Correlational Design

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Correlational Design
The following form will help you generate a visual representation of your correlational (quantitative) research design. After submitting the form,  you will receive an email with the generated graphic in pdf format, as well as a link so that you can modify its content. You can see an example of the graphic that will be generated, in the following link:

The main objective of correlational designs is to establish relationships between variables and / or to make a prediction about the possible future behavior of a situation based on these relationships.

Two variables are related when they vary together. These covariations can be positive or negative. A relationship is positive when the values ​​of both increase or decrease simultaneously; that is, as "a" increases, "b" increases or as "a" decreases, "b" decreases. For example, in children between 1 and 10, height and weight probably have a positive relationship. While the age and physical strength in a sample of people between 30 and 80 years will have a negative relationship; that is to say that the high scores in one variable are related to the low scores of the other.

Correlational studies, as well as descriptive ones, are usually simple in terms of designs and, in many of them, two variables are enough to establish a relationship. Only the level of measurement to apply the correlation coefficient will have to be taken into account. In fact its use is frequent and recommendable for new researchers in the investigation.

It is common to find in some works a direct relationship between correlation and causal relationships. It is one of the biggest risks in these designs and it is a major error. In correlational designs, although sometimes you can have some assumption that one variable may be causing another, they do not prove cause-and-effect relationships.
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