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Before performing CFA, exploratory factor analysis (EFA) using a principal axis factoring method with varimax rotation was performed to refine the measures.
The data were subjected to Exploratory Factor Analysis using a Principal Axis Factoring method to extract the initial factors (dimensions) of the measured construct.
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To quantify the position of each segmented cell, we computed the geodesic distance to the distal end along the distal proximal axis (using a principal curve computed as described [ 86]).
Here, envelopes of postures are constructed using a principal component analysis (PCA) to find the skeleton's principal axis and orientation at each instant in time.
used in the design of the program are presented, followed by a description of the structure of the program, which uses an "extreme" principal axis method and a two-step diagonalization procedure.
We then conducted a principal axis factor analysis with this number of factors, using an orthogonal rotation to attain a simple factor structure.
We used the Keiser-Meyer-Olkin and Bartlett tests to check for applicability of factorial analysis, then a principal axis factoring extraction method was applied.
In the EFA, a principal axis factoring analysis with oblique rotation (direct oblimin method) was adopted.
The EFA (nEFA = 1221) was computed using the Principal Axis extraction method in SPSS 16.0 in order to adjust to non-normal item distributions [ 7].
2. Using the Principal Axis Factoring method, analyses of QOL-GRAV items 27 to 38 for each trimester are presented in Tables 6 and 7.
Exploratory principal component analyses using the principal axis factoring method of extraction and Promax rotation were then conducted with the Statistical Package for Social Sciences Version 18 to identify the factor structure of the scale.
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