The formula to calculate Cohen's D is:
\[ D = \beta \times \sigma \]
where \( D \) is Cohen's D, \( \beta \) is the beta coefficient, and \( \sigma \) is the standard deviation of the predictor.
Cohen's D is a measure of effect size used in statistics to indicate the standardized difference between two means. It is commonly used in the context of t-tests and ANOVAs to understand the magnitude of differences between groups. Cohen's D is calculated by taking the difference between two means and dividing it by the standard deviation. This metric is useful for comparing the effect sizes across different studies and contexts, providing a standardized way to interpret the strength of an effect.
Let's assume we have the following values:
Step 1: Multiply the beta coefficient by the standard deviation:
\[ D = 0.5 \times 2 = 1 \]
Therefore, Cohen's D is \( D = 1 \).
