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Once your experiment has been conducted you should have data that will hopefullly demonstrate the relationship between the IV and the DV. Before you get to analysing that question though, you must present the data in the most appropriate way as to allow you and the reader of your report to see exactly what your results are and if there are any trends or correlations present.
A simple table can be a great way to show all of your data at once. All columns should be clearly labelled and titled so that the reader can see exactly what is being presented and where it is being presented.
While a table might serve to present us with a good understanding of the spread of results, it may be difficult to see trends or correlations if information is only presented in the format above. Firstly, we might choose to present our data as averages or means, rather than showing the entire data set. This may be more informative, or it may be misleading depending on the spread of results.
demonstrating a relationship between variables is often best done using a graphical representation of the data. The appropriate graph to use would depend on the type of data being investigated and the relationship that might exist.
BAR CHARTS are good at showing quantity or comparing quantities between categories.
LINE CHARTS are good at demonstrating trends over time/trials
PIE CHARTS are good at demonstrating proportional relationships
SCATTERPLOTS are good at showing correlating data
Whichever graphical representation is used, it is essential that it is clearly labelled, titled appropriately
The above graph is good at showing us a quantity as it related toa category. I can see the age (or the IV) on the x axis and I can see the time and/or score on the y axis (likely to be the DV)
Line graphs are great for showing trends and relationships. As we can see in this graph as the amount of sleep increases, the number of errors has decreased. This suggests that amount of sleep (IV) does actually play a role in the accuracy of the task in question.
Similar to line graphs, scatterplots are great at showing correlation between two data sets, In this instance we are looking at facial attractiveness compared with intelligence. If one value rises at the same time as the other then we can suggest that the two sets of data share a positive correlation. Whereas if one value tends to go down as the other rises it might be considered to be a negative correlation.
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