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However, the grouping or structuring of the measures was difficult and was therefore not selective.
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The software AMOS was used to confirm the factorial structure of the measures and thus top up the reliability of the results.
We took into account the repeated structure of the measures and the hierarchical data structure (i.e., the fact that the segments and vessels were clusters of observations in the patients).
In order to analyze discriminant validity, a multi-dimensional Rasch analysis was applied to examine the empirical separability (i.e., empirical structure) of the measures which form the dependent variables of our study (ACCEPTANCE and INTENTION measure) as well as the measures which the predictors of the affective domain (Astley and Francis [2010]).
Internal consistency for both versions is good (Cronbach's α of 0.91 and 0.94), there is support for a one-factor structure of the measures and their convergent validity have been established [ 27].
Since the very structures of the measures were meant to be validated (i.e., which items cluster under what specific domain), classic reliability indices like Cronbach's α alone would be inadequate for measures that are not first proven to be unidimensional.
The fine structure of the measured HRXRD curves changes for measurements at different positions of the same sample and among different samples due to the different number density and size distribution of islands and NWs.
This results in simplification of the structure of the measuring device and increases its reliability.
Phase I addressed the content and structure of the measure, and the documentation of content validity through qualitative research methods.
Confirmatory and exploratory factor analyses were performed to evaluate the structure of the measure and develop a scoring algorithm.
This behavior will significantly simplify the precise analysis of how the finite feedback response determines the structure of the measured topography and error signal data.
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