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what is a correlation?
measurement of how strongly two or more variables are related
the variables are called co-variables, no cause-and-effect established as there is no IV
types of correlation
positive - both co-variables increase or decrease
negative - one variable increases and the other decreases
no - no relationship between them
hypotheses are the same as experiments
correlation coefficients
shows the strength and direction of a correlation - +1 is perfect positive and -1 is perfect negative
scatter grams are used to display data - one variable on each axis
sometimes there are curvilinear relationships - more complex
pros of correlations
precise, quantifiable measure of relationship
quick and economical to do
suggest patterns that lead to experiments
used to study sensitive topics
allow us to study naturally occurring variables
cons of correlations
cannot demonstrate cause and effect so we don’t know what is changing what - bidirectional ambiguity
another variable may cause the relationship between the two co-variables - intervening variable
correlations can be easily misinterpreted
