1. Causation and Correlation

2. What is Causation?
One factor (or a combination of factors) causes a result.
Example: Flipping the light switch will cause the lights to turn on.
If the factor is not present then the result will not occur.
Example: The lights will not come on unless the switch is flipped to “on.” X Y
Causes
Factor
Switch
Result
Lights

3. Other Examples of Causation
Football weekends cause heavier traffic near the stadium.
Holiday dinners cause food sales to increase.
Less oxygen causes a fire to die.

4. Correlation Correlation is a relationship among variables
Correlations can be strong.
The data points are close together and show a clear pattern.
Correlations can also be weak.
The data points are spread out, but a pattern can still be detected.

5. Strong Positive Correlation
As one variable increases, the other also increases.
As income increases, home purchases increase.
Strong Positive Correlation Y +
Home
Purchases - X
Income

6. Weak Negative Correlation
As one variable increases, the other decreases.
As employee morale increases, complaints decrease. X + Y -
Weak Negative Correlation
Complaints
Morale

7. No Correlation No relationship can be determined.
It does not appear that crime rates rise during a full moon.
No Correlation Y + -
Crime Rates X
New Moon
Full Moon

8. Examples of Correlation
There is a strong positive correlation between a student’s GPA and their standardized test scores.
There is a strong positive correlation between self-esteem and academic achievement.
There is a positive correlation showing that attractiveness is related to better jobs.

9. Lurking Variables
Sometimes a factor appears to cause a certain result, but really there is some other contributing factor.
Lurking variables often affect both the factor and the result.
Example: Fires that caused a lot of damage also had a high number of firefighters at the scene.
Did a large number of firefighters cause more damage than fewer firefighters?
Of course not. The size of the fire is the lurking variable. A larger fire will cause more damage and require more firefighters at the scene to contain it. X
Factor
Firefighters Y
Result
Damage Z
Lurking Variable
Size of Fire
Affects
Affects

10. Examples of Lurking Variables
Do tutors cause lower test scores?
Why do students with tutors have lower test scores?
Students with high test scores do not typically need tutors.
Lurking variable: The degree to which the student understands the material.
Do students with bigger feet know more?
Why do students with bigger feet have a bigger vocabulary?
The older a student is the bigger their feet and the more opportunities they have had to increase their vocabulary.
Lurking variable: Age

11. What Correlation is Not!
Correlation is not causation!
Two variables being related does not mean that one caused the other.
Beware of lurking variables.

12. Experimental Research

13. Critical Components of Experimental Research
Hypothesis
Control groups
Treatment groups
Randomization
Measurement

14. Hypothesis A hypothesis is a statement used to test a theory.
A hypothesis can be tested to show that it is either true or false.
A hypothesis provides the direction for research.

15. Examples of Hypotheses
Children who witness a violent act are more likely to be aggressive.
Cameras that detect speeders will reduce the number of accidents.
People who get regular exercise are more likely to be happy.

16. Control and Treatment Groups
Control groups
Same or similar characteristics as treatment groups
Gives researchers baseline information about subjects
Do not receive treatment-may be given placebo
Helps researchers determine how the treatment group was affected
Treatment groups
This group is given a treatment designed to prove the hypothesis
The only difference between control group and treatment group is the treatment

17. Control and Treatment Groups
Scenario
Control Group
Treatment
Group
Witnessing aggression makes people more aggressive
A group of people who do not witness aggression
A group of people who witness an aggressive act
People who get regular exercise are happier
A group of people who do not exercise regularly
A group of people who exercise regularly

18. Randomization
Subjects who participate in research experiments are randomly assigned to either a treatment group or control group.
Randomization reduces confounding factors that may influence results such as education, culture, and gender.

19. Measure
The measure is the variable that indicates whether the hypothesis is true or not.
When determining if witnessing a violent act increases aggression, the measure is aggression.
When determining if exercise increases happiness, the measure is happiness.

20. Examples of Experiments
Scenario
Control Group
Treatment
Group
Measure
Witnessing aggression makes people more aggressive
A group of people who have not witnessed aggression
A group of people who have witnessed an aggressive act
Measure aggressive behavior
People who get regular exercise are happier
A group of people who do not exercise regularly
A group of people who exercise regularly
happiness

21. Subjects are divided into groups
Experiments
Measure
No treatment given
Control group
Subjects are divided into groups
Treatment
group
Measure
Treatment given

22. Experiments Example
Hypothesis: Viewing violent videos promotes violent actions in children
Measure
aggressive behavior
Subjects do not view violent video
Control group
Subjects have agreed to participate in the study and are randomly divided into two groups
Treatment
group
Subjects view violent video
Measure
aggressive behavior