Numbers as descriptive
Measures of central tendency
Measures of dispersion
When we undertake research which provides us with quantitative data, those data come to us as lists of numbers. We obtain numbers, or scores, each time one of the participants in our study completes a task we have asked them to do; and since quantitative studies usually involve quite a lot of participants, those numbers come to us as long lists. But long lists of numbers are not easy for anyone to understand. So the first analytical task that we have to carry out on our data is to convert those lists of numbers into a form which allows us to see – and to grasp – what we have found more easily. This is the purpose of descriptive statistics.
Descriptive statistics, as we saw in the last chapter, are statistics which allow us to describe our data. They don't draw conclusions about probability, or allow us to infer how typical those scores may be. Instead, they give us an image of the data, allowing us to collect our information and present it clearly. Sometimes that image takes the form of more numbers – ones which summarise the information that we have found. But sometimes descriptive statistics take the form of graphic images, showing in pictures what the data are like. We will look at numerical descriptive statistics in this chapter, and graphical descriptive statistics in the next.
Numerical descriptive statistics, then, are ways of summarising research findings by using numbers. There are three main types of numerical descriptive statistics. The first ones we will look at are known as measures of central tendency. These are single numbers which are typical of, or represent, the data that we have obtained. The second