Describe the conceptual
meaning of Cronbach’s alpha. If your scale has α = 0.85,
how do you interpret that number?
Replicate the estimation
of alpha and the item-total correlations for the GDS data presented
in text and Figure 11.5 Alpha and item-total correlations for the GDS data.
Then replicate the estimation of bootstrapped CI for the subsample
of N=50 presented in Alphas and 95% CI for three subsamples
and Item-total correlations and 95% CI for three subsamples. Use a seed
of 3 and simple random sampling to replicate the selection of the N=50
sample. Then use a seed of 5 and 2000 resamples to estimate bootstrapped
CI. By setting the seed values, you should find that your results
match those presented in the tables exactly. Refer to Chapter 7 for
more information about the bootstrapping process and the general syntax
that is required. You can also check your code against that provided
on the book website.
Use the engineering
data set from the book to do the following:
Make sure that all items
are coded in the same direction (recode items where necessary) and
calculate alpha for the problem-solving and interest in engineering
scales. Also examine the item-total correlations to see whether any
items could be removed from the scale to improve reliability.
Bootstrap confidence
intervals around the alphas and interpret the replicability of the
estimates.
Interpret alpha in terms
of quality of measurement, referencing the bootstrapped confidence
intervals.