# 2: Data Types

## Summary

### 4 Main Basic Data Types:

• Nominal
• Ordinal
• Interval
• Ratio

#### Nominal:

• Pertaining to names
• Named categories
• Unordered (Not mathematically related)
• Referred as “Categories”
• Can be used to count percentage, but not an average
• Dichotomous data (yes/no)

#### Ordinal

• Relates to the order of the data
• E.g. a survey question that explores strongly agree – strongly disagree accompanied with the numbers 1-5
• Has no true mathematical value. The numbers are used as a guide to make data easy to enter
• Can use to count percent but some argue as to whether an average can be calculated

#### Interval:

• Separated into periods (e.g. Time of day)
• Numeric: Can do mathematic operations
• It does not have a zero point (e.g. 0˚C does not mean there is no temperature)

#### Ratio:

• Numeric (similar to interval)
• Has a meaningful zero (e.g. \$0 means there is no money)

### Qualitative vs. Quantitative

#### Qualitative

• No numeric data
• Descriptive information
• E.g. Direct observation, transcripts interviews

#### Quantitative:

• Numeric Data
• Numerical Information
• Interval and ratio Data

## Reflection

The most important aspect is understanding the four main data types (Nominal, ordinal, interval and ratio) to distinguish the difference when presenting data (choice of colour, type and size). For example some are not mathematically related (Nominal and Ordinal), but can be used as a relative guide (Ordinal). This is beneficial to know as I can now distinguish the difference between data types and apply it to the design process.