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The upward continuation of the reduced to the pole data to an altitude above the measurements level will eliminate the effects of possible near-surface noises that may result in misleading responses.
An estimated sample size of 31 subjects, with two repeated measurements, level of significance α, and power (1-β) = 95%, was required [ 20].
The three-level hierarchy will exist of repeated measurements (level 1), nested within patients (level 2), nested within physical therapists (level 3).
To account for the multilevel structure of measurements (level 1) nested within counselees (level 2) nested within counselors (level 3), random effects multilevel regression analyses were conducted.
In both models, a first-order autoregressive covariance structure for the repeated measurements level was used as recommended in longitudinal studies where correlations between measurements tend to weaken as time between them increases.
The data with measurements (level 1) clustered in subjects (level 2) clustered in matched groups (level 3) were, therefore, subjected to multilevel logistic regression analysis, which is ideally suited for analysis of this type of data [ 38].
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Meanwhile, the material strength, as revealed by micro hardness measurements, levels out.
These models account for the correlation between the repeated measurements for each patient and also for the hierarchical structure of the data, with measurements (level-1) clustered within patients (level-2) who in turn were clustered within PNs (level-3).
At the measurement level this was an "on track to graduation index" for students.
Results: intraobserver variability varied between radiologists, measurement plane (anterior-posterior vs transverse) and measurement level.
Predictor variables considered were: level 1 gender, age, cardiorespiratory fitness and year of measurement; level 2 school community.
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