http://spssx-discussion.165.s1.nabble.com/categorical-data-analysis-question-tp5729576p5729602.html
"Perhaps this is a poorly considered idea ..." -- what is bad, the scale or your idea?
Are you trying to investigate whether having reversed-items results in different scaling?
- Please consider, "different scaling" is the REASON for having reversed items, to
discount what is called "response bias". If response bias is what you are interested
in establishing, see my last paragraph, below.
For those who are not familiar with the theoretical approach:
The early scale developers in the 1930s noted that there will be *some* (not all)
responders who will tend to favor the positive (or negative) responses. When that
happens and all the items are asked in the same direction, then the user's score
will be a sum of his inherent tendency on the scale, plus (or minus) "response bias."
That is undesirable. How do we get rid of response bias?
The simple solution is to construct scales that have equal numbers of items that
are answered in each direction, so that the "bias" averages out. Thus, you have
manuals and guidebooks that tell investigators that it is ideal to have items this way.
In practice, this observation led to a problem that I noted, even before I studied
up on scales, where scale writers wrote terrible items -- by syntax, ease of reading,
whatever criterion -- in order to change the direction of half the answers. (I hope
this rarely happens any more, and that the word got around that the "cure" for
response bias can be worse than the disease.)
In any case: I noticed long ago that when I had a (decent) scale with many items
reversed, I could detect the response bias by performing a factor analysis: The solution
of a factor analysis of items-as-initially-scored would typically produce one factor with
all loadings positive, possibly in the unrotated solution rather than the varimax solution.
Given the opposite scalings, that factor measures response bias. This would not be the
largest factor, but I think I saw it when the N was large enough to show structure for
other known factors.
--
Rich Ulrich
Date: Tue, 19 May 2015 21:59:53 +0000
From:
[hidden email]Subject: categorical data analysis question
To:
[hidden email]
I have 4-point likert type responses to survey items that are supposed to define several scales. Some items on each scale are reversed. Perhaps this is a poorly considered idea but what I’d like to do, after reversing the reversed items,
is to see to what extent scale items have the same frequency distribution. I was looking through the selection of non-parametric tests and it seems that the Friedman test might be appropriate. However, (Q1) the Friedman is described as being for ‘related’
samples. I am unsure of the meaning of ‘related samples? Help please.
Q2. Is there a better way to go at this?
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