1. CORRELATION RESEARCH / STUDIES

2. Correlation and Research In correlation studies, researchers observe or measure a relationship between variables in which changes in one variable are reflected in changes in the other variable. In these studies, researchers DO NOT DIRECTLY MANIPULATE the variables. Correlations are used to analyze the data gathered in any type of descriptive method.

3. Correlation When one trait or behavior accompanies 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 = 0.37 + Correlation Coefficient is a statistical measure of the relationship between two variables.

4. More on the Correlation Coefficient A correlation coefficient is a numerical value that indicates the strength and direction of the relationship between two variables. Correlation coefficients are calculated by a formula that produces a number ranging from +1.00 to -1.00.

5. We’ve got data, now how do we begin????

6. Data Data showing height and temperament in people.

7. Identify the variables The Independent Variable (IV) is the height. The Dependent Variable (DV) is the Temperament. Use a Scatterplot to depict the data. –Usually the Dependent Data is on the Vertical Axis. –Usually the Independent Data is on the Horizontal Axis.

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

9. Scatterplot The Scatterplot below shows the relationship between height and temperament in people. There is a moderate positive correlation of +0.63.

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

11. In the numbers… Correlations become stronger as they approach either -1.0 or +1.0. For instance: –A positive correlation of +.87 means that there is a very strong relationship between the two variables. –A negative correlation of -.83 means that there is a very strong INVERSE relationship. The strength of the correlation weakens as the correlation coefficient approaches 0.00.

12. Positive Correlation Indicates that two variables move or vary in the same direction. Example: Studies have found a positive relationship between smoking and the incidence of lung cancer. That is, as frequency of smoking increases, so does the incidence of lung cancer.

13. Negative Correlation Indicates that two variables move or vary in opposite directions. Example: Studies have found a negative correlation between level of education and anger. That is, as level of education increases, expressions of anger decrease.

14. Zero Correlation Indicates that there is no relationship between two variables. Example: A study by Isabelle Deltour for the Danish Cancer Society found no correlation between cell phone use and incidence of brain tumors.

15. Correlation and Causation Correlation studies indicate the possibility of a cause-and-effect relationship. Remember that correlation does not PROVE causation. –Example: Studies have found a moderate correlation of +0.4 between SAT scores and college grades. However, this correlation does not tell us if high SAT scores cause high college grades.

16. Con’t. Other known and unknown factors, such as the level of achievement motivation and the presence or absence of tutors, could be responsible for both the SAT scores and the college grades.

17. Correlation Cautions… Potential for Statistical Abuse Correlation only shows that two variables are RELATED and may not necessarily show cause and effect. Correlation may be a coincidence or there may be a common underlying cause.

18. A Few Examples Negative correlation of weight of vehicle and gas mileage (cause and effect). Positive correlation of number of NFL football players and number of truck drivers over a period of 20 years. (No cause and effect – common underlying cause) Positive correlation of number of full-time students at West Chester and Federal debt over the past 10 years. (no cause and effect)

19. or Correlation and Causation Correlation does not mean causation!

20. Advantages of Correlation Studies They can be used to describe or clarify a relationship between two variables. They can be an efficient way to utilize preexisting data.

21. Also…it can dispel Illusory Correlations They can be used to dispel illusory correlation. Although widely believed, an illusory correlation is in fact non-existent. –Example: It is widely, but erroneously, believed that there is a correlation between date of birth and personality traits.

22. Disadvantages of Correlation Studies They cannot be used to establish cause- and-effect relationships. They cannot be used to establish the direction of causal influence. They do not allow researchers to actively manipulate the variables. They make it difficult to identify the impact of confounding variables.