4 Cases of MO interactions with IV

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Case 1: Moderator and IV are Categorical


  • A dichotomous independent variable’s effect on the dependent variable varies as a function of another dichotomy
  • 2 x 2 ANOVA
  • The moderation is indicated by the interaction



IV – Level of Education
DV – Income


Case 2: Moderator is Categorical and IV is Continuos

  • Deficiency: Assumes that the IV has equal variance at each level of the moderator
  • The effect of IV on DV is tested using unstandardized regression coefficient. The regression coefficients are then tested for differences (see formula, Cohen & Cohen, 1983, p.56)
  • Reliabilities should be tested foe the level of moderation, and slopes should be disattenuated



Case 3: Moderator is continuous and IV is categorical

  • We must know a prior how the effect of the IV varies as a function of the moderator

(1)  Linear Function
(2)  Step Function
(3)  Quadratic Function

  • Example: IV: Rational VAS Fear – arousing attitude change. Moderator: Intelligence (IQ test)

Case 4: Moderator and IV are continuous

  • One can dichotomise the moderator at the point where the step tales place (step function)
  • The measure of the effect of IV is a regression coefficient
  • If the effect of IV (X) on the DV (Y) varies linearly or quadratically with respect to moderator (Z), the moderator squared is introduced
  • The XZ term is tested by moderation



Moderator in Regression Analysis:

When a variable interacts with a predictor to change the relationship between that predictor and the outcome variable: increase, decrease or change direction (e.g. positive or negative)
Assessing for moderation effects depends on the characteristics of your predictor and moderator:
  • If both are categorical, either factorial, ANOVA or regression (MR used for examining shared variance)
  • If at least one is an interval/continuous variable, use multiple regression methods


Sources from: Professor Dr. Dileep Workshop on Role of Theory, Moderation & Mediation on 3 August 2019.   

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