Hierarchical multiple regression is a statistical method used in regression analysis to explore the relationship between a dependent variable and multiple independent variables while accounting for the influence of different sets of variables in a specific order or hierarchy. The term “hierarchical” indicates that the independent variables are entered into the regression equation in a specific sequence based on theoretical or practical considerations.

Here’s a general overview of how hierarchical multiple regression works:

1. Stepwise Entry of Variables:
   • Variables are grouped into sets or blocks based on the researcher’s hypotheses or theoretical considerations.
   • The independent variables are entered into the regression equation in a predetermined order, typically in multiple steps or blocks.
2. Model Building:
   • In the first step, one set of variables is entered into the model and their contribution to explaining the variance in the dependent variable is assessed.
   • In the second step, additional variables are added to the model, and their contribution to explaining the remaining variance is evaluated.
3. Assessment of Variable Contributions:
   • At each step, the change in the variance explained by the model is examined to determine the unique contribution of each set of variables.
4. Interpretation:
   • The final model includes all sets of variables, and the coefficients for each variable represent its contribution to predicting the dependent variable while accounting for the other variables in the model.

Hierarchical multiple regression is particularly useful when there is a theoretical reason to believe that certain variables should be entered into the model before others. This method allows researchers to examine the incremental contribution of each set of variables and provides insights into how the variables interact in explaining the variation in the dependent variable.

It’s important to note that the order in which variables are entered into the model can influence the results, and the order choice should be guided by a solid theoretical rationale or prior empirical evidence. Additionally, hierarchical multiple regression assumes that the relationship between the independent and dependent variables is linear. If this assumption is violated, alternative methods may be more appropriate.

To see a demonstration of this analysis technique using SPSS, please click here: https://www.youtube.com/watch?v=xgA8qY63dX0&list=PLtx0cY9iho28Iw0o97hVjao2NB-LLd9wT&index=3