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Here V1 TO V26, ξ1 to ξ5, e1 to e26, represents the measured variables, latent factors, and measured variable residuals.
The DI is based on the Kullback Leibner information distance (KLID), the mean of the measured variables, and the range of the measured variable.
In our measurement model, we first examined correlations between each measured variable (Table 3).
SEM examines how each measured variable contributes to each latent variable (the measurement model) and how the latent variables relate to one another (the structural model).
Both are designed to detect a change in the mean of a measured variable leading to an inference regarding the presence of a biased measurement.
Measured variable.
z = measured variable.
Descriptive statistics were calculated for each measured variable.
Noise signals were used to construct a realistic noise model for each measured variable in turn.
This, in turn, permits the derivation of an irrational transfer function from manipulated to measured variable.
There were no F C × FOR interactions (P>0.05) for any measured variable.
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