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Every cell visualised like rectangle represents one category of the individual variable.
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The percentages in the individual variable categories are given for the description of the data.
In arithmetic, for example, we may want to be able to quantify over natural numbers and sets thereof and we may find it convenient to classify the individual variables of the language into at least two categories.
The individual variables that were reported were divided in four categories: demographic, diagnostic, intervention, and effect variables.
Each dataset has multiple observations about each category of the independent variable considered as individual (number of surveys, two in our case).
The Missing Indicator Method is applied to categorical exposures and includes an extra category of the exposure variable for those individuals with missing data.
Each category of the predictor variable was then compared with the reference category for categorical variables.
Ref refers to the reference category of the predictor variable.
The MOR was calculated for each category of the outcome variable compared with the reference category.
Qualitative variables were presented as numbers and frequencies for each category of the variable.
In the analyses, gender and each category of the other variables was recoded into dummy variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com