Re: Interactions binary logistic regression
Posted by PhD student on Dec 15, 2016; 5:45pm
URL: http://spssx-discussion.165.s1.nabble.com/Interactions-binary-logistic-regression-tp5733602p5733612.html
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.. I apologize my presentation was not clear. My design was 2 (Group: patients vs control) X 2 (Formulation: Gain vs Loss) X 2 (Level of Risk: High vs Low) with Group a between subjects factor and Formulation + Level of risk within-subjects factors.
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. It is a form of quasi-independent variable. I propose my experiment to patients and I match controls on age, IQ, sex...
So each subject/participant is repeatedly measures 8 times but
these represent a factorial (?) combination of three independent
variables/factors. Yes each participant was confronted to 8 choices. Choices were derived from the combination of two independant variables: Formulation (Gain vs Loss) and Level of risk (Low, 20% 40% vs High 60% 80%)
>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. Yes my dependent variable was dichotomous. Choice of the sure option was coded 0 and choice of the risky option was coded 1
> 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) Yes
(2) a factor which we'll call "Riskiness" (2 levels: low vs high) Yes
(3) a facotr which we'll call "Repitition" (2 levels: 1st trial vs 2nd
trial) I have only two factors, but as each level of risk includes 2 percentages I have 8 different trials
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. Yes I have a 3-way interaction: Group X Formulation X Level of risk
> 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. Exactly, my DV is dichotomous, which led to the choice of binary logistic regression
> 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". Here is an hypothetic example of my data set
Subject Group Formulation Level of risk Choice
1 1 Gain Low 1
1 1 Gain Low 0
1 1 Gain High 0
1 1 Gain High 0
1 1 Loss Low 1
1 1 Loss Low 0
1 1 Loss High 1
1 1 Loss High 1
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? Yes, my Group X Formulation X Level of risk was also significant but I gave an example that I though easier to understand
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 Except repetition, it is exactly my design