Using your creativity, share specific instances where you would expect to find when comparing two different variables a strong, positive correlation, a very weak correlation, and no correlation at all. Secondly, why do researchers insist that you cannot show causation when doing correlation?
Discussion Question: Must be at 250 words.
Read: Ravid, R. (2020). Practical statistics for educators (6th ed.). Rowman & Littlefield Publishers. ISBN: 9781475846836. Read chapters 7 & 8.
After reading the textbook, explain the difference between a simple linear regression and a multiple regression. Next, explain a useful multiple regression that you could complete using data from your present occupation.
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Instances where you would expect to find when comparing two different variables
Full Answer Section
No correlation: This occurs when there is no relationship between two variables. For example, there is no correlation between the number of times a person eats ice cream and their level of intelligence. In other words, eating ice cream does not have any impact on a person's intelligence.
Here are some reasons why researchers insist that you cannot show causation when doing correlation:
- Correlation does not imply causation. Just because two variables are correlated does not mean that one causes the other. For example, there is a correlation between the number of times a person sees a therapist and their level of happiness. However, this does not mean that seeing a therapist causes happiness. It is also possible that happy people are more likely to see a therapist.
- There may be other variables that are causing the correlation. For example, there is a correlation between the number of times a person eats ice cream and their level of intelligence. However, this correlation may be caused by a third variable, such as the person's socioeconomic status. People from higher socioeconomic statuses are more likely to eat ice cream and also more likely to be intelligent.
- The correlation may be due to chance. Just because two variables are correlated does not mean that the correlation is statistically significant. A statistically significant correlation means that the correlation is not likely to be due to chance.
It is important to remember that correlation is a statistical measure of the relationship between two variables. It does not prove that one variable causes the other. To establish causation, researchers must conduct experiments that control for other variables and isolate the effect of the variable of interest.
Sample Answer
Strong, positive correlation: This occurs when there is a direct relationship between two variables. For example, there is a strong, positive correlation between the amount of sleep a person gets and their academic performance. In other words, the more sleep a person gets, the better their academic performance is likely to be.
Very weak correlation: This occurs when there is a very small relationship between two variables. For example, there is a very weak correlation between the number of times a person sees a therapist and their level of happiness. In other words, seeing a therapist may have a very small impact on a person's happiness.
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