Random effects structure for nested, repeated measures in clmm? I'm trying to code a clmm model in R to analyze 1-5 Likert data.  Respondents in different cities were asked to take a survey twice: once before a treatment, then again afterwards (Treatment).  The model aims to assess whether and how the treatment interacts with demographic factors, namely respondent age (Age) and socioeconomic status (SES).  Thus, I believe I need a random effects structure that will accommodate both the nested nature of subjects (City), and the repeated measures (Subject).  I'm thinking my model should look something along the lines of: clmm(Likert~Treatment*Age+Treatment*SES+(1|City/Subject), but I'm largely self-taught when it comes to mixed effects models and often struggle with defining random effects.  Thanks!

The proposed model is:
clmm(Likert ~ Treatment*Age + Treatment*SES + (1|City/Subject)

This model will fit fixed effects for:

*

*Treatment

*Age

*SES

*the two way interaction between Treatment and Age

*the two way interaction between Treatment and SES

*random intercepts for City

*random intercepts for Subject varying within City (ie Subject is nested within City.

Based on the description in the post, this seems like a perfectly reasonable approach.
