1. Cause and Effect

2. Usually the main reason for a correlational study is to prove that a change in X produces a change in Y. For example a school board may want to know whether calculators help students learn math However, a strong correlation does not prove that one variable causes the other to change. For example, after WW2, there is a strong positive correlation between the number of storks and the annual number of human births in Copenhagen.

3. What Causes what!!!!

4. Cause and Effect

5. Types of Causal Relationship We are going to look at the different relationships between variables we might see

6. Cause and Effect Relationship A change in X ACTUALLY produces a change in Y Increasing the height from which you drop and object increases its impact velocity. Increasing the speed of a production line increases the number of items produced.

7. Common Cause Factor An external Variable causes both variables to change in some way A town finds that revenue from parking fee’s at the public beach each summer correlates with the sales at local restaurants Good weather is the common cause factor that increases both variables.

8. Reverse Cause and Effect The dependent and independent variable are reversed in the process of establishing causality. A researcher finds a positive linear correlation between the amount of coffee consumed by a group of medical students and their levels of anxiety. Researchers theorize that drinking coffee causes nervousness, but instead finds that nervous people are more likely to drink coffee.

9. Accidental Relationship A correlation exists without any causal relationship The number of females enrolled in an engineering program and the number of reality shows on TV both increased for several years. The correlation is likely coincidental.

10. Presumed Relationship A correlation does not seem to be accidental even though no cause- and-effect relationship or no common-cause factor is apparent. Suppose you found a correlation between peoples level of fitness and the of adventure movies they watched. It seems logical that a fit person might prefer action movies, but it would be difficult to find a common cause of prove that one variable effects the other.

11. What kind of Relationship? The rate of a chemical reaction increases with temp. Cause and Effect- higher temps cause faster reactions

12. What kind of Relationship? A scatterplot of the number of storks in Oldenburg, Germany plotted against the population of the town shows a strong positive correlation Reverse Cause and Effect – it turns out that storks nest on house chimneys. More people means, more nesting sites and so more storks.

13. What kind of Relationship? An increase in the number of students enrolled in Data Managements and increase in the number of BWM’s on the road Accidental – The correlation between the number of students enrolled in data management and the number of BWM’s is coincidental

14. What kind of Relationship? Heart attack rates drop as fitness clubs bring more revenue. Presumed relationship – A negative correlation seems logical however there is no apparent common cause-factor or cause and effect relationship.

15. Extraneous Variables Deterring the nature of causal relationships can be complicated by the presence of extraneous variables. These are just other variables that can change an outcome.

16. Control Group and Experimental group To reduce the effect of extraneous variables researchers often compare an experimental group to a control group Groups should be as similar as possible Researchers vary the independent variable for experimental group but leave control group the same.

17. Control Group and Experimental group A researcher wants to test a new drug believed to help smokers overcome the addictive effects of nicotine. One group is given nicotine patches while the second is given an ordinary patch (placebo).

18. Assignment Pg 199-201 #’s 1-4, 6, 10, 14