Types of Data

Anatomy of Data

• Observation: An individual unit from which data are collected
• Variable: A characteristic for which different observations can take on different values
• Constant: A characteristic that is the same for all observations

Recognize each parts of data

1. A research team recruits $100$ adults aged between $25$-$40$ to participate IN A $6$-month study. Participants log their daily coffee intake and wear sleep trackers at night to record their sleep quality.

Answer

• Observation:
  • The adults
• Variables:
  • Coffee consumption frequency
  • Sleep quality
• Constant:
  • Age Group (all adults are between $25$-$40$)

2. $150$ office workers are surveyed over a $3$-month period where they report their weekly exercise routines and undergo monthly stress tests

Answer

• Observation:
  • The office workers
• Variables:
  • Exercise regime
  • Stress levels
• Constant:
  • Time (all cases are measured in the same span of time, $3$-months)

3. $200$ households in a city participate in a year-long study where their usage of cooking oil is recorded monthly. Additionally, all adult members undergo quarterly heart health check-ups

Answer

• Observation:
  • The households
• Variables:
  • Type of cooking oil used
  • Heart health indicators
• Constant:
  • Geographic location (all households are in the same city)

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Types of Variables

• Categorical (Qualitative): Data that describes qualities or characteristics and is used to group information into labels or categories.
  • Nominal: Data with no logical order or ranking between categories (e.g., eye color, nationality, or favorite food).
  • Ordinal: Data that follows a clear, logical sequence or rank, though the intervals between values are not necessarily equal (e.g., education level, survey ratings like “Satisfied” to “Dissatisfied”, or military rank).

• Numerical (Quantitative): Data that represents measurable quantities and is expressed in numbers for mathematical analysis.
  • Continuous: Data that can take any value within a range, including fractions and decimals, often measured with a tool (e.g., precise height, outdoor temperature, or wind speed).
  • Discrete: Data consisting of distinct, separate values—usually whole numbers—that are counted rather than measured (e.g., the number of siblings, total goals scored in a game, or the number of items in a shopping cart).

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Explanatory vs Response Variables

• Explanatory Variable (Independent / Predictor / $X$):
  • Role: It is the presumed “cause.” In an experiment, the researcher controls or manipulates this variable to see how it affects the subject.
  • In Research: If you are studying how fertilizer affects plant growth, the amount of fertilizer is the explanatory variable.
  • Visualization: Always plotted on the horizontal x-axis.
• Response Variable (Dependent / Outcome / $Y$):
  • Role: It is the “effect” or result. Its value “depends” on the explanatory variable. It is what the researcher measures at the end of the study.
  • In Research: Following the fertilizer example, the height of the plant is the response variable.
  • Visualization: Always plotted on the vertical y-axis.