Date: Sun, 25 Sep 2011 20:32:15 -0400
Reply-To: R B <ryan.andrew.black@gmail.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: R B <ryan.andrew.black@gmail.com>
Subject: Re: 2 x (2) design with binary-->continuous within-subject
variable
In-Reply-To: <1316920405699-4837785.post@n5.nabble.com>
Content-Type: text/plain; charset=ISO-8859-1
You need to better describe the administration of the experiment. It
looks like you have four measurements for each subject, two of which
are binary and two are bounded between 0 and 100. Is that correct?
Please provide a concrete example of how this experiment was
administered to a single subject. A step-by-step explanation would be
helpful (i.e., subject is presented with x and responded with x, then
subject is presented with x and responded with x, etc.). It isn't
clear to me, for instance, how the first subject in your dataset
illustration obtained a value of 0.15 (second case in your dataset).
Ryan
On Sat, Sep 24, 2011 at 11:13 PM, ac11ca <ac11ca@hotmail.com> wrote:
> Hi there,
>
> I am hoping that you might be able to help me analyse some data from a
> decision-making experiment I ran recently.
>
> I have a 2 x (2) design where the within-subjects measure variable changes
> from a binary response (i.e., 0,1) to a continuous response (i.e., 0-100). I
> realise that this is very odd, but it is central to my current research
> question: Are people more like to prefer a risky gamble over a safe gamble
> if
> the choice is presented as a single play or the accumalted sum of 100 plays?
>
> My data looks like this in SPSS (Note: Format = between-subjects
> categorical
> IV [coded 1,2] and Plays = within-subjects categorial IV [coded 1,2], and
> Choice = DV that is half the time binary [0,1] and half the time
> continuous[0-100]):
>
> ID Format Plays Choice
> 1 1 1 0.00
> 1 1 2 0.15
> 1 2 1 1.00
> 1 2 2 0.78
> .
> .
> .
> .
> 93 1 1 0.00
> 93 1 2 0.01
> 93 2 1 1.00
> 93 2 2 0.97
>
> I started by analysing my data with a mixed ANOVA and found an interaction,
> which is what I was hypothesising. Of course, one of the assumptions of the
> ANOVA is that the data are normally distributed, which they clearly are not
> with my binary response data.
>
> To get around this problem I conducted a repeated measures logistical
> regression using the SPSS Generalised Estimating Equations function (GEE)
> under a binary model type. However, the GEE method accepts only one
> distribution for my within-subjects variable: binomial or scale responses.
> If
> I chose scale, then I am just running an ANOVA (I think!). If chose
> binomial,
> then I have to convert the continuous DV to a binary DV (and cut all 50/50
> responses), which basically undermines the motivation of the experiment and
> eliminates crucial differences.
>
> Thus, I think that I must use the original mixed ANOVA analysis and produce
> some hand-waving sort of justifcation. I was wondering if anyone might be
> able to help me with this justification. For example, what is the impact of
> having a binary DV in the middle of a mixed ANOVA and is it really so bad?
>
> Thanks for any help that you might be able to provide. If anyone wants to
> see
> the data, feel free to email me at ac11ca[at]hotmail[dot]com.
>
> Cheers,
> Adrian
>
>
> --
> View this message in context: http://spssx-discussion.1045642.n5.nabble.com/2-x-2-design-with-binary-continuous-within-subject-variable-tp4837785p4837785.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
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