# Repeated measures: Use random intercepts model, too many intercepts?

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?

The Data
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


The Question

• 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.

My Syntax

# 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.