[1] R Package ‘MicroMacroMultilevel’

with Jackson G. Lu, Elizabeth Page-Gould

Version Published: July 2017

 

To date, most multilevel methodologies can only unbiasedly model macro-micro situations, wherein group-level explanatory variables (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to unbiasedly model micro-macro situations, wherein individual-level explanatory variables (and group-level explanatory variables) are used to predict a group-level outcome variable. 

 

In most micro-macro multilevel modeling, it is statistically biased to directly regress the group-level outcome variable on the unadjusted group means of individual-level explanatory variables (Croon & van Veldhoven, 2007). 

This R package is useful because it enables unbiased micro-macro multilevel modeling by producing the best linear unbiased predictors (BLUP) of the group means (i.e., the adjusted group means). 

 

◘ Creator and Maintainer

◘◘ https://cran.r-project.org/web/packages/MicroMacroMultilevel/MicroMacroMultilevel.pdf

◘◘◘ Suggested citation: Lu, J.G., Page-Gould, E., & Xu, N.R. (2017). MicroMacroMultilevel. R package version 0.4.0.

 

 

 

© 2019 by Nancy R. Xu, Boston College

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