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Unit 1: Science of Psychology

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1 Unit 1: Science of Psychology
WHS AP Psychology Unit 1: Science of Psychology Essential Task 1-6: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 1-: 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 relationship between two variables. Strength – If you know how they are related you can predict outcomes. Weakness – Correlation is not causation.


6 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? 6

7 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

8 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?

9 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. 9

10 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 (???)

11 Collect Data In correlational research each participant in the research is measured on two different dependent (or criterion) variables. Are these measurements unrelated to each other or are they somehow related?

12 Random Sampling Hingham High School Every person from a population has an equal chance of being selected for your study. Get an alphabetical list and pick every 10th name. If you randomly sampled then you can generalize your findings to the population from which you sampled. Weymouth High School

13 See variable sheet to do our own research

14 Scatter plots 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. 14

15 Scatter plots 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. 15

16 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) 16

17 Correlation and Strength

18 University of Virginia school of Engineering (n=588)

19 University of Virginia school of Engineering (n=588)

20 If not just IQ, then what?


22 Study of Low Self Esteem and Depression
You do the research because you assume the two are related Compare two variables Variable 1 = Score on a self-esteem test Variable 2 = Length of a bought of depression in months 22

23 Score on a self-esteem test
Length of a bought of depression in months 23

24 Correlation is NOT Causation
OBJECTIVE 9| Explain why correlational research fails to provide evidence of cause-effect relationships. 24

25 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.) 25

26 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.) 26

27 Diet soda and weight gain???
The study of more than 600 normal-weight people found, eight years later, that they were 65 percent more likely to be overweight if they drank one diet soda a day than if they drank none. And if they drank two or more diet sodas a day, they were even more likely to become overweight or obese. 27

28 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. 28

29 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. 29

30 There are two relationships which can be mistaken for causation:
Common response Confounding 30

31 1. 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. 31

32 2. Confounding: X and Y respond to changes in some unobserved variables, A and B. A  X B  Y 32

33 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 when the data in question stands out 33

34 Correlation is not Causation

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