11.1 Generating Understanding from Data
Every day we hear claims that are based on observations. Some of these observations provide hard data that can be expressed exactly in numbers, while others provide soft data that are not easily quantified. For example, saying that the weather is cold could be based on hard data from an outside thermometer, but saying that the neighbor looks happy today is certainly based on soft data.
The famous physicist Lord Kelvin said that it is important for scientists to be able to measure what they are speaking of because “when you cannot express it in numbers, your knowledge is of a meagre kind”. This statement is often criticized because it plays down the value of soft data. It must be acknowledged that soft data are very useful in many situations. It is also true that quantitative data are not the same thing as meaningful knowledge. To realize this we only have to recall the methodical gardener who counted his apples in Chapter 2. However unique the information, knowing that there are 1493 apples in his garden is not terribly interesting, unless this is stated in the light of a meaningful problem. Data must answer a well-posed question in an unambiguous way to be useful; formulating that question was the central topic of the last chapter.
The point of Kelvin's statement is that, to provide an unambiguous answer to a research question, observations must be quantified at some level. Research requires a high degree of inter-observer correlation, meaning ...