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Table 1 Categorization of variables and constructs of the theoretical model.
Cut-off values for categorization of variables were based on cut-off values from the literature.
In the analysis, categorization of variables was based on previous studies conducted in Ethiopia [ 8, 18].
The three levels of burnout risk served as the framework for the categorization of variables that we created in this study.
Review of the studies on CYP17 illustrates the difficulties of synthesizing evidence on such interactions, notably differences in definition and categorization of variables and in methods of analysis.
After data collection and the categorization of variables, we created a database for statistical analysis using SPSS (Statistical Package for Social Sciences) version 17.
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Compared with 1.5 T, the increased SNR and improved background suppression at 3.0 T may also allow better categorization of variable components (fluid, blood, etc).
Compared with 1.5 T, the increased signal-to-noise ratio and improved background suppression at 3 T may allow better categorization of variable components (fluid, blood, lipid, etc).
To assess the relationship between a continuous variable and the outcome and to subsequently analyze whether transformation or categorization of continuous variables was needed, a scatterplot smoother and a method of quartiles was used for each variable.
We are far from the first to argue against the categorization of continuous variables[ 4, 5] or advocate for the use of non-linear modeling of continuous variables[ 11, 12, 19].
Our analysis was done independently from Blount et al., so these differences are likely related to differences in the definitions and categorization of some variables (e.g., postmenopause or premenarche status), the use of different statistical programs, and different methods of selecting independent variables to remain in the final regression models.
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