Multiple linear regression tutorial  General steps

View the accompanying screencast: [1]

The general recommended steps for conducting a multiple linear regression analysis are:
 Conceptualise the model (e.g., draw a path diagram or Venn diagram to indicate the IVs and the DV) and establish research questions and/or hypotheses.
 Check assumptions:
 Levels of measurement
 Sample size
 Normality
 Linearity
 Homoscedasticity
 Multicollinearity
 Multivariate outliers
 Normality of residuals
 Choose the type of MLR:
 Standard
 Hierarchical
 Stepwise / Forward / Backward
 Interpret statistical output and psychological meaning of results. Consider:
 Overall model
 R, R^{2}, Adjusted R^{2}, sig. of R
 Change in R^{2} and the sig. of this change (if a hierarchical MLR is conducted)
 Regression coefficients
 Yintercept (labelled "Constant" in the SPSS MLR Coefficients table output)
 Unstandardised (B)
 Standardised (β (beta))
 t and significance for each predictor
 Zeroorder correlations (r) and semipartial correlations squared (sr^{2}) for each IV in each model
 Depict the relationships in a path diagram or Venn diagram (if useful/relevant)
 Regression equation: Present a prediction equation for Y (if useful/relevant)
