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A good starting point to research and very good at showing relationship between variables but doesn't demonstrate cause and effect
NO. correlation just (implies) a relationship ... for example, both may be caused by the same thing.
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
a. The correlation between X and Y is spurious b. X is the cause of Y c. Y is the cause of X d. A third variable is the cause of the correlation between X and Y
Absolutely not. The simplest way to demonstrate this is to consider a measure of agreement - disagreement. If we scored it so that "strongly agree" is 5 and "strongly disagree" is 1, we would get one value of the correlation. If we reverse-scored it, we would get exactly the same value, but with the opposite sign. The strength of the correlation is the same, but the direction of the relation has switched. Another consideration is the fact that the actual strength of the correlation is based on the square of its value. 0.20 squared is 0.04; 0.40 squared is 0.16. A correlation of 0.40 is four times as strong as a correlation of 0.20. But when you square something, you automatically lose the sign. The square of a negative number is positive. So by definition, correlations of the same size but different signs are equal in strength.