Explained Variance Measures for Multilevel Models
Document Type
Article
Publication Date
10-1-2014
Abstract
One challenge in using multilevel models is determining how to report the amount of explained variance. In multilevel models, explained variance can be reported for each level or for the total model. Existing measures have been based primarily on the reduction of variance components across models. However, these measures have not been reported consistently because they have some undesirable properties. The present study is one of the first to evaluate the accuracy of these measures using Monte Carlo simulations. In addition, a measure based on the full partitioning of variance in multilevel models was examined. With the exception of the Level 2 explained variance measure, all other measures performed well across our simulated conditions.
Repository Citation
LaHuis, D. M.,
Harman, M. J.,
Hakoyama, S.,
& Clark, P. C.
(2014). Explained Variance Measures for Multilevel Models. Organizational Research Methods, 17 (4), 433-451.
https://corescholar.libraries.wright.edu/psychology/584
DOI
10.1177/1094428114541701
