There are two main types of data: qualitative (categorical) and quantitative (numerical). Within these broad categories, there are subtypes that provide more specific information about the nature of the data.
Qualitative Data (Categorical Data):
- Nominal Data:
- Represents categories with no inherent order or ranking.
- Examples: colors, gender, types of fruits.
- Ordinal Data:
- Represents categories with a meaningful order or ranking.
- The intervals between categories are not consistent.
- Examples: education levels (e.g., high school, bachelor’s, master’s), customer satisfaction ratings (e.g., low, medium, high).
Quantitative Data (Numerical Data):
- Discrete Data:
- Consists of separate, distinct values with no intermediate values possible.
- Examples: the number of cars in a parking lot, the number of students in a class.
- Continuous Data:
- Can take any value within a given range.
- Often measured with greater precision.
- Examples: height, weight, temperature.
Further Classifications for Quantitative Data:
- Interval Data:
- Has a consistent interval or difference between values.
- There is no true zero point.
- Examples: temperature measured in Celsius or Fahrenheit.
- Ratio Data:
- Has a consistent interval between values.
- There is a true zero point, meaning a value of zero indicates the absence of the quantity.
- Examples: height, weight, income.