Uploaded January 2024 | Updated September 2026, 2 weeks ago
This video demonstrates a common pre-processing step that is carried out prior to performing multilevel modeling using RStudio: creating mean-centered (either grand mean centered or group mean centered) Level 1 predictors and/or compositional variables (i.e., aggregates of Level 1 predictors). In this presentation, I rely on R code described in chapter 6 of Huang's (2023) text, Practical Multilevel Modeling (see here us.sagepub.com/en-us/nam/practical-multilevel-modeling-using-r/book276872#reviews).
You can download a copy of the data file I reference in the video here:
drive.google.com/file/d/182P_ZkgqlC9VqzQhJNl4xInwf33J4que/view
Feel free to download the following supplemental documents:
drive.google.com/file/d/1pOYaOtoDw76UcFq_Wy_L7wqbFWhIEzYS/view
drive.google.com/file/d/1oYJFp49V5r-n8EdqqQD0pk5ea4jLdDvN/view
This video demonstrates a common pre-processing step that is carried out prior to performing multilevel modeling using RStudio: creating mean-centered (either grand mean centered or group mean centered) Level 1 predictors and/or compositional variables (i.e., aggregates of Level 1 predictors). In this presentation, I rely on R code described in chapter 6 of Huang's (2023) text, Practical Multilevel Modeling (see here us.sagepub.com/en-us/nam/practical-multilevel-modeling-using-r/book276872#reviews).
You can download a copy of the data file I reference in the video here:
drive.google.com/file/d/182P_ZkgqlC9VqzQhJNl4xInwf33J4que/view
Feel free to download the following supplemental documents:
drive.google.com/file/d/1pOYaOtoDw76UcFq_Wy_L7wqbFWhIEzYS/view
drive.google.com/file/d/1oYJFp49V5r-n8EdqqQD0pk5ea4jLdDvN/view
