Correlation studies

Question 1

What is a correlation?

Answer 1

• a method used to analyse datasets, specifically the association between two variables e.g co-variables

Question 2

Types of correlation

Answer 2

Zero correlation - where co-variables are not linked at all e.g there is no relationship between age or beauty
Positive correlation - where both co-variables increase together e.g as you get older you become more beautiful
Negative correlation- as one co-variable decreases the other increases e.g as people get older you become less attractive

Question 3

Correlational hypothesis

Answer 3

When conducting a correlational analysis then a correlational hypothesis is produced
- states the expected association between the co-variables
e.g as people get older they are rated as more beautiful (positive correlation, directional hypothesis)
as people get older their beauty decreases (negative correlation, directional hypothesis)
age and beauty are correlated ( positive/negative correlation, non directional hypothesis)

Question 4

Scatter diagrams

Answer 4

• a correlation can be illustrated using a scatter diagram
• for each individual we obtain 2 scores which are used to plot one dot for that individual
• the scatter of the dots indicated the degree of correlation between the co variables

Question 5

Correlation coefficient

Answer 5

• researchers use a statistical test to calculate the correlation coefficient which is a measure of the extent of correlation that exists between
• a correlation coefficient (CC) is a number
• a CC has a maximum value of +1 for a positive correlation and -1 for a negative correlation
• they are written with a plus or minus sign to show if it is a positive or negative correlation
• tells us how closely the variables are related
• to find out if the CC is efficient then we use tables of significance which tell us how big the coefficient needs to be for it to count as meaningful

Question 6

Difference between correlations and experiments

Answer 6

• in a correlation the variables are simply measured and no change is made so not conclusion can be made about the co-variables
• in an experiment the investigator deliberately changes the IV to observe the effect on the DV so a causal conclusion can be drawn

e.g a study showing that there is a correlation between students attendance and their academic achievement — a researcher could not conclude that the level of attendance cause better achievement

Question 7

Disadvantages of correlations

Answer 7

• misinterpretations of correlations may mean that people design programmes for improvement based on false research e.g if a headteacher believed that higher attendance caused better academic achievement then they would believe this and not focus on any other potential factors that can cause it — causal connection might be true but it is not justified from correlational research
• fail to consider intervening variables that can explain why the co-variables being studied are linked e.g dislike of school may cause bad attendance which impacts exam results
• can lack internal/external validity e.g the method used to measure academic achievement may lack validity or the sample used may lack generalisability

Question 8

Advantages

Answer 8

• can be easily repeated which means findings can be confirmed
• if correlation is significant then further investigation can be justified