Steps

1. Assess the measurement model

2. Find the factor score weight for each manifest variable of each of the constructs.

3. Create new variable by using the factor score weight in SPSS (Transform)

4. Standardize the variables resulted from step 3 in SPSS (Analyze, Descriptive Statistics, Descriptive). Don't forget to save standardized value as a variable.

5. Calculate the interaction effects (Standardized IV x Standardized Moderator) in SPSS.

6. Create a structural model to test hypotheses.

7. If the interaction effect is significant, then do slope analysis.

Note:

Standardizing the variables is important to reduce the effect of mulcollinearity issue. A regression model almost certainly has an excessive amount of multicollinearity if it contains polynomial or interaction terms. Fortunately, standardizing the predictors is an easy way to reduce multicollinearity and the associated problems that are caused by these higher-order terms

Interaction Plots/Slope Analysis

Why Plot?

However, it is not entirely clear how it differs. If you get a positive coefficient, the positive coefficient of the interaction term suggests that it becomes more positive as Image increases; however, the size and precise nature of this effect is not easy to define from examination of the coefficients alone, and becomes even more so when one or more of the coefficients are negative, or the standard deviations of X (IV) and Z (Moderator) are very different (Dawson, 2013).

Excel template for Slope Analysis

Interaction plot for the categorical data moderator. Click here to download.

Interaction plot for the continuous data moderator. Click here to download.