Seeking New Alternatives to Recover Level 2 Covariates in Multilevel Models

• In Event: Hierarchical Linear Models

Abstract

Analyzing hierarchical data with Multilevel Modeling is crucial to understanding the relationship at the individual and group levels. However, one of the most significant problems with this kind of data is small sample sizes and very low Intraclass Correlations.
The multivariate Latent Covariate Model is often accepted as the gold standard for analyzing hierarchically structured data. However, previous studies showed that this model did not work very well under the above mentioned conditions. The first research question intended to show how the Multilevel Latent Covariate Model worked under these conditions via a simulation study and a real data application. The second research question suggested new alternatives.The results showed that the alternative candidate models outperformed the gold standard.

Authors

• Ismail Dilek, University of Iowa
  Presenting Author

• Lesa Hoffman, University of Iowa
  Non-Presenting Author