I have two sets of models. The first set contains a number of logistic models, fitted using
glm(), with different binary dependent variables. The second set contains a number of linear models, fitted with
lm(), with different continuous dependent variables.
All models are testing for differences between participants in one of four conditions (three treatments and one control) coded as a factor (with the control as the contrast), while controlling for a number of demographic variables, some of them factors and some of them continuous.
The sample size is roughly 800 per condition (the conditions aren't perfectly balanced), so about 3,200 in total.
In addition to comparing the treatment conditions to the control through
summary(), however, I would also like to compare the treatment conditions to one another, to see if some are significantly more effective than others. My question is whether to use
emmeans() to do this.
As I understand it (e.g., from here), the main difference is that
emmeans() uses a t statistic (assuming that hasn’t changed from
multcomp() uses a z statistic, and that the latter therefore tends to result in inappropriately small p values and short confidence intervals.
That would seem to recommend
emmeans(). Or are there ever other considerations making
multcomp() more appropriate?