- 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:
- Nominal
- Ordinal
- Interval
- 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:
- The order of responses – whether it matters or not
- The distance between observations – whether it matters or is interpretable
- 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.
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.
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