As @statmerkur said in a comment:
If your data are balanced with regard to observations per subject per
condition and there are scale data for every subject, then I see
hardly any advantage of using linear mixed models (compared to a mixed
factorial ANOVA) for your data if your interest is in the main effect
of condition and the condition x symptoms interaction.
Linear mixed models can be helpful when you don't have well balanced data. They can in principle provide more flexibility, allowing for different types of experimental designs. In your case, with
symptoms self-reported by each
subject, you could then have an unbalanced design (different numbers of cases for each of the
symptoms). It would still be possible to perform ANOVA in that case (taking the differences in numbers of cases into account), but a linear mixed model might be used and could be implemented quite simply. If you want to use a linear mixed model, here are some thoughts.
As @PeterFlom said in a comment:
condition] would be a fixed effect. The random effect would be person [
According to your presentation, you specifically want to model the main effect of
condition, the main effect of
symptoms, and their interaction, on
HR. Those 3 have to be treated as fixed effects. The random effects are those that you wish to control for without modeling as individual fixed effects; in your case that is
In principle you can use a linear mixed model to cover all sorts of random effects: random effects for intercepts (e.g., differences among
subjects in baseline
time1), random effects for slopes (e.g. differences among
HR responses to
conditions), and so on. A linear mixed model can handle many types of crossed or nested designs, and the syntax in the
lme4 package, when used correctly, allows the software to figure out the appropriate analysis for the design.
In your case, however, you only have 1 instance of each
condition and 1
symptoms value for each
subject. So in your case you could account for an intercept of baseline HR for each of the
subjects with a random effect, but as you don't have replicates your model might be over-specifed if you tried to incorporate additional random effects.
A few additional thoughts on the experimental design. From the labels it looks like the
conditions are always presented in the same order in time, so there might be some ambiguity in terms of actual effects of
condition versus the passage of time. The self-report of
symptoms seems to be after
time4 in all cases, so the same ambiguity could arise in the
symptoms interaction. Also, the report of
symptoms might be considered an outcome variable rather than a predictor per se. (My sense is that you are treating the
sypmtoms as predictors based on a class of people who report either of the
symptoms; that might be OK based on your knowledge of the subject matter.) If my interpretation of your design is correct, a reviewer or reader will be likely to note these issues, so you should be ready to address them.