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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.

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

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Essential Task 1-: Correlational research Setting up the study –operational definitionsoperational definitions –random samplingrandom sampling Organizing your data –excel Analyzing your data –scatter-plotsscatter-plots –correlational coefficientcorrelational coefficient –Interpreting the correlation coefficientInterpreting the correlation coefficient Correlation is Not Causation and WHYCorrelation is Not Causation Outline

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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.

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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?

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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

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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?

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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.

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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 (???)

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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?

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

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See variable sheet to do our own research

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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. Scatter plots

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

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Correlation Coefficient (r=) When one trait or behavior varies with another, we say the two correlate. Correlation coefficient Indicates direction of relationship (positive or negative) Indicates strength of relationship (0.00 to 1.00) r = Correlation Coefficient is a statistical measure of relationship between two variables.

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Correlation and Strength

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University of Virginia school of Engineering (n=588)

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If not just IQ, then what?

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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

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

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or Correlation is NOT Causation

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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.)

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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.)

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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.

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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. Third or Missing Variable Problem

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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.

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

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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.

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

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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

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Correlation is not Causation

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