1. Research Methods
Correlation

2. Correlation
Correlation research tells us about the relation between two variables.
co-relation

3. Correlation
The purpose is to examine whether and how two variables change together.
Example: If one variable increases, what happens to the other?
If studying increases, what happens to grades?

4. Correlation
When two variables change together, we can use one to predict the other.
If grades increase with studying, then we can predict that the more someone studies, the higher their grades will be.

5. Correlation
Positive Correlation: When two variables rise or fall together.
Examples:
As sleep increases, memory increases.
As salary decreases, spending also decreases.
As hourly pay increases, employee morale increases.

6. Correlation Examples:
Negative Correlation: When one variable increases, the other decreases.
Examples:
The higher the number of absences, the lower the grades
As hours of sleep decrease, levels of stress increase

7. Correlation
Positive doesn’t mean “good” and negative doesn’t mean “bad.” It only describes the direction of the relationship.

8. Correlation The most important thing to remember:
Correlation does not mean causation!!
Just because two variables correlate doesn’t mean one causes the other!

9. Correlation
Correlation ONLY means that two variables change together. It cannot tell us why.
It’s possible that a variable we aren’t even aware of is the cause of the relationship, but only experimental research can determine that. CORRELATION CANNOT.

10. Correlation
Correlations are graphed using scatterplots.

11. Positive correlations start low and move higher.

12. Negative correlations start high and move lower.

13. Correlation
When there is no correlation there is no line because the results are random.

14. Represented by the letter r
Correlation
Correlation coefficient: A numerical value that expresses the degree/strength of the relationship and its direction.
Represented by the letter r

15. The closer to -1.00 or +1.00, the stronger the relationship.
Correlation
Correlation coefficient:
The value of r is always between and +1.00
The closer to or +1.00, the stronger the relationship.

16. r = +.72 .72 strong positive correlation
Correlation coefficient:
r = strong positive correlation
This means that as one variable increases, the other variable also increases significantly.

17. r = -.65 relatively strong negative correlation
Correlation coefficient:
r = -.65 relatively strong negative correlation
This means that as one variable increases, the other variable decreases somewhat significantly.

18. Correlation
Students’ reports of inner speech correlated negatively (-.36) with their level of psychological distress.
This means that those who reported more inner speech tended to report slightly less psychological distress.

19. Correlation
Longitudinal design: measures the relationship between two variables within the same population over time.

20. Correlation Longitudinal design:
This shows how the relationship between the two variables changes as people grow older.
Expensive, takes a long time, and can jeopardize results if people drop out of the study over time.

21. Correlation
Longitudinal design: only works if you use the same participants from beginning to end.

22. Correlation
Cross-sectional design: compares different population groups at the same time.

23. Correlation Cross-sectional design:
Example: a study that splits men and women into separate groups and then measures the relationship between sleep deprivation and stress levels among each group.

24. Correlation Cross-sectional design:
This allows you to see whether the same variables relate differently among different groups.