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Categorical Variables
Variables that describe qualities or groups where mathematical averaging is not meaningful.
Nominal Variables
Unordered categories with no hierarchy, such as blood types or native languages.
Ordinal Variables
Categories with a clear rank or sequence but without measurable numerical differences between ranks.
Numerical Variables
Variables that represent measurable amounts where standard mathematical operations are valid.
Discrete Variables
Numerical variables that arise from counting and take isolated whole values.
Continuous Variables
Numerical variables that arise from measuring and can take any real value within an interval.
Primary Data
Firsthand data gathered directly by the researcher for a specific inquiry.
Secondary Data
Existing data compiled and published by external organizations for another purpose.
Quantitative Variables
Variables that represent amounts that can be measured.
Qualitative Variables
Variables that describe qualities or characteristics.
Identification of Variable Types
Determining if a variable is categorical or numerical based on its characteristics.
Best Methods for Primary Data Collection
Surveys, direct measurements, observations, structured interviews.
Best Methods for Secondary Data Collection
Official censuses, government databases, peer-reviewed publications.
Example of Categorical Variable
Destination airport or Seat class (Economy, Business, First).
Example of Numerical Variable
Seated passenger count or Flight duration.
Ranked Categories Definition
Categories that have a clear order or sequence (e.g., satisfaction ratings).
Unranked Categories Definition
Categories that do not have a meaningful order (e.g., blood types).
Scope of Primary Data
Best suited for novel questions, localized populations, and high experimental control.
Scope of Secondary Data
Best suited for large-scale demographics, long-term trends, and cost-effective research.
Counting vs. Measuring
Discrete variables arise from counting while continuous variables arise from measuring.