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
clmm2(q1 ~ group * time.p, 
      random = respondent, 
      Hess = TRUE, 
      nAGQ = 10, 
      data = Df)

# syntax for the continuous var.
lmer(q2 ~ group + time.p + 
     (1 | respondent),
     data = Df)

By the way, for all models I run I get convergent warning.


1 Answer 1


Yes, you can use a random intercepts model to account for the correlations in the repeated measurements of each respondent. Even though you have 1000 respondents the intercepts, you include for them are assigned a normal distribution, and for this reason, the actual number of parameters is much less than 1000.

Regarding the convergence problems, you could also give a try in the GLMMadaptive package. For ordinal data you can fit the continuation ratio model; for more info, check here.

  • 1
    $\begingroup$ Thanks so much for the very quick reply. I did not know GLMMadaptive could be used on ordinal data. I've used it on binary response var with good results. I will try it to get rid of the convergent warning and shorten the computation time. $\endgroup$ Apr 11, 2019 at 19:25

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.