Thank you for reading this question.
I know there have been a few discussions regarding this topic, but I couldn't get a satisfactory answer. So here is my question, with some details in the beginning-
I am trying to understand an effect of a chemical on insect's body mass. So I have 3 groups - untreated insects, insects fed with dose 1, and insects fed with dose 2. Multiple experimental blocks. I am measuring body mass to see an effect of the chemical. I am running a linear mixed model in R, fixed effect = treatment, random effect = block, family = gaussian.
I my linear model, I am comparing "untreated" with "treated dose 1 and dose 2" As an output I get values for these:
Intercept Estimate Std.Error DF t-value p-value
What I gather from other blogs - the "Estimate" value is where you get your effect size from. Is that correct?
If estimate value is 0.01, I need to convert it to odds ratio by exp(0.01) exp(0.01) is = 1 roughly.
This means, that there is no effect of my chemical treatment, correct?
I will also look at p value and confidence intervals along with odds ratio, but is my overall understanding fine?
Is there any other way of getting effect sizes in linear mixed models?