# How to use ordinal logistic regression with random effects?

In my study I will be measuring workload with several metrics. With heart-rate variability (HRV), electrodermal activity (EDA) and with a subjective scale (IWS). After normalization the IWS has three values:

1. Workload lower than normal
2. Workload is average
3. Workload is higher than normal.

I want to see how well the physiological measures can predict subjective workload.

Therefore I want to use ratio data to predict ordinal values. According to: How do I run Ordinal Logistic Regression analysis in R with both numerical / categorical values? this is easily done by using the MASS:polr function.

However, I also want to account for random effects such as between-subject differences, gender, smoking etc. Looking at this tutorial, I don't see how I can add random effects to MASS:polr. Alternatively lme4:glmer would then be an option, but this function only allows the prediction of binary data.

Is it possible to add random effects to an ordinal logistic regression?

• You are not obliged to use proportional odds for this sort of outcome, you can use continuation ratio models and others. You could investigate the ordinal package available from CRAN. – mdewey Oct 5 '16 at 12:54
• @RobinKramer Please clarify what you think you mean by random effects. When statisticians say random effects, they usually want to account for clustering among different observations. For example, say you had repeated measures on the same individuals, so each obs is one person at a certain time, and you had 4 observations per person. You arguably should fit a random effects model; each person has a person-specific random effect (usually assumed to be from a normal distribution). When you say gender, smoking, etc, those can usually be modeled as fixed effects. So, what do you mean? – Weiwen Ng May 9 '17 at 15:24
• @WeiwenNg the question is rather old, but I was used to use LME regressions in which I placed variables, in which I was not interested (but did have an effect on the DV), as random effects. I attempted to do the same with this project. – Robin Kramer May 9 '17 at 15:55
• @RobinKramer My bad, I failed to note the date! That said, I still think there is some confusion here. Do you have repeated measures on the individuals? If so, then you should probably include a random intercept by person. If you're interested in the effect of gender on the DV, then you would probably only need to model it as a normal covariate. Some would say model it as a fixed effect (because you're treating its effect on the DV as fixed). Treating gender as a random effect would really be ontologically confusing. – Weiwen Ng May 9 '17 at 16:26