I am trying to get an effect-size measure for simple effects in a binary logistic regression. I am using the GENLIN procedure in SPSS; here is the syntax:
GENLIN Bar_exact_score (REFERENCE=FIRST) BY Skill Bar_cut_point (ORDER=ASCENDING) /MODEL Skill Bar_cut_point Skill*Bar_cut_point INTERCEPT=YES DISTRIBUTION=BINOMIAL LINK=LOGIT /CRITERIA METHOD=FISHER(1) SCALE=1 COVB=MODEL MAXITERATIONS=100 MAXSTEPHALVING=5 PCONVERGE=1E-006(ABSOLUTE) SINGULAR=1E-012 ANALYSISTYPE=3(WALD) CILEVEL=95 CITYPE=WALD LIKELIHOOD=FULL /EMMEANS TABLES=Skill*Bar_cut_point SCALE=ORIGINAL COMPARE=Skill CONTRAST=SIMPLE(2) PADJUST=LSD /MISSING CLASSMISSING=EXCLUDE /PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION.
The CONTRAST subcommand within EMMEANS provides Wald Chi-square and p values, but I would like to get an effect size measure - for example, an odds ratio. I cannot work out how to do this.
Extra background details:
- In my study, I show expert and novice police officers edited video clips of law enforcement incidents. The clips are edited to stop at a certain point; when the video clip stops, the officers are asked to predict what happens next in the clip (i.e., if it were to continue playing). Officers' responses are scored as correct (1)/incorrect (0).
- There are 3-5 versions of each clip - each one with a different stop point (e.g., early, mid, late stop points).
- This is a between-subjects study, so each officer sees only one version of each clip.
- Each officer watches 23 different video incidents, and each officers sees a mix of cut points across those incidents.
- The number of officers who viewed each version of a clip is different. For example: For Clip X, 20 novices and 12 experts may have viewed cut point #1 for Clip A, and 15 experts and 14 novices may have viewed cut point #2.
- My goal is to identify the stop point in each clip that maximizes the difference between experts' and novices' ability to predict what will happen next. So, I want a way of comparing the simple effects of skill at each cut point.
Thank you for your time.