Posted by
Maguin, Eugene on
Dec 15, 2016; 6:54pm
URL: http://spssx-discussion.165.s1.nabble.com/Interactions-binary-logistic-regression-tp5733602p5733614.html
This is kind of grueling but it is still not clear. Your response to Mike included this example data
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
Choice is your DV. Each person sees each combination of formulation and risk level twice. Thus, replication is another level in your experimental design. If you were doing this in anova, you'd describe the analysis as one between, three within:
GLM yf0l0r1 yf0l0r2 yf0l1r1 yf0l1r2 yf1l0r1 yf1l0r2 yf1l1r1 yf1l1r2 by group/Wsfactor form 2 level 2 rep 2/
wsdesign form level rep form*level form*rep level*rep form*level*rep.
* f=formulation, l=level of risk, r=replication.
I know there are list members who are more knowledgeable and experienced than I am with both genlin and genlinmixed and can give you better help.
Gene Maguin
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of PhD student
Sent: Thursday, December 15, 2016 12:23 PM
To:
[hidden email]
Subject: Re: Interactions binary logistic regression
1) Two groups (got that) and each participant completed all 8 trials? Or something else? This element is important! *Yes, exactly*
2) In each trial participants selected either a risky option or a safe option. The dichotomous DV. *Yes*
3) A trial was formulated as a gain or as a loss and as low risk or high risk. Four combinations. *I was not clear enought, the level of risk was 20% chances of winning, 40% 60% and 80%. Classically, but authors recommend to categorize 20 and 40% as low and 60 and 80% as high*
4) Each combination was replicated twice. *Therefore, each combination was presented once.*
If each participant did all eight trials, you have a repeated measures design. Just suppose the DV were continuous, how would you set up the analysis? *If my DV was continuous, I would use ANOVA. In my speciality, data are classically treated with ANOVA, but I am not sure that is the most appropriate technique due to the dichotomous DV*
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