Just wanted people's thought on the following:
I am a graduate student in sociology studying individual's perceptions of
control (locus of control) using existing data. The data set include four
items to measure this construct which were taken from a larger scale of more
than twenty, the larger scale reaching an acceptable level of reliability (I
do not know the exact level, but it is a widely researched and used
instrument) in previous research. The four items that were included were
selected as the best measures of the construct based on empirical evidence
(item-total correlation's, factor analysis).
In my own research, I used these items and decided to sum responses across
these four likert-type items. However, the Alpha reliability is very low
0.30 (items were reverse scored as necessary and coding was double-checked).
I defended the decision to sum the items, despite the low Alpha, based on
the fact that they were selected from a larger set of items which are
internally consistent. In presenting my findings, I was heavily criticized
for this decision.
Now, I could use individual items and a procedure such as logistic
regression (I was using GLM before with this scale as the dependent and a
sample of better than 5000) without changing my conclusions (I ran logistic
models anticipating the criticism), however I was not convinced that this is
necessary.
My question is, is summing these items defensible or at least as defensible
as summing any set of likert-type items to produce a single score. Where
could I find support for what I am doing if it is (clearly my peers won't
just take my word for it)?
Regards,
Brett
