SPSS, part 6: Regressions


Regression can be used to predict variables:

  • IV: x axis (predictor)
  • DV: y axis (criterion / outcome)

Analyze -> Regression -> Linear

  •  move variables into corresponding boxes
  • ensure “Method = Enter”
  • in SPSS
    • raw equation: DV = (B [slope] x IV) + constant [intercept]
    • standardised: ZDV = β [beta] x ZIV (standardised variables to Z scores)
    • standardised is a better indicator of strength and measure independent of units
  • in multiple regression (more than one IV) β measures unique effect of IV – no shared variance
  • look in last table for β value (the correlation) and B and constant
  • look in ‘model summary’ table for R2 (proportion of variance explained by all IVs)
  • look in ‘ANOVA’ table for F value (to check if R2 predicts DV better than chance – if F is significant then R2 is better)
  • R2: need to multiply by 100 to get %