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
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
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
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.
I chose scale, then I am just running an ANOVA (I think!). If chose
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
the data, feel free to email me at ac11ca[at]hotmail[dot]com.
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