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Correlation

Example of Descriptive Research: Nose Picking Studies. Authors: Andrade

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Correlation

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    1. Correlation

    2. Example of Descriptive Research: Nose Picking Studies Authors: Andrade & Srihari (2001) Sample: 200 adolescents from 4 schools in India Findings: 100% admit to nose picking Average of 4 times a day 17% believed they have a nose picking problem 25% experienced nose bleeds from picking Ig-Nobel Prize Winner, Annals of Improbable Research http://www.improb.com/

    3. Authors: Jefferson & Thompson (1995) Sample and method: 254 adults (21-84 years) Rhinotillexomania Questionnaire Characterize time involved, level of distress, location, attitudes toward practice, technique, method of disposal, reasons, complications, and other habits Findings 91% were current nose pickers 75% felt “everyone does it” Men were more likely to consider public nose picking to be normal Example of Descriptive Research: Nose Picking Studies

    4. Correlational Research Strategy Goal of the correlational strategy is to examine and describe the associations and relationships between variables. Goal of a correlational study is to establish that a relationship exists between variables and to describe the nature of the relationship. This is a nonexperimental approach to research

    5. A correlational study Examines the relationship between two variables Determines predictive relationships Does not manipulate either variable (no IV) Assesses co-variation among naturally occurring variables

    6. A correlational study Measurements can be made in natural surroundings or in the lab Researcher collects two measurements for each individual participant, one for each of the two variables being examined The question: Is there a consistent pattern of relationship between the two variables?

    7. A correlational study E.g.: - mathematical ability & musical ability

    8. “Correlation is not causation” ** Important: Discovering a correlation does not tell you anything about whether one variable causes another.** There could be a causal relationship between the variables, but the fact that they're correlated doesn't tell us that there is one. e.g.: People who eat a lot of fat weigh more than people who eat low-fat diets

    9. “Correlation is not causation” ** Important: Discovering a correlation does not tell you anything about whether one variable causes another.** Possibilities: Variable A causes Variable B Variable B causes Variable A Variable C causes both A and B

    10. “Correlation is not causation” Directionality problem Does A cause B or does B cause A? Third variable problem Variable C could cause both A and B

    11. “Correlation is not causation”

    12. “Correlation is not causation”

    13. Correlational design and prediction Findings sometimes allow researchers to predict future behavior e.g., certain warning signs of immanent suicide Findings sometimes allow researchers to make predictions about other variables e.g., To some extent a person's IQ score predicts the IQ scores of his/her parent

    14. Correlational design and prediction Within a correlational studies, the two variables are essentially equivalent. But they are each given a name: the predictor variable e.g., GRE scores and college performance the criterion variable e.g., graduate school performance

    15. Correlational design and validity Relatively strong external validity Observe behavior as it naturally occurs Relatively low internal validity Limits in causal conclusions Directionality problem Third variable problem

    16. Scatterplot A visual picture of the relationship between two variables Plots individual data points One variable on each axis Visualize the strength and direction of the relationship

    17. Scatterplot

    18. Scatterplot

    19. Strength and direction of a correlation Strength Amount of predictability that is possible A stronger relationship leads to better predictability

    20. Strength and direction of a correlation Direction Specifies in what way two variables change together Positive: Increase in one variable is accompanied by an increase in the other variable Decrease in one variable is accompanied by a decrease in the other variable Negative: Increase in one variable is accompanied by a decrease in the other variable Decrease in one variable is accompanied by an increase in the other variable

    21. Direction of a relationship

    22. Strength and direction of a correlation

    23. Example: Head size and memory Is there a relationship between head size and memory? Variable A: Measure the circumference of a person’s head (in centimeters). Variable B: Record the number of words recalled from a list of 30.

    24. Hypotheses for correlations Research hypothesis Predicts there IS a relationship between two variables Variable A is (positively or negatively) correlated with Variable B Changes in the value of A will (positively or negatively) correspond to changes in the value of B Example: Head size is positively correlated with memory ability. As head size increases, memory ability will increase.

    25. Hypotheses for correlations Null hypothesis Predicts there is NO relationship between two variables Variable A is not correlated with Variable B Changes in the value of A will not correspond to changes in the value of B Example: Head size is not related to memory ability.

    26. Hypotheses for correlations Construct level: Research (nondirectional): Head size is correlated with memory ability Research (directional): Head size is positively correlated with memory ability Null: There is no relationship between head size and memory

    27. Hypotheses for correlations Operational level: Research (nondirectional): Head circumference will be related to the number of words recalled. Research (directional): Head circumference will be positively related to the number of words recalled. As the circumference of the head increases, the number of words recalled will increase. Null: The circumference of a person’s head will not be related to the number of words he/she recalls.

    28. Head size and memory

    29. Correlation coefficient A mathematical index of the relationship between two variables Symbolized by the letter “r”

    30. Correlation coefficient Direction Positive (+) Negative (-)

    31. Strength of correlation coefficients .8 – 1.0 very strong relationship .6 - .8 strong relationship .4 - .6 moderate relationship .2 - .4 weak relationship 0 - .2 very weak relationship (+ or -)

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