Correlations

Correlational studies

the movement and direction of co-variables in response to each other is measured

measures how strong the relationship is between 2 or more variables

there is no claim of cause and effect relationship , but after a correlational study further research may be done to determine if one variable affects the other

e.g. cigarette-smoking and lung cancer. It was noticed that there was a positive correlation between the number of cigarettes smoked and likelihood of getting lung cancer. Later, this research was extended and a cause and effect relationship was found.

Types of correlation

positive, negative, zero

Positive correlation

as one variable increases, so does the other

e.g height and shoe size

Negative correlation

as one variable increases the other decreases

e.g student GCSE grades and the amount of time they are absent from school

Zero correlation

when a correlational study finds no relationship between variables

e.g. the amount of rainfall in Wales and number of people who have read the Lord Of The Rings Trilogy

Correlation coefficient

measures the strength and nature (positive or negative) of the relationship between the co-variables

ranges between -1.0 and +1,0

the nearer the number is to -1.0 or +1.0 the stronger the correlation

A perfect positive correlation = +1.0

A perfect negative correlation = -1.0

scattergram / scattergraph

shows the correlation between 2 sets of data by plotting points to represent each pair of scores. It indicates the degree and direction of the correlation between the co-variables (one on y-axid and one on x-axis)

strong negative correlation scattergram

weak negative correlation scattergram

strong positive correlation scattergram

weak positive correlation scattergram

no correlation scattergram

strengths of correlational techniques

can be used when a lab experiment would be unethical as the variables are not manipulated, only correlated

measures the strength of a relationship between variables, allowing for further research to be conducted

Economical - no need for a controlled environment and can use secondary data

limitations of correlational techniques

not possible to establish cause and effect relationships through conducting a correlation

can only identify linear relationships and not curvlinear

3rd variable - may explain relationship between co-variables