Posted by
Mike on
Dec 15, 2016; 4:53pm
URL: http://spssx-discussion.165.s1.nabble.com/Interactions-binary-logistic-regression-tp5733602p5733609.html
On Thursday, December 15, 2016 10:35 AM, "PhD student" wrote:
It may just be me but the way you describe your design is a little
odd/confusing. It sound like you have a 4-way mixed design (old
school mixed) or a 2x2x2x2 design with a dichotomous dependent
variable..
> In my experiment, 2 independent groups (patient vs. control)
Assuming that you have random assignment to the two "Treatment"
groups, your independent variable is a two level between-subjects
factor. If not randomly assignment, it is some form of
quasi-independent
variable.
Next are the Within-subject factors which is where the confusion arise.
>carried out 8 trials
So each subject/participant is repeatedly measures 8 times but
these represent a factorial (?) combination of three independent
variables/factors.
>in which they had to chose between 2 options, one safe and one risky.
Now, this seens to be your dependent variable, right? But it is
unclear what the response actually is (e.g., "Yes" vs "No", or
"Accept" vs "Refuse", etc.) which apparently you coded as 0,1
or 1,2 or whatever, which is why you are doing a binary logistic
regression -- the fact that you independent variables/factors are
dichotomous is besides the point.
> 4 trials were formulated in terms of gain (2 with a low risk and 2
> with a
> high risk) and 4 trials were formulated in terms of loss (2 with a low
> risk
> and two with a high risk).
So, this is where the within-subject design is described:
(1) a factor which we'll call "Gain-Loss" (2 levels: gain vs loss)
(2) a factor which we'll call "Riskiness" (2 levels: low vs high)
(3) a facotr which we'll call "Repitition" (2 levels: 1st trial vs 2nd
trial)
Assuming a factorial design, this gives one a 2x2x2 combination of
conditions which produces the 8 trials that each subject/participant
responds to, right? In what you originally posted you only went up
to a 3-way interaction while my design implies the presence of a
4-way interaction. You've done something that is not obvious.
> All my IV, Group (patient vs control), formulation (gain vs loss) and
> level
> of risk (low vs high) are dichotoumous.
I really don't understand what you are saying here. The critical point
is
whether your dependent variable is dichotomous or not. If it is, then
binary logistic regression may be the appropriate analysis. If not,
then you're not providing full information.
> For example, I have a significant Group X Formulation interaction, but
> I am
> not sure how I can precisely observe the influence of the formulation
> within
> each group.
If by "Formulation" you are referring to what I call "Gain-Loss", then
you appear to be referring to a 2x2 result, with each group having two
values for "Gain-Loss". Do you have a table or a figure for this result?
If so, please reproduce it so people can better see what you mean
by "influence of formulation within each group".
For completeness sake, it appears that you have the following design
and set of results. The 2-way interaction Gx1 seems to be of interest to
you but I have to ask: are any of the higher interactions significant?
4 main effects: G (for groups), 1 (for gain-loss), 2 (for riskiness) & 3
(for repetition)
6 two-way interactions:
Gx!, Gx2, Gx3, !x2, 1x3, 2x3
4 three-way interactions:
Gx1x2, Gx1x3, Gx2x3, 1x2x3
1 four-way interaction:
Gx1x2x3
-Mike Palij
New York University
[hidden email]
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