Types of Variable

Measurement Levels:

This classification focuses on the nature of the data and the permissible mathematical operations:

• Nominal Variables:
  • Represent categories with no inherent order or ranking.
  • Examples: Colors, Genders, Types of products
  • Permissible operations: Counting frequencies, calculating proportions, mode
• Ordinal Variables:
  • Represent categories with a meaningful order or ranking, but the differences between categories are not quantifiable.
  • Examples: Education levels, Satisfaction ratings, Socioeconomic status
  • Permissible operations: Counting frequencies, calculating proportions, mode, median, percentiles
• Interval Variables:
  • Represent ordered categories with meaningful and consistent intervals between values, but lack a true zero point.
  • Examples: Temperature in Celsius or Fahrenheit, Years on a timeline
  • Permissible operations: Counting frequencies, calculating proportions, mode, median, mean, standard deviation, addition, subtraction
• Ratio Variables:
  • Represent ordered categories with meaningful intervals and a true zero point, indicating the absence of the attribute being measured.
  • Examples: Height, Weight, Age, Income
  • Permissible operations: All arithmetic operations, including ratios and percentages

Role in Analysis:

This classification is relevant in experimental or analytical contexts:

• Independent Variables:
  • Variables that are manipulated or controlled by the researcher to observe their effect on other variables.
  • Also called predictor or explanatory variables.
• Dependent Variables:
  • Variables that are measured or observed as the outcome or response to changes in the independent variables.
  • Also called response or outcome variables.