Continuous data were categorized into categorical variables using SPSS for Windows release 19.0.
The data were categorized for proximal, middle and distal third tibial fractures for analysis.
The acquired data were categorized in a Cartesian data sheet and graph.
Data were categorized according to [46] framework, as detailed in [13].
The data were categorized based on the orientation of each patient's room and the positions of the heads of their beds in relation to window views.
The interview data were categorized retaining the classification of learning strategies (Gargallo et al. 2009) and, from these, the following scheme was generated: (Fig. 1).
The NEISS data was also categorized by disposition: Released, Hospitalized, Fatality, and Other (including transferred, left without being seen, not recorded, etc).. Age groups (in years) for NEISS data were categorized as: 2 4, 5 10, 11 13, 14 17, 18 22, 23 29, 30 39, 40 49, and 50 + .
To determine if morphological change had occurred, data were categorized into two time periods.
KD patients with available echocardiographic data were categorized into 2 groups according to the presence of CAL.
Qualitative data were categorized using key thematic areas and the data was interpreted and presented using verbatim.
Since microarray analysis generates vast amounts of data, the resulting data were categorized based on the functional role of each altered transcript.
