Meditation and Moderation Analysis
, as the name suggests, “mediates” the relationship between the dependent and independent variables, explaining why the relationship exists. Another way to look at mediation is that the mediator variable carries an effect. During the mediation process, the independent variable makes some changes to the mediator variable, which consequently changes the dependent variable. However, when applying the technique in real life, the relationship between the dependent, mediator, and independent variable is not tested for causality; it is only tested for correlation.
The whole idea behind mediation analysis is to find out if the effect of the mediator on the dependent variable is stronger than that of the independent variable. A good example of a real-life mediator is the temperature on a cooking stove. Food will not start to cook until you have turned the stove on but in reality, it is not the knob of the stove that makes the food to cook, it is the heat that is created when you turn the knob. To analyze the relationship between turning the knob and cooking food, we could check the strength of the heat in relation to time. For the first couple of minutes after the knob has been turned on, there would be no noticeable effect, so that can be treated as a weak correlation. If we compare the relationship between the heat emitted by the stove and the state of the food, we can clearly see that it is the heat from the stove (in this case the mediator) which is causing the food to cook, not the action of turning on the knob (independent variable). If we compare the strength of the effect of turning the knob to the effect of the temperature of the stove, we will find out what is really transferring these two effects into the cooking food (the dependent variable).
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is the process of determining whether the third variable affects the direction or strength of the relationship between a dependent variable and an independent variable. The easiest way to remember this is to know that the moderator variable can alter the strength of the relationship between the other two variables from strong to moderate and sometimes to nothing at all. A moderator is like a turn dial on relationships; when you change its values, the relationship that was observed earlier would likely change or dissolve away.
For instance, if you expect that the amount of time you spend studying for an algebra test is related to the grades you score in the end, you are probably right. There is a very strong relationship between the amount of time spent studying for the test and how much you score. However, this relation may not hold up if the grade is the moderator variable. If you change the value of the grade moderator from a university student to a high school student, the relationship is likely to change too because the level of grading is different.
Another example that uses the concept of moderation is the relationship between the interpersonal differences among company staff and the staff stress compared to the behavior of the resolution supervisor. In this case, the interpersonal differences are the independent variable, stress is the dependent variable, and the behavior of the resolution supervisor is the moderator. When the supervisor is not present, the relationship between employee interpersonal differences and stress is stronger compared to when the supervisor is present. This shows that a supervisor, whether present or not, will affect the relationship between the staff’s interpersonal differences and stress. In other words, the moderator variable changes the direction or strength of the relation between the dependent variable and the independent variable. To test for moderation, simply introduce an interaction variable between the moderator variable and the independent variable.
The examples explained in each analysis should clarify the distinction between mediation and moderation variables. If you are still having trouble understanding the two techniques, feel free to contact us
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