SPSS AMOS Moderation Analysis

Moderation Analysis in SPSS AMOS

The Focus of the video tutorial is guiding how to perform Moderation Analysis in SPSS AMOS.

Concept of Moderation

Moderation is where the direct influence of an independent variable on a dependent variable is altered or changed because of a third variable.
This third variable, called the “moderator”, can influence the strength (and sometimes sign) of the relationship from the independent variable to the dependent variable.
A moderator is said to “interact” with the independent variable to determine the influence on the dependent variable.
Thus, you will hear the term “interaction” when testing for moderation where the combined effect of the independent variable and the moderator is examined.

Test for Moderation

There are numerous ways to test for moderation using SEM. The first method I will discuss is the “interaction term” method.
An interaction term is where you form a product term of the independent variable and the moderator.
This interaction term will then let you know if the presence of the moderator is significantly influencing the relationship from the independent variable to the dependent variable.
If your moderator is a continuous variable, the interaction term method is the preferred option in moderation testing.

Example

Let’s look at a moderation example that uses an interaction term.
For simplicity, I am going to use a path model with composite variables to initially show how a moderation test is performed.
Later, in coming video tutorials, I will show you how to perform a moderation test with a full structural model.
Using our example from the path model test, Collaborative Culture will lead to improved Organizational Performance. If we say that the relationship from Collaborative Culture to Organizational Performance is moderated by level of Role Ambiguity.
We need to see how the interaction of Collaborative Culture and Role Ambiguity influence Organizational Performance.

Procedure - Step 1: Mean Centering

To assess this interaction, we need to form a product term of Role Ambiguity and Collaborative Culture.
A problem that can occur with a product term is the issue of high collinearity with the original constructs which can cause problems in the analysis (Frazier et al. 2004).
One way to circumnavigate this problem is to mean center the variables in your data.
There has been an ongoing debate on whether mean centering is necessary. Previous research has stated the results are essentially the same whether you mean center or leave the data in its raw form (Echambadi and Hess 2007; Hayes 2018).
While the differences between these methods are minimal, the advantage of mean centering the data is not only are you accounting for potential collinearity issues, but it also makes the interpretation of results easier. Thus, the recommendation to mean center your data before analyzing the data is encouraged (Dawson 2014).
In testing for moderation, we need to mean center the independent variable and the moderator before we form the product term.
To mean center the data, we first need to get the mean for the independent variable and moderator.
In SPSS, go to the “Analyze” option on the top menu, then go to “Descriptive Statistics” and then “Descriptives”.

This will bring up a descriptives pop-up window. Since this is a path model, we are concerned only with the composite variables of the independent variable and moderator.

In the Descriptives window, you need to select your independent variable (Collaborative Culture(CC)) and moderator (Role Ambiguity(RA)) and hit “OK”.
This will give us the mean value for each composite variable. CC was 4.7019 and RA was 2.6598 (on a 1–7 Likert Scale).

Once we have the mean values, we need to form a new variable that is mean centered. In SPSS, go to the “Transform” menu option and “Compute Variable”.

For CC, let’s call the new variable “centerCC” and in the numeric expression we will subtract the mean from the original variable of “CC” (composite variable of Collaborative Culture) – (CC-4.7019).

Let’s do the same thing with the moderator. We will call that variable “centerRA”. – (RA-2.6598).

The new variables will be listed in the last columns of the SPSS data file.

To verify Mean Centring your results. Check the Descriptives of the newly formed CenterCC and CenterRA. You can see that the mean for those variables is listed as a zero.
The standard deviation is the exact same for the mean centered and original variables.

This concludes the step 1

Procedure - Step 2 - Interaction Term

Once we have created the new centered variables, we need to create a product term (interaction) variable. Let’s go back to the “Compute Variable” function in SPSS.
Now we are going to multiply the two centered variables of “centerCC” and “centerRA”.
This will create an interaction term that we are going to need to assess moderation. Let’s call the interaction variable “InterCCxRA”.

Once we have formed the mean centered variables and the interaction variable, we are ready to save the data and then go to AMOS to draw our moderation model in the graphics window.

Procedure - Step 3 - Modelling in AMOS

In AMOS, to test for moderation you need to include a path from the moderator and interaction variable to the dependent variable, which in this example is Organizational Performance (OP).
We will have three paths leading to OP: the path from the independent variable, the moderator, and the mean centered interaction of those two constructs.
Note that you need only to bring in the centered interaction variable to AMOS; all other constructs can be the original composite variables.
Also, Make sure to add error term to the Dependent variable (OP).

Here is the Summary of the Steps

Analyzing the Output

Let’s go to the “Estimates” link in the output. Notice that our interaction term is negative and significant.
This means that the relationship from Collaborative Culture to Organizational Performance is being weakened by Role Ambiguity.
If the interaction was significant but positive, this would indicate that in the presence of the moderator, the relationship from Collaborative Culture to Organizational Performance is strengthened.
Our moderator has a significant direct relationship to OP; but even if it was non-significant, that is okay because we are really concerned only with the interaction to determine if moderation is present.
If interaction term was non-significant, then we could say that there is evidence that RA is not moderating the relationship from CC to OP.

Review

Just to recap, in testing for moderation with a continuous variable, you need to form a mean centered interaction term that is a product of the moderator and independent variable (Mean Centered IV and Moderator).
In AMOS, you will form a direct relationship from the Composite independent and Moderating variable, and mean centered interaction to the specified dependent variable.
From there, you will examine the interaction term in the analysis to determine if the “interaction” between the moderator and independent variable is influencing the strength of the relationship of the independent variable to the dependent variable.

Reporting Moderation Analysis

Moderation Analysis

The study assessed the moderating role of Role Ambiguity (RA) on the relationship between Collaborative Culture (CC) and Organizational Performance (OP). The results revealed a negative and significant moderating impact of RA on the relationship between CC and OP (b= -0.095, t = -2.767, p = .006), supporting H1. Moderation analysis summary is presented in Table 1.

Results of simple slope analysis conducted to better understand the nature of the moderating effects are shown in Figures 1. As can be seen in Figure 1, the line is much steeper for Low RA, this shows that at Low level of RA, the impact of CC on OP is much stronger in comparison to high RA. As shown in Figure 2, as the level of
RA increased, the strength of the relationship between CC and OP decreased.

Reference

Collier, J. E. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques. Routledge.

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