Explain why most researcher use the default(Enter) method for model building.
To understand this, you need to understand the logic that underlies multiple regression. In multiple regression, we're trying to predict one variable (y) from a set of other variables (x1, x2, x3, etc). We decide whether each x variable should stay in the regression model by looking at how much variance in y each explains. Only variables that explain a lot of variance in y stay in the model.
Let's start simple. Imagine we're doing a simple regression, where we're trying to predict a variable (y) from a single variable (x). For example, if we were predicting 'years of schooling' from 'age', we would expect that age (x) would explain a lot of variance in years of schooling (y) (and would be a significant predictor in the model). In contrast, if we were predicting 'years of schooling' from 'hair colour', we would expect that hair colour (x) was not a good predictor of years of schooling (y): it would not explain a lot of variance in y (and would not be a significant ...
Explains the theory underlying the use of the default Enter method in multiple regression analyses.