Including random slopes (when warranted) in multilevel models is necessary to avoid making Type I errors as a result of underestimated standard errors. We typically investigate the presence of random slopes (RS) by using a likelihood ratio test and comparing two competing models. Often, random slopes are included when we have cross level interactions. If a random intercept (RI) model is used when a random slope model is warranted, the regression coefficients will be the same though the standard errors will be incorrect.
Jan 6, 2026
Although CRSEs are commonly-used, there are many instances where they can fail.
Nov 8, 2025
Robust standard errors provide a convenient way to account for random slopes in multilevel models. They can also function as a diagnostic to test for the presence of random slopes.
Oct 8, 2025
I had written before about using the CR2 standard error variant and how they can be used to account for clustering when using basic OLS (or GLM) regression. Some articles on the topic:
Oct 3, 2025
Feb 1, 2025
Jan 1, 2024
MLM
Oct 12, 2023
CR2 plug in for SPSS can be downloaded from: https://github.com/flh3/CR2
Jan 1, 2023
The SPSS version can be accessed here: https://github.com/flh3/CR2/tree/master/SPSS
Jan 1, 2022