Читать книгу Methods in Psychological Research - Annabel Ness Evans - Страница 94

If predicting someone’s performance using one predictor variable is a good idea, using more than one predictor variable is a better idea. Entire textbooks are devoted to multiple regression analysis techniques, but the basic idea is to use more than one predictor variable, X₁, X₂, X₃, and so on, to predict one criterion variable, Y. As with simple regression, multiple regression requires the fitting of a line through your data, but first, all the predictor variables are combined, and then the linear combination of Xs is correlated with Y. It is easy to visualize multiple regression with two predictors. This would be a line in three-dimensional space. Imagining more than two predictors is difficult and fortunately not necessary. Multiple regression produces an r value that reflects how well the linear combination of Xs predicts Y. Some predictor variables are likely to be better predictors of Y than others, and the analysis produces weights that can be used in a regression equation to predict Y. Simply multiply the values of the predictor variables by their respective weights, and you have your predicted value.

Y(predicted) = B₁(X₁) + B₂(X₂) + B₃(X₃) + … + Constant

In addition to the weights used to predict criterion values, multiple regression analysis also provides standardized weights called beta (β) weights. These values tell us something about each individual predictor in the regression analysis. They can be interpreted much like an r value, with the sign indicating the relationship between the predictor variable and the criterion variable and the magnitude indicating the relative importance of the variable in predicting the criterion. Thus, in a multiple regression analysis, we can examine the relative contribution of each predictor variable in the overall analysis.

As you just learned, multiple regression is used to determine the influence of several predictor variables on a single criterion variable. Let’s look briefly at two useful concepts in multiple regression: (1) partial and (2) semipartial (also called part) correlation.