What type of regression allows researchers to define the order of predictors entered into the model?

Hierarchical regression is a statistical method that allows researchers to enter predictor variables into the regression model in a specific order based on theoretical or empirical reasoning. This technique is particularly useful when researchers want to assess the incremental value of adding new predictors to the model after accounting for existing ones.

In hierarchical regression, predictors can be entered in multiple blocks. For instance, a researcher might first enter demographic variables (such as age and gender) in the initial block, followed by psychological variables (like anxiety and depression scores) in a subsequent block. By doing this, researchers can examine how much variance in the outcome variable is explained by the new predictors after controlling for the variance explained by the previous ones. This approach helps in understanding the unique contributions of different sets of predictors and in making clearer interpretations of relationships in the data.

Other forms of regression, such as simple regression, multiple regression, and linear regression, typically do not allow for the structured entry of predictors in this particular way or focus on assessing relationships without the emphasis on order of entry or the examination of the incremental effects of variables.