What is a correlation coefficient?

A correlation coefficient is a statistical tool that measures the strength and direction of the linear relationship between two variables. It's a numerical value, typically represented by the letter "r," that falls between -1 and 1.

Here's a breakdown of what the coefficient tells us:

  • Strength of the relationship:

    • A positive correlation coefficient (between 0 and 1) indicates that as the value of one variable increases, the value of the other variable also tends to increase (positive association). Conversely, if one goes down, the other tends to go down as well. The closer the coefficient is to 1, the stronger the positive relationship.
    • A negative correlation coefficient (between -1 and 0) signifies an inverse relationship. In this case, as the value of one variable increases, the value of the other tends to decrease (negative association). The closer the coefficient is to -1, the stronger the negative relationship.
    • A correlation coefficient of 0 implies no linear relationship between the two variables. Their changes are independent of each other.

It's important to remember that the correlation coefficient only measures linear relationships. It doesn't capture other types of associations, like non-linear or categorical relationships. While a strong correlation suggests a possible cause-and-effect relationship, it doesn't necessarily prove it. Other factors might be influencing both variables, leading to a misleading correlation.

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What is internal validity?

What is internal validity?

In the realm of research, internal validity refers to the degree of confidence you can have in a study's findings reflecting a true cause-and-effect relationship. It essentially asks the question: "Can we be sure that the observed effect in the study was actually caused by the independent variable, and not by something else entirely?"

Here are some key points to understand internal validity:

  • Focuses on the study itself: It's
...
Understanding reliability and validity
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