What term describes a numerical measure of the relationship between a predictor variable and a criterion variable?

The term that accurately describes a numerical measure of the relationship between a predictor variable and a criterion variable is the correlation coefficient. This statistical measure quantifies the degree to which two variables are related, indicating both the direction and strength of the relationship. A correlation coefficient can range from -1 to +1, where values close to +1 imply a strong positive relationship, values close to -1 indicate a strong negative relationship, and values around 0 suggest no relationship at all.

Understanding the correlation coefficient is crucial in fields such as social sciences, finance, and natural sciences, where it helps researchers and analysts determine how effectively one variable can predict another. In contrast, variance measures the spread of a single variable, standard deviation indicates the degree of variation or dispersion of a dataset, and regression coefficients describe the slope of the line in a regression analysis, indicating how much the criterion variable is expected to change with a one-unit change in the predictor variable. Though related to predictive analysis, these terms do not specifically quantify the relationship between two separate variables in the same way that the correlation coefficient does.