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In this paper, we develop a nested error linear regression model with an area-level covariate subject to structural measurement error.
Only properly-signed estimates should be introduced, and special caution is warranted for measurement error covariances because the measurement error variables in structural equation models are beyond, or are external to, the substantive modeled latent theory, and hence covariances between error variables often lack substantive theoretical grounding.
The general question of error in measurement raises the topic of measurement theory.
We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model.
The results establish, for the first time, the uncertainty of the AMOC estimate due to the combined structural errors in the measurement design and suggest ways in which the error could be reduced.
The error in measurement will change the classification.
Standard error in permeability measurements is ±0.2 log units.
There were some sources of error in the measurements.
Rather than making structural measurements in post-mortem brains, Shaw and colleagues used magnetic resonance imaging (MRI) in living subjects.
Under the assumption of MVN errors in both the measurement and structural model, the distribution of Y is MVN as well.
Structural equation modeling will also be used because it minimizes measurement error in the measure of pre-existing externalizing behavior problems[ 49].
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