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Washington, DC: American Psychological Association
Erscheinungsjahr
2008
Link zum Volltext
Quelle
Applied Social Sciences Index & Abstracts (ASSIA)
Beschreibungen/Notizen
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 number of groups, the intraclass correlation, the sampling ratio (the
percentage of cases within each group sampled), and the nature of the data. To
address this pervasive problem, the authors introduce a new multilevel latent
covariate (MLC) approach that corrects for unreliability at L2 and results in
unbiased estimates of L2 constructs under appropriate conditions. However, under
some circumstances when the sampling ratio approaches 100%, the MMC approach
provides more accurate estimates. Based on 3 simulations and 2 real-data
applications, the authors evaluate the MMC and MLC approaches and suggest when
researchers should most appropriately use one, the other, or a combination of
both approaches.