correlations

• correlation illustrates the strength and direction of an association between two or more co variables (things that are being measured). correlations are plotted on a scattergram. one co variable forms the x axis and the other the y axis. each point or dot on the graph is the x and y position of each co variable.
• frequent use of caffeine can be correlated with anxiety. researchers may get participants to work out how much caffeine they conume and then using a self report get them to record their anxiety levels. in this scenario, a postive correlation can be expected between the two co variables if the data was plotted on a scattergram. also, if we get these same people to record the amount of sleep they have, they are likey to get less sleep when they drink more coffee which is a negative correlation.
• in an experiment the researcher controls the independant variable to meaure the effect on the dependant variable. due to this delibrate change in one variable is it possible to infer that the IV caused a change to the DV. however, in a correlation, there is no manipulation of one variable so it is not possible to establish a cause and effect between the co variables. even if there is a strong postive correlation between the co variables, it cannot be assumed that the IV caused the DV as there may be intervening variables
• one strength of correlation is that they are a useful preliminary tool for research. for example, by assessing the strength and direction of a relationship, they provide a precise and quantifiable measure of how two variables are related. this suggests future ideas for research if variables before researchers commit to an experimental study.
• a second strength of correlations is that they are relativley quick and economical to carry out. for example, there is no need for a controlled environment and no manipulation of variables is required. this means that data collected by others (secondary data) can be used which matters as correlations are therefore less time consuming that experiments
• however, due to the lack of experimental manipulation and control within a correlation, studies can only tell us how variables are related but not why. correlations cannot demonstrate cause and effect between variables and therefore we do not know which co variable is causing the other to change. for instance, research suggests that high levels are caffeine result in high levels on anxiety however, this cannot be proven as these people may have intervening variables. so establishing the direction of the effect is an issue