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3.4 Cause and Effect

3.4 Cause and Effect. For correlation, any change in x corresponds to a change in y. A high r value indicates the strength of the relation of two variables. Examples.

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3.4 Cause and Effect

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  1. 3.4 Cause and Effect • For correlation, any change in x corresponds to a change in y. • A high r value indicates the strength of the relation of two variables.

  2. Examples • In the early part of the twentieth century, it was noticed that, when viewed over time, the number of crimes increased with membership in the Church of England.

  3. Examples • During WWII it was noticed that bombers were less accurate when the weather was clearer.

  4. Cause and Effect • Sometimes a high r value does not prove that… a change in x caused a change in y!!

  5. Page 195 • Read text and do investigation questions 1,2,3

  6. Types of Causal Relationships • Cause and Effect • Common Cause • Reverse Cause and Effect • Accidental Relationship • Presumed Relationship

  7. Cause and Effect • A change in X produces a change in Y • Usually very apparent • Ex, hours worked and weekly earnings

  8. Common Cause • An external variable causes two variables to change in the same way • In the early part of the twentieth century, it was noticed that, when viewed over time, the number of crimes increased with membership in the Church of England. both crimes and Church membership increased as the population increased. Association does not imply causation!

  9. Reverse Cause and Effect • Independent and dependent variables are reversed when trying to determine causality • Ex. In a study, a positive correlation exists between amount of coffee consumed and level of anxiety.

  10. Reverse Cause and Effect • Researcher theorizes that drinking coffee causes anxiety… • but instead finds the opposite was true… anxious people are more likely to drink coffee.

  11. Accidental Relationship • A correlation exists between two unrelated variables. • Increase in childhood obesity rates and the rise in the TSX index.

  12. Presumed Relationship • Given a high r value, no cause-and-effect or common-cause relationship exists between two variables… • but the correlation doesn’t seem accidental.

  13. Presumed Relationship Example • There is found to be a correlation between physical strength and the number of times per week they watched MMA. • Its makes some sense that strong people enjoy MMA but it is difficult to prove that one variable affects the other.

  14. Page 196 • Causal Relationships • Example 1a to e

  15. Extraneous Variables • Outside factors that can affect one of the variables • Ex. high school marks and first year university marks are expected to have a strong correlation. • However U marks can be affected by… ability to adapt to new environment, higher course workload, social temptation…

  16. Reduce Effect of Extraneous Variable • Researchers will study two groups: Study group and control group Ex. To assess the effect of transcendental meditation on U marks researchers have the study group perform TM each day. The control group goes about it’s regular daily routine. Final marks are recorded.

  17. Extraneous Variables If study group has higher marks, may be due to TM. Note: you must have a large sample to make definitive conclusions Read example 2 page 198

  18. Assignment • Page 199 • 1,2,3,4,5,6,7,11

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