Summary lecture 1, Emperical research project for IB
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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:
Tips:
Include interaction terms if hypothesis is conditional:
Conditional hypothesis:
Include all constitutive terms: include each of the elements that constitute interaction term:
Interpret 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 z
Meaningful MEs and SEs:
Correct interpretation of B’s involve derivation of marginal effects of the independent variables and the uncertainty with which they are estimated
Without 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:
If z is continuous a graphical analysis is required:
What is mediation:
The impact of x on y is driven by the presence of another variable, example, w is mediator and then the explanatory mechanism that drives the relation between x and y.
Moderation: conditional
Mediation: the relationship runs via a third variable w, y depends on mediator and mediator depends on x. Can be causcal result on x. Mediator explains process
Article:
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