Chapter 20 Multiple Regression Analysis

To explore the association between multiple predictors and a continuous dependent variable, regression analysis can be used. In this example, a dataset/dataframe called dat contains three continuous variables, predictorVariable1, predictorVariable2, and dependentVariable, and one nominal variable predictorVariable3.

20.1 jamovi

Go to the Regression menu and select ‘Linear Regression’. Numerical variables are dragged to the window, labeled ‘Covariates’. Nominal variables (factors) are dragged to the window, labeled ‘Factors’.
Select the variables for the regression analysis

Figure 20.1: Select the variables for the regression analysis

20.2 R

The most simple function to use is lm (linear model), which comes with R base. The summary function shows the most important results.

result <- lm(dependentVariable ~ predictorVariable1 + predictorVariable2 + predictorVariable3,
     data=dat)
summary(result)

20.3 SPSS

REGRESSION
  /DEPENDENT dependentVariable
  /METHOD ENTER predictorVariable1 predictorVariable2 predictorVariable3
  /STATISTICS COEF CI(95) R ANOVA.