Multiple linear regression tutorial - General steps
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View the accompanying screencast: [1]
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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, R2, Adjusted R2, sig. of R
- Change in R2 and the sig. of this change (if a hierarchical MLR is conducted)
- Regression coefficients
- Y-intercept (labelled "Constant" in the SPSS MLR Coefficients table output)
- Unstandardised (B)
- Standardised (β (beta))
- t and significance for each predictor
- Zero-order correlations (r) and semi-partial correlations squared (sr2) 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)
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