Date: Wed, 4 Jun 2008 08:33:12 -0700
Reply-To: David Futrell <dfutrell62@yahoo.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: David Futrell <dfutrell62@yahoo.com>
Subject: Re: Handling Don't Know Responses in Scale Construction
In-Reply-To: <200806040906.m53JIDPa011259@malibu.cc.uga.edu>
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Hannah,
They should be treated as "missing" data. The respondents had the option of choosing the mid-point (i.e., neutral) point on the scale, but chose "don't know" instead. Usually, this means they didn't have enough information to answer the question.
This probably means something qualitatively different from "neutral" which might imply something like ambivalence (i.e., being torn between the ends of the scale) or apathy (not caring).
David Futrell, Ph.D.
Senior Workforce Research Consultant
Eli Lilly and Company
Hannah State-Davey <hmclarke@HOTMAIL.CO.UK> wrote: I am currently developing an attitude scale and have collected data for
initial item reduction. The scale uses a 7-point likert scale (1=strongly
disagree, 2=disagree, 3=slightly disagree, 4=neutral, 5=slightly agree,
6=agree, 7=strongly agree). I also included a 'Dont Know' category to
filter out any items that respondents consistently could not answer. My
problem is how to handle these 'Don't Know' responses correctly. It
clearly provides an 'attitude' and so is important information that should
be included in analysis. There are a number of options that I have come up
with:
1) to treat DK responses as missing values and replace with mean, EM or
such like. I feel this option distorts the data somewhat.
2) to collapse DK responses into the mid-point (i.e. neutral) - despite
neutral and DK having different meanings. This is an option that I have
seen several researchers use.
3) to treat DK responses as a valid response in the scale. The problem
here is that they are of a different charachter to the 1-7 scale. This
presumably creates problems when you come to factor analysis??
If anyone can provide any advice on this i would be really grateful?
Many thanks
Hannah
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