This is a common question, but I couldn't find a question / answer on Cross Validated dealing with the same problem. In short, is 1000 intercepts too many intercepts, that is, can individual be a random intercept var?
I have a longitudinal data-set with 3 time points and two groups. Time-point 1 is baseline for both groups, so t1 = no treatment for both groups.
Treatment group has 1000 individuals, control group has c.a. 300 individuals. As is expected, some individuals only answer on one time-point, others answer on two or on all three time-points.
respondent time.p group q1 q2 a 1 t agree 1 a 2 t neither nor 2 a 3 t disagree 6 b 1 c neither nor 9 b 2 c neither nor 5 b 3 c disagree 1 c 1 t agree 3 c 3 t agree 5 d 2 c disagree 8
- Should I use mixed effects model for this problem?
- If yes, is it correct to have respondent as random intercept (so c.a. 1000 intercepts)?
- Do I have too few time-points?
I have both ordinal and continuous response vars. I'm using R, so I was going to use lme4 for the continuous response var and package ordinal for the likert.
# syntax for the ordinal var. # levels = Strongly agree, Somewhat agree, Neither nor, # Somewhat disagree, Strongly disagree library(ordinal) clmm2(q1 ~ group * time.p, random = respondent, Hess = TRUE, nAGQ = 10, data = Df) # syntax for the continuous var. library(lme4) library(lmerTest) lmer(q2 ~ group + time.p + (1 | respondent), data = Df)
By the way, for all models I run I get convergent warning.