
precise statement, then it achieved nothing, and you
would need to go back and reverse-score a subset that
would scale the latent dimension in a consistent direction.
--
Rich Ulrich
The error message is wrong. A negative alpha does not violate any
assumptions of the reliability model. The error is in interpreting
alpha as the reliability. In general, alpha is only a lower bound
for the reliability. Only in the special case where the items are
essentially tau-equivalent does alpha equal the reliability. Since a
reliability can not be negative, a negative alpha simply provides no
information about the reliability. See Lord & Novick, _Statistical
Theories of Mental Test Scores_, Theorem 4.4.3 and Corollary 4.4.3b.
Ray,
I'm not unhappy with the message.
I think that my "model assumption" includes the idea that
there is a latent dimension which is readily discernible.
Doesn't that say, "There isn't"?
--
Rich Ulrich
A readily discernible latent dimension may be desirable, but it's not
part of the reliability model. At the univariate level, for each item
separately, the model is tautological and contains no assumptions. At
the multivariate level, when the items are considered together, the
only assumption is that the errors of measurement are mutually
uncorrelated.