In regression analysis, what does a standardized beta coefficient indicate?

• A. Indicates the expected change in the dependent variable in standard deviations for a one SD change in the predictor, holding other predictors constant.
• B. Shows the raw units change in Y per unit change in X.
• C. Represents the correlation between X and Y.
• D. Measures the proportion of variance explained by the predictor.

Answer: (A)

Standardized beta is about effect size in standardized units. When all variables are transformed to z-scores, the regression slope tells you how many standard deviation units the dependent variable would change for a one standard deviation increase in the predictor, while holding the other predictors in the model constant. This makes the sizes of different predictors directly comparable, since they’re all measured on the same scale.

So, the coefficient conveys the unique, adjusted impact of that predictor on the outcome, independent of the original measurement units. It’s not giving a raw unit change in Y, and it’s not simply the correlation between X and Y (which doesn’t account for other predictors in the model). In simple (one-predictor) regression, the standardized beta and the correlation can align, but in multiple regression they reflect the predictor’s effect after controlling for others, expressed in standard deviation units.