All,
I am constructing a measurement scale and have used a 7-point Likert
scale. Due to the lack of agreement over whether Likert scale data can
really be considered interval data and the fact that it results in a
skewed distribution, I am looking to compare the results of Pearson's and
Spearman's correlations on the data. Although I have a large enough sample
to reduce the effects of skewness and kurtosis, and the scatterplot
reveals a reasonable linear fit to the data (well equally good linear or
quadratic fit) I still feel I need to confirm that Pearson's is
appropriate to use for this data. The pearson's correlations are primarily
larger than spearman's so i guess it is not underestimating the strength
of the relationship due to non-linearity as would be indicated if
spearman's correlations were larger than pearson's. Is it that the
divergence I am seeing between spearman's and pearson's a product of the
data (i.e. there will be a lot of ties in the data due to people rating
scale items the same)?
I have calculated item-total correlations as the main form of item
reduction before the use of factor analysis using both Spearman's and
Pearson's. I have ordered the results from both methods highest to lowest
and ranked the sets of scores. I then used Spearman's to determine the
degree of association between the two ranked sets of scores. I found a
high correlation which would indicate that the same set of items would be
selected by either of the methods (Nunnally, 1978).
I am also examining the inter-item correlations to determine bloated
specifics or items with a very high number of low correlations indicating
that they don't measure the same as the other items. Can I do a similar
method as above to determine if both correlation methods would result in
the same set of items being selected? Is it enough to just do it for item-
total correlations? Or am I unduly worrying about the applicability of
Pearson's here?
Apologies for all the questions, but if anyone could provide some insight
I would much appreciate it.
Thanks in advance
Hannah
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