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Underlying constructs, also known as latent factors, were deduced from the correlations between the measured variables of the questionnaire and provided a basis for data reduction and the development of a new simplified index.
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This allows for data reduction.
CATPCA is often used for data reduction.
There are two modes for data reduction, i.e., 'sum' reduction mode and 'skip' reduction mode.
Probabilistic models were introduced for data reduction.
Spatial DFA gave best results for data reduction method.
Sampling techniques are also employed in situ for data reduction.
Methods for data reduction or compression must be included.
The authors employed the first approach for data reduction.
plink v1.06 [19] software was used for this data reduction.
Moreover, a set of measured data, providing a basis for the reduction and consistent reporting of uncertainty in future DGT-based studies, is presented.
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