Questions and Answers
In statistical analysis, what type of variable occupies a half-way house between an unordered categorical variable and a continuous variable?
Ordinal variable
For smaller samples and variables with only a few categories, what is usually needed for the analysis of ordinal data?
Special statistical methods
What is the simplest form of categorical variable with just two categories called?
Binary variable
In the context of binary variables, what does it mean when a patient either does or does not possess an 'attribute'?
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What parameter do we deal with when summarizing a population of binary variables?
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Which type of data is appropriate for the methods described so far in the course?
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Why are special methods and approaches required for some continuous variables?
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What is the main difference between ABO blood group and tumour stage as categorical variables?
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Why cannot non-continuous variables be Normal?
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What makes the analysis of categorical data inappropriate for the methods discussed previously?
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Study Notes
Types of Variables
- An ordinal variable occupies a half-way house between an unordered categorical variable and a continuous variable.
- Ordinal data requires a relatively large sample size and/or many categories for analysis.
Binary Variables
- A binary variable is a type of categorical variable with just two categories, also known as a dichotomous variable.
- In the context of binary variables, a patient either possesses an 'attribute' or does not.
Summarizing Binary Variables
- The proportion (or percentage) is the parameter used to summarize a population of binary variables.
Data Types
- The methods described so far in the course are suitable for categorical data.
- Special methods and approaches are required for continuous variables that are not normally distributed.
Categorical Variables
- The main difference between ABO blood group and tumour stage as categorical variables is that ABO blood group is a nominal variable, while tumour stage is an ordinal variable.
- Non-continuous variables (e.g., categorical variables) cannot be normally distributed.
- The analysis of categorical data is inappropriate for the methods discussed previously because they are designed for continuous variables.
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