The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies.
Max Planck Society · Max Planck Institute for Human Development · +4 more institutions
Abstract
In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach, in which the observed (manifest) group mean is assumed to be perfectly reliable. This article demonstrates mathematically and with simulation results that this MMC approach can result in substantially biased estimates of contextual effects and can substantially underestimate the associated standard errors, depending on the number of L1 individuals per group, the…
Citation impact
- FWCI
- 13.91
- Percentile
- 100%
- References
- 75
Authors
6- OLOliver LüdtkeCorresponding
Max Planck Society, Max Planck Institute for Human Development
- HWHerbert W. Marsh
University of Oxford
- ARAlexander Robitzsch
Humboldt-Universität zu Berlin
- UTUlrich Trautwein
Max Planck Society, Max Planck Institute for Human Development
- TATihomir Asparouhov
Muthén & Muthén (United States)
Topics & keywords
- Covariate
- Multilevel model
- Statistics
- Intraclass correlation
- Econometrics
- Sampling (signal processing)
- Correlation
- Group (periodic table)
- Climate action