Lecture 6:Moderation: the relationship between x and y depends on a third variable z, z is than the moderation. In a regression multiplicative interaction term between the predictor x and the moderator z. For making hypothesis: effects become stronger, weaker or change direction.Moderation: statistical approach:Regress depend variable on:Predictor (x)Moderator (z)Interaction of the two (xz)Tips: Include interaction terms if hypothesis is conditional:Conditional hypothesis:A relationship between two or more variables depends on the value of one or more other variablesAn increase in x is associated with an increase in y when condition z is met, but not when condition z is absentA positive effect of x on y gets stronger as z increasesConditional hypotheses can be tested using interaction termsInclude all constitutive terms: include each of the elements that constitute interaction term:If you include xz, always include x and yIf you include xzj, always include x, z, j, xz, zj and xj If you include X^2, always include xInterpret correctly:Interpretation of B is now different, so B2 does no longer explains the increase of x on y (Only when z is zero, it does) to see this you should calculate the marginal effect of x on y for different values of zMeaningful MEs and SEs:Correct interpretation of B’s involve derivation of marginal effects of the independent variables and the uncertainty with which they are estimatedWithout interaction With interaction:Traditional results tables only report Bs and SEs, but we can only tell whether x has an significant effect on y when z is 0, however this could also be on other variables. If z is binary present four number:Marginal effect of x when z is 0 and z is 1The corresponding standard errorsIf z is continuous a graphical analysis is required:Plot the marginal effect of xAcross a substantively meaningful range of zAnd add the confidence interval for assessing significance...
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