Multiple linear regression tutorial - General steps

## General steps

 View the accompanying screencast: [1]

The general recommended steps for conducting a multiple linear regression analysis are:

1. 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.
2. Check assumptions:
1. Levels of measurement
2. Sample size
3. Normality
4. Linearity
5. Homoscedasticity
6. Multicollinearity
7. Multivariate outliers
8. Normality of residuals
3. Choose the type of MLR:
1. Standard
2. Hierarchical
3. Stepwise / Forward / Backward
4. Interpret statistical output and psychological meaning of results. Consider:
1. Overall model
1. R, R2, Adjusted R2, sig. of R
2. Change in R2 and the sig. of this change (if a hierarchical MLR is conducted)
2. Regression coefficients
1. Y-intercept (labelled "Constant" in the SPSS MLR Coefficients table output)
2. Unstandardised (B)
3. Standardised (β (beta))
4. t and significance for each predictor
5. Zero-order correlations (r) and semi-partial correlations squared (sr2) for each IV in each model
5. Depict the relationships in a path diagram or Venn diagram (if useful/relevant)
6. Regression equation: Present a prediction equation for Y (if useful/relevant)