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Bivariate correlation is a measure of the relationship between the two variables; it measures the strength of their relationship, which can range from absolute value 1 to 0. The stronger the relationship, the closer the value is to 1. The relationship can be positive or negative; in positive relationship, as one value increases, another value increases with it. In the negative relationship, as one value increases, the other one decreases.
Example / Application
For example, the positive relationship of .90 can represent positive correlation between the hot weather temperatures and the sales of ice. The hotter the weather, the more ice is sold. The negative correlation can be found between going on the shopping spree and your savings. The more you shop, the less you can put aside.
In the video they show the
experiment in which a researcher proposed how the phenomenon of group conformity affects the way people make their decisions. Actors sitting next to the volunteer are asked to say the wrong answer, while volunteer is being monitored on whether he will give the right answer or will go along with the majority’s opinion. They don’t show it in the video. but if they announced the positive relationship of .90 between actors’ giving a certain answer and volunteer repeating that answer, it would indicate to positive correlation between the two variables. In other words, bivariate correlation would measure the strength of the relationship between the two variables: the "number of actors providing the wrong answer" and the rate at which volunteer’s opinion conforms to the majority’s wrong answer.
Field, A. (2006). Discovering Statistics Using SPSS: Second Edition. London. Thousand Oaks. New Delhi. Sage Publications.