1. In other words the relationship between variables
Correlations
In other words the relationship between variables

2. Correlational Studies
Offer us a way of establishing whether there is a relationship between two variables
enable us to assess the strength of that relationship by calculating a
correlation coefficient (R)
The statistical test is known as the Spearman’s Rho
Unlike experiments, correlational studies do not tell us about causal (cause and effect) relationships

3. Correlations are all about relationships!
The difference between an experiment and a correlation is
An experiment looks at cause and effect
a correlation looks at the relationship between 2 variables-
it doesn’t look at which variable has caused the other variable to change.

4. We assume the IV has an effect on the DV
Examples
If the 2 variables were stress and hours of sleep:
An experiment would have 2 conditions:-
IV - 1 group might be given a non-stressful task before sleeping. The 2nd group might be given a stressful task before sleeping.
DV - Amount of hours of sleep would be recorded
in both conditions.
We assume the IV has an effect on the DV

5. Therefore there is no IV or DV in a correlation.
However
A correlation might investigate self-reported levels of stress and the number of hours sleep a participant had.
The difference is that in an experiment something is manipulated, where as correlations are merely looking at 2 variables occurring at the same time.
Therefore there is no IV or DV in a correlation.

6. Rules to correlations
You have to look at the two variables to see if they are occurring at the same time.
The 2 variables have to be measurable
Question: On your white boards write down the term for making something measurable?
answer operationalisation.

7. Examples of Correlation studies
Number of subjects taken at 6th form and hours of homework
Self-reported stress levels and personality type
Out of school activities and grades
Hours of TV watch and level of aggression

8. Can you come up with 3 ideas for a correlation?
Activity
Can you come up with 3 ideas for a correlation?
Write them on your white board now

9. How do you know if the variables are correlated?
There are 3 types of correlations
positive  and negative  and zero 0
Positive-as one variable increases the other variable increases.
Negative- as one variable increases the other variable decreases.

10. Scattergrams
The results from a correlation can be plotted on a scattergram. The scattergram will show whether there is a positive correlation, a negative correlation, or no correlation at all
You can analyse the relationship by
1. Drawing a scattergram
2. Calculating a correlation coefficient (R)

11. Positive Correlation
This is a positive correlation because as the number of hours watching TV increases so does the level of aggression.
Line of best fit

12. Negative Correlation
This is a negative correlation because as the temperature increases, the number of clothes worn decreases.

13. Uncorrelated relationships
This scattergram shows there is no correlation between head circumference & IQ.
This is also known as a zero correlation

14. The Correlation Coefficient
Gives us a statistical method for assessing the strength of a correlation
The sign (+ or -) tells you the direction of the correlation
The number (between 0 and 1) tells you the strength of the correlation
A perfect negative correlation would be -1

15. As one variable rises, so does the other
What kind of correlation is this – write it on your whiteboards

16. The Type of Correlation is Positive
Well done if you got this right 
What number would represent this perfect positive correlation?

17. What goes in the gap? Right it on your white board now
As one variable rises, the other falls

18. The Type of Correlation is Negative
Well done if you got this right 

19. Cause and Effect? What can we conclude from this scattergraph?
White board time again 

20. What number would represent no correlation at all?
A correlation between two variables does not mean that changes in one variable is causing changes in the other
It simply means there is a relationship between the two variables that might be worth investigating further
What number would represent no correlation at all?

21. Advantages and disadvantages
Data may have already been collected
Other factors which are ignored- not controlled for
Ideal for showing a link between 2 measures
No cause and effect
Provides quantitative data
Some problems of using a scale
Can use observational data and self-report measures= high ecological validity.
As some results are taken from a laboratory environment the results may have low ecological validity.

22. Examples Correlational Studies
Research question: is there a relationship between stress and illness?
Variable 1: scores on a stress questionnaire
Variable 2: number of days absent from college since September
Hypothesis: There will be a positive correlation between stress scores and number of absences
Write the null hypothesis on your whiteboard now

23. Correlational Studies
Research question: is there a relationship between absence from college and final AS marks?
Variable 1: absences
Variable 2: final AS mark (Max 300)
Null Hypothesis: There will be no correlation between absences and AS marks
Write the alternate hypothesis now

24. Why alternate and not experimental hypothesis?

25. Main task To write a testable hypothesis and a null hypothesis
To design your own correlational study you will need
To write a testable hypothesis and a null hypothesis
To decide how you will measure your 2 variables.
To know what your sample will be
To know how you will carry out your research

26. http://www.youtube.com/watch?v=A3b9gOt Qoq4