AP Psychology Science of Psychology Essential Task Describe

AP Psychology Science of Psychology Essential Task: Describe a correlational research study taking into account operational definitions, random sampling, correlational coefficient, and scatter-plots.

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

Essential Task: 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

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

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?

Start with two Dependent Variables 1. DV = Height DV = Weight 2. DV = Golf Score DV = Number of years the person has played golf 3. DV = IQ scores DV = Size of your big toe 4. DV = Salary DV = Happiness

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?

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.

Scatterplots Perfect negative correlation (-1. 00) No relationship (0. 00) Scatterplot on the left shows a relation between the variables, and the on the right shows no relationship between the two variables.

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 Correlation Coefficient is a statistical measure of relationship between two variables. r = + 0. 37 Indicates direction of relationship (positive or negative)

Correlation and Strength

University of Virginia school of Engineering (n=588)

Correlation is NOT Causation or

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)

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 1950 s (when older respondents were children, and so they are much more likely to have eaten oatmeal. )

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.

• 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.

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

There are two relationships which can be mistaken for causation: 1. Common response 2. Confounding

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.

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

Correlation is not Causation

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.

Operational Definitions in green 1. DV = Height (in inches without shoes) 2. DV = Weight (in lbs without clothes) 3. DV = Golf Score (on golf course x) DV = Number of years the person has played golf 4. DV = IQ scores (from the WAIS test) DV = Size of your big toe (in mm from top of joint to top of toe) 4. DV = Salary (annual salary including bonuses and benefits) DV = Happiness (? ? ? )