providing accurate calculations for 12+ years!

Below you will find descriptions and links to 5 free statistics calculators for computing values associated with hierarchical regression studies.

If you like, you may also use the search page to help you find what you need.

This calculator will tell you the minimum sample size required for a hierarchical multiple regression analysis; i.e., the minimum sample size required for a significance test of the addition of a set of independent variables B to the model, over and above another set of independent variables A. The value returned by the calculator is the minimum sample size required to detect an effect of the specified size, probability level, and power level for the addition of set B to the model.

This calculator will tell you the beta level (the Type II error rate) for a hierarchical regression analysis; i.e., the beta level for a significance test of the addition of a set of independent variables B to the hierarchical model, over and above another set of independent variables A. The value returned by the calculator is the beta level (i.e., the Type II error rate) for the addition of the set of independent variables B to the overall hierarchical model.

This calculator will tell you the effect size for a hierarchical multiple regression study (Cohen's f^{2}), given an R^{2} value for a set of independent variables A, and an R^{2} value for the sum of A and another set of independent variables B. The value returned by the calculator is the effect size attributable to the addition of set B to the model.

This calculator will tell you the F-value for a hierarchical multiple regression study, given an R^{2} value for a set of independent variables A, an R^{2} value for the sum of A and another set of independent variables B, the number of predictors in A, the number of predictors in B, and the total sample size. The value returned by the calculator is the the F-value associated with the addition of B to the model.

This calculator will tell you the observed power for a hierarchical regression analysis; i.e., the observed power for a significance test of the addition of a set of independent variables B to the hierarchical model, over and above another set of independent variables A. The value returned by the calculator is the observed power for the addition of the set of independent variables B to the overall hierarchical model.