And yet again I have to find from somewhere an ‘interesting’ blog topic to write about so this weeks chosen gauntlet is correlation. Enjoy!
Correlation best describes the form of data that is produced from observational research. Observational research is where no variables are altered (except in controlled observations, but these are pretty much field experiments, but I digress) and individuals are just watched doing as they are doing to get real data, not something that has been ‘grown’ in a lab. After observational data you are left with correlational data that simply tells you that A could predict B and vice versa. There is no causality in correlational data as no manipulation of variables has taken place so you cannot tell whether it was variable A that predicts B or whether variable C is actually a better predictor. This leads us to our first disadvantage of correlation.
Without causality, correlation can only really tell us that there may be a relationship. Also the relationship shown on a scatter graph may mean nothing as the data could be completely down to chance and we would not know. An example of this (although silly) is as follows: A correlational data set shows that large quantitites of chocolate eaten (A) directly influences obesity (B). However the data set does not show that the people who ate large quantities of chocolate studied in this procedure were also incredibly heavy drinkers or ate other high fat foods. However the correlation data set shows a near perfect relationship between chocolate and obesity without looking at all the other factors. But I am being very nitpicky! There are some positives to correlational data.
The major positive is that correlational data steers future research. Observational and correlational research is a cheaper way to explore whether or not an area of research is worth studying. In the same breath I would like to add that even though correlational data cannot show causality it CAN show when there is no causality. If a correlational data set comes back show little to no relationship, then you know as a researcher that there is not much point in continuing with this research. This can save time and money for researchers.
To conclude although correlation cannot show causality or true relationships between variables, it can give us a strong indicator as to whether there will be a relationship. Correlation is a cheap alternative to running a fully blown experiment only then to find out that there was no relationship after all. Same time next week (and another week)!
These websites are some great (but simple) resources for correlation: