Representing Data
Tables
For Psychologists, tables are a very important thing. The title should be informative with the units stated and the column titles should allow you to know exactly what the results show.
Below is a table with the results from an experiment where students who had had breakfast and not, did a maths test.
Graphs
Graphs are a good way of visually representing your findings in a study, the first one will be a histogram. They are different to bar charts because their scale is continuous (1,2,3,4,5 rather than red, blue and green), so the bars are put next to each other. In a 'proper' histogram there is frequency density but for the purposes of Psychology we will only look at frequency which means the 'number of'.
Another type of graph that you may encounter is a scatter graph this is done by plotting one thing against another, and will show a correlation. As well as a visual representation there are a number of methods that we can use to calculate correlation numerically.
A correlation of +1 would be perfect positive correlation and a correlation of -1 is perfect negative. And having a correlation of 0 would say there is no relationship at all. The diagram below outlines all of this.
Averages and Distribution
Another term for average is measures of central tendancy. So an average gives an indication as to the most typical result; there are three main types, outlined in the table below.
| Mode | The most frequent piece of data. i.e. the one that appears the most. |
|---|---|
| Median | All of the data is put in order, and the middle one is selected. If there is an even amount of data the two middle are added together and halved. |
| Mean | All of the data is added together and then divided by the number there was. |
The distribution of some data can also be called its spread. The easiest method is called the range and you simply subtract the smallest value from the largest. However this is not too accurate.
This is why statisticians have devised a measure called standard deviation. Put simply this measure tells you how much the data deviates (is smaller or larger than) the mean; on average. It is relative to the size of the mean.