correlations

define correlation

a mathematical technique in which a researcher investigates an association between two variables, called co-variables

co-variables

the variables investigated within a correlation, e.g. height and weight. they’re not referred to as the IV and DV because a correlation investigates the association between the variables, rather than trying to show a cause-and-effect relationship.

positive correlation

as one co-variable increases, so does the other

negative correlation

as one co-variable increases, the other decreases

zero correlation

when there’s no relationship between co-variables

correlations

- illustrate the strength and direction between two or more co-variables
- are plotted on a scattergram
→ one co-variable forms the x-axis and the other the y-axis
- people may be anxious (e.g. personality type) and thus their influence on the other variables can’t be disregarded → these ‘other variables’ are called intervening variables

the difference between correlations and experiments

- in an experiment the researcher manipulates the IV in order to measure the effect on the DV → as a result of this deliberate change in one variable it’s possible to infer that the IV caused any observed changes in the DV
- in contrast, in a correlation, no manipulation of one variable and thus it isn’t possible to establish cause-and-effect between one co-variable and another

AO3 - strength of correlations: a useful preliminary tool for research

by assessing the strength and direction of a relationship, they provide a precise and quantifiable measure of how 2 variables are related

this may suggest ideas for possible future research if variables are strongly related or demonstrate an interesting pattern

correlations are often used as a starting point to assess possible patterns between variables before researchers commit to an experimental study

AO3 - strength of correlations: correlations are relatively quick and cheap

there’s no need for a controlled environment and no manipulation of variables is required

secondary data (data collected by others, e.g. govt stats) can be used, which means correlations are less time-consuming than experiments

AO3 - limitation of correlations

as a result of the lack of experimental manipulation and control within a correlation, studies can only tell us how variables are related but not why.

correlations can’t demonstrate cause-and-effect between variables and thus it’s unknown which co-variable is causing the other to change.

another untested variable could be causing the relationship between the two co-variables - an intervening variable. this is known as the third variable problem. thus, correlations can occasionally be misused or misinterpreted.