types-of-data

Quantitative Data

• Definition: Data that represents measurable quantities and can be expressed numerically.

• Example: Height (170 cm), age (25 years), or income ($50,000).

Important Note: Quantitative data can be further divided into:

• Discrete: Countable items (e.g., number of children).

• Continuous: Measurable quantities with infinite possible values within a range (e.g., weight).

Qualitative Data

• Definition: Data that describes qualities or characteristics and cannot be measured numerically.

• Example: Colors (red, blue), types of cuisine (Italian, Mexican), or feedback (satisfied, neutral).

Important Note: Qualitative data is often categorized as:

• Nominal: Categories without order (e.g., types of fruits).

• Ordinal: Categories with a natural order (e.g., satisfaction levels: good, average, poor).

Measurement Scales

Nominal	Ordinal	Interval	Ratio
A scale for labeling categories without any order.	A scale with ordered categories, but differences between values aren’t uniform.	A scale with ordered values and equal intervals, but no true zero point.	A scale with ordered values, equal intervals, and a true zero point.
Types of fruits (apple, banana, cherry).	Customer satisfaction levels (satisfied, neutral, dissatisfied).	Temperature in Celsius (10°C, 20°C, 30°C).	Weight (0 kg, 10 kg, 20 kg).
No mathematical operations can be performed on nominal data; it’s purely for categorization.	Shows ranking but not the exact difference between levels.	Differences can be compared, but ratios are meaningless (e.g., 20°C is not “twice as hot” as 10°C).	Allows for full mathematical operations, including ratios (e.g., 20 kg is twice as heavy as 10 kg).