TYPES OF DATA

• In order to understand the nature of data it is necessary to categorize them into various types. 
• Different categorizations of data are possible. 
• The first such categorization may be on the basis of disciplines, e.g., Sciences, Social Sciences, etc. in which they are generated. 
• Within each of these fields, there may be several ways in which data can be categorized into types.

There are four types of data: 

1. Nominal 
2. Ordinal 
3. Interval 
4. Ratio 

Each offers a unique set of characteristics, which impacts the type of analysis that can be performed.

The distinction between the four types of scales center on three different characteristics:

1. The order of responses – whether it matters or not
2. The distance between observations – whether it matters or is interpretable
3. The presence or inclusion of a true zero

Nominal Scales

Nominal scales measure categories and have the following characteristics:

• Order: The order of the responses or observations does not matter.
•  Distance: Nominal scales do not hold distance. The distance between a 1 and a  2 is not the same as a 2 and 3
• True Zero: There is no true or real zero. In a nominal scale, zero is uninterruptable

Appropriate statistics for nominal scales: mode, count, frequencies Displays: histograms or bar charts

Ordinal Scales 

At the risk of providing a tautological definition, ordinal scales measure, well, order. So, our characteristics for ordinal scales are: 

• Order: The order of the responses or observations matters. 
• Distance: Ordinal scales do not hold distance. The distance between first and second is unknown as is the distance between first and third along with all observations. 
• True Zero: There is no true or real zero. An item, observation, or category cannot finish zero.

Appropriate statistics for ordinal scales: count, frequencies, mode Displays: histograms or bar chart

Interval Scales 

Interval scales provide insight into the variability of the observations or data. Classic interval scales are Likert scales (e.g., 1 - strongly agree and 9 - strongly disagree) and Semantic Differential scales (e.g., 1 - dark and 9 - light). In an interval scale, users could respond to “I enjoy opening links to thwebsite from a company email” with a response ranging on a scale of values. 

The characteristics of interval scales are: 

• Order: The order of the responses or observations does matter. 
• Distance: Interval scales do offer distance. That is, the distance from 1 to 2 appears the same as 4 to 5. Also, six is twice as much as three and two is half of four. Hence, we can perform arithmetic operations on the data. 
• True Zero: There is no zero with interval scales. However, data can be rescaled in a manner that contains zero. An interval scales measure from 1 to 9 remains the same as 11 to 19 because we added 10 to all values. Similarly, a 1 to 9 interval scale is the same a -4 to 4 scale because we subtracted 5 from all values. Although the new scale contains zero, zero remains uninterruptable because it only appears in the scale from the transformation. 

Appropriate statistics for interval scales: count, frequencies, mode, median, mean, standard deviation (and variance), skewness, and kurtosis. Displays: histograms or bar charts, line charts, and scatter plots.

Ratio Scales

Ratio scales appear as nominal scales with a true zero. 

They have the following characteristics:

• Order: The order of the responses or observations matters.
• Distance: Ratio scales do do have an interpretable distance.
• True Zero: There is a true zero.

Income is a classic example of a ratio scale:

• Order is established. We would all prefer $100 to $1!
• Zero dollars means we have no income (or, in accounting terms, our revenue exactly equals our expenses!)
• Distance is interpretable, in that $20 appears as twice $10 and $50 is half of a $100.

For the web analyst, the statistics for ratio scales are the same as for interval scales.

Appropriate statistics for ratio scales: count, frequencies, mode, median, mean, 

standard deviation (and variance), skewness, and kurtosis.

Displays: histograms or bar charts, line charts, and scatter plots.