Moderating Variable

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Introduction on Moderating Variable
Moderating variable is the variable that ‘moderates the effects’ of an independent variable to its independent variable

The social science researchers, in particular, define moderator as the variable that ‘interfere’ in the relationship between an independent variable and dependent variable. For illustration, let M be the moderator variable in the X-Y relationship. Then the moderation role of M is ‘to alter’ the effect of X on Y.

A MO variable is a qualitative (sex, religion) or quantitative (such as firm’s size, financial leverage and price) that affect the strength AND/OR direction of the relationship between the dependent or criterion variable(Y) and the independent or predictor (X) variable (Baron and Kenny, 1986)

It may be naturally occurring, measured or determined variable (e.g. age, gender, industry type) or can artificially be created by manipulation of the condition (e.g. negative/positive service quality (RO, 2012). A MO variable in facts acts like the second independent variable.

Before introducing a moderator into the model, the effect of independent variable X on its Independent variable Y must exist and significant.

Thus, when a moderator M enters the model, the causal effects would change due to some ‘interaction effect’ between independent variable X and moderator variable M just entered.

As a result, the effect of independent variable on its dependent variable would depend on the level of moderator variable.


Condition for Moderating Variable to exists:

·         X occurs before Y

·         MO maintain a causal relationship with Y.

·         MO plays same function as X

·         MO does not have any correlations with X



Moderator – Interactions:
Look at relationship between IV & Moderator, that we call as block 1. Next, we are connecting to block 2, this is 2-stage analysis. DV – intercept A into IV, b into moderator, c into IV. This is exactly called as interaction effect.
The 1st block connecting between IV & moderating relationship with the DV. 2nd block is exactly the interaction effect. Finally, we try to give better explanation to the DV when the moderator comes into the picture. DV is Y, when the moderator is not introduced then intercept is not started influencing, (0 stage) then B1, it started influencing. Before influencing Beta zero, after influencing Beta one. Beta one plus X1, Beta two plus X2. E is standard error. SPSS no provision to clear this error but Pearless and AMOSS have the ability to clear this error that why measurement model analysis we are doing. We do all the measurement model test to remove standard error, 

Example:
Taking an example, DV is endurance, IV is age and moderating variable is previous years of rigorous physical exercise. Age is IV- causative factors, DV is endurance, MO rigorous physical exercise that we also try to connect with endurance, that we call as B, the 3rd one is interaction effect that is connected to endurance, A & B we call it as block 1, The next level we call it as block 2 which is interaction affect.
Sources from: Professor Dr. Dileep Workshop on Role of Theory, Moderation & Mediation on 3 August 2019. 

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