Evaluate the fundamentals of correlational research. Use examples
What will be an ideal response?
Correlational research focuses on the nature of relationships between two or more variables. For example, a college admissions officer may want to know if there is a relationship between grades in high school and grades in college. A physician may be interested in whether the amount of fat in someone's diet is related to heart disease. In any case, a mathematical computation allows one to quantify the strength of the relationship. This is represented by a correlation coefficient. This can range from –
1.00 to +1.00 . The algebraic sign indicates the direction of the relationship. A negative sign indicates that as one variable increases, the other decreases. A positive sign indicates that as one variable increases, the other also increases (likewise, as one variable decreases, the other decreases). The size of the number indicates the
strength of the relationship. Numbers close to –1.00 or +1.00 indicate a strong relationship. Numbers near 0 (0.03; +0.08) indicate a very weak relationship. Correlational research is useful for making predictions. However, it cannot be used to establish cause-and-effect relationships. For example, if there is a correlation between dietary fat and heart disease, the physician cannot conclude that the fat causes the heart disease (this does not mean dietary fat does NOT cause heart disease, only that correlational research does not permit a cause-and-effect conclusion).
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