Measurement error variables are routinely assumed to be independent of the latents in the model, and that renders the causal foundations of measurement error covariances entirely disconnected from the original latent theory.
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.
For all tree topologies, female group size is the only predictor in the best fit model, and simultaneous estimation of the regression parameters and phylogenetic signal suggests that the relationship between these 2 variables is not phylogenetically constrained (e.g., error in the model is due to measurement error or variable expression: Revell 2010).
Observational studies in epidemiology often involve several sources of uncertainty, such as measurement error, proxy variables of unknown validity, confounder adjustment, and multiple comparisons with outcome variables.
One of the advantages of this latter approach is that it enables the researcher to account for measurement error in variables.
In response to these limitations, structural equation modeling has evolved to allow for non-linear relations among variables, clustering, repeated measures, measurement error, feedback loops, and latent variables.
The questionnaire or measuring instrument is insufficient to dictate what constitutes measurement error in a variable like y5 because the latent-effect portion of the model contributes importantly to η3's identity or meaning.
On the basis of the internal consistency results, the latent depression variable was corrected for measurement error in the variables (Stoolmiller, 1995).
Variables εt are the measurement errors, and variables ζ1 and ζ2 consist of the variations of the latent level and slope components, respectively.
Experimental data are subject to measurement error; thus, the variable measurement error should be considered to determine how the error propagates through the operations [24].
