1. Science of Psychology AP Psychology
Essential Task:Describe a correlational research study taking into account operational definitions, random sampling, correlational coefficient, and scatter-plots.
Logo Green is R=8 G=138 B= Blue is R= 0 G=110 B=184
Border Grey is R=74 G=69 B=64

2. The Science of Psychology
Approaches to Psych
Growth of Psych
Research Methods
Statistics
Descriptive
Correlation
Experiment
Case Study
Survey
Naturalistic Observation
Inferential
Ethics
Sampling
Central Tendency
Variance
Careers
We are here

3. Essential Task: Correlational research Setting up the study
Outline
Correlational research
Setting up the study
operational definitions
random sampling
Organizing your data
excel
Analyzing your data
scatter-plots
correlational coefficient
Interpreting the correlation coefficient
Correlation is Not Causation and WHY

4. Correlational Research
Purpose – to show a relationship between two variables
Strength – If you know how they are related you can predict outcomes
Weakness – Correlation is not causation

5. Research Methods in Psychology
Correlational Research
Research technique based on the naturally occurring relationship between two or more variables
Used to make PREDICTIONS, such as the relation between SAT scores and success at college
Cannot be used to determine cause and effect
Asks: Do the two variables vary together? 5

6. Start with two Dependent Variables
DV = Height DV = Weight
DV = Golf Score DV = Number of years the person has played golf
DV = IQ scores
DV = Size of your big toe
DV = Salary
DV = Happiness

7. Do the variables vary together?
Are the numbers which represent height somehow related to (vary with) the numbers which represent weight?
Does a person’s golf score vary with years of practice?
Does IQ vary with big toe length?
Does happiness vary with salary?

8. Scatterplots
Perfect positive
correlation (+1.00)
Scatterplot is a graph that comprises of points generated by values of two variables.
The slope of points depicts the direction,
The amount of scatter shows the strength of relationship. 8

9. Scatterplots
Perfect negative
correlation (-1.00)
No relationship (0.00)
Scatterplot on the left shows a relation between the variables, and the one on the right shows no relationship between the two variables. 9

10. Correlation Coefficient (r=)
When one trait or behavior varies with another, we say the two correlate.
Indicates strength
of relationship
(0.00 to 1.00)
Correlation
coefficient
r = +
0.37
OBJECTIVE 8| Describe positive and negative correlations and explain how correlational measures can aid the process of prediction.
Correlation Coefficient is a statistical measure of relationship between two variables.
Indicates direction
of relationship
(positive or negative) 10

11. Correlation and Strength

12. University of Virginia school of Engineering (n=588)

13. Correlation is NOT Causation
OBJECTIVE 9| Explain why correlational research fails to provide evidence of cause-effect relationships. 13

14. Correlation is Not Causation: It Only Predicts!
Children with big feet reason better than children with small feet.
(Children who are older have bigger feet than younger children; thus they can reason better)
Study done in Korea: The most predictive factor in the use of birth control use was the number of appliances in the home.
(Those who have electrical appliances probably have higher socioeconomic level, and thus are probably better educated about birth control) 14

15. Correlation is Not Causation: It Only Predicts!
People who often ate Frosted Flakes as children had half the cancer rate of those who never ate the cereal. Conversely, those who often ate oatmeal as children were four times more likely to develop cancer than those who did not.
Cancer tends to be a disease of later life. Those who ate Frosted Flakes are younger. In fact, the cereal was not around until the 1950s (when older respondents were children, and so they are much more likely to have eaten oatmeal.) 15

16. Third or Missing Variable Problem
A relationship other than causal might exist between the two variables. It's possible that there is some other variable or factor that is causing the outcome. You don’t know this because you never controlled for those variables. 16

17. Ice cream sales and the number of shark attacks on swimmers are correlated.
Skirt lengths and stock prices are highly correlated (as stock prices go up, skirt lengths get shorter).
The number of cavities in elementary school children and vocabulary size are strongly correlated. 17

18. Illusory Correlations
Redelmeier and Tversky (1996) assessed 18 arthritis patients over 15 months, while also taking comprehensive meteorological data. Virtually all of the patients were certain that their condition was correlated with the weather.
In fact the actual correlation was close to zero
Usually seen when the data in question stands out 18

19. There are two relationships which can be mistaken for causation:
Common response
Confounding 19

20. Common Response:
Both X and Y respond to changes in some unobserved variable, Z. All three of our previous examples are examples of common response. 20

21. Confounding:
X and Y respond to changes in some unobserved variables, A and B.
A  X
B  Y 21

22. Correlation is not Causation

23. Create Operational Definitions
An exact description of how to derive a value for a characteristic you are measuring. It includes a precise definition of the characteristic and how, specifically, data collectors are to measure the characteristic.
It is a way to get a number from one of your variables. 23

24. Operational Definitions in green
DV = Height (in inches without shoes)
DV = Weight (in lbs without clothes)
DV = Golf Score (on golf course x) DV = Number of years the person has played golf
DV = IQ scores (from the WAIS test)
DV = Size of your big toe (in mm from top of joint to top of toe)
DV = Salary (annual salary including bonuses and benefits)
DV = Happiness (???)