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HCI

[HCI Stats] Types of data

Based on Yatani’s wiki, this post introduces the four types of data one will encounter in a statistical analysis.

Start with a story:

Your team has invented a new kind of interface that gives thermal feedback (i.e., cold, cool, warm, hot). You are conducting two studies to test this interface. In your first study, you want to explore, when changing the temperature, what is the minimum change (from room temperature to X temperature) that a user can tell, and how long it takes for one to ‘sense’ a changed temperature. In the second study, you want to compare this new interface with a conventional one. Besides asking users to do some tasks on both, you also want to do a questionnaire.

So what types of data you can get from these two studies? A lot. But we just want to find the shadows of four representatives.

  • Nominal. A nominal variable is similar to an identity, or a category. For example, in the second study, the ‘interface’ (independent) variable is a nominal that contains two levels: a thermal interface and a conventional interface;
  • Ordinal. An ordinal variable is similar to a rank – you know the position of a given value within the range of all values. In the second study, if the Likert scale is used in the questionnaire, the score is an ordinal variable, e.g., rating the satisfaction from 1 (very unsatisfied) to 7 (very satisfied).
  • Interval and Ratio. Both are continuous numeric values that can be any real numbers. The difference between an Interval variable and a Ratio variable is similar to the difference between ‘distance’ and ‘displacement’. For example, in the first study temperature is Interval and the ‘sensing time’ is Ratio. Because for temperature only the difference of values matters (whereas whether it is Celcius or Fahrenheit doesn’t); but time is different – 1s is shorter than 2s no matter what.

In Yatani’s wiki, he says:

  • It is better to design your experiment so that your dependent variable (what you measure) is ratio;
  • It is important to figure out which dependent variables and independent variable you use and what types of data they are before jumping into any kind of statistical tests.
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