The tutorial discusses in detail how to perform moderation analysis in SmartPLS4 using Latent unobserved constructs and Categorical moderator. Further, a video session on how to report moderation analysis results is also shared.
The tutorial is a step by step guide on how to perform Moderation Analysis using SmartPLS4.
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Moderation describes a situation in which the relationship between two constructs is not constant but depends on the values of a third variable, referred to as a moderator variable. The moderator variable (or construct) changes the strength, or even the direction of a relationship between two constructs in a model. For example, prior research has shown that the relationship between customer satisfaction and customer loyalty differs as a function of the customers’ income or age. More precisely, income has a pronounced negative effect on the satisfaction to loyalty relationship – the higher the income, the weaker the relationship between satisfaction and loyalty. This means the satisfaction to loyalty relationship is not the same for all customers but differs depending on the income level.
Moderating relationships are hypothesized a priori by the researcher. The testing of the moderating relationship depends on whether the researcher hypothesizes whether one specific model relationship or whether all model relationships depend on the values of the moderator. Moderators can be either single items or multi-item constructs.
Moderators can be present in structural models in different forms. They can represent observable traits, such as gender, age, or income. But they can also represent unobservable traits, such as risk attitude, attitude toward a brand, or ad liking. Moderators can be measured with a single item or multiple items and using reflective or formative indicators. The most important differentiation, however, relates to the moderator’s measurement scale, which involves distinguishing between categorical (typically dichotomous) and continuous moderators.
In the following two examples, the Independent, Moderating, and Dependent variables are latent construct measured using multiple items on a metric scale.The first example is simple with 1 moderator, the second example is slightly complex with multiple moderators.
Later in the tutorial, categorical moderating variables are also discussed.
Alternatively, we could also hypothesize that several relationships using categorical variables. If the categorical moderator is affecting all the relationships in the study, Run Multi-group analysis. However, if the categorical moderator is not affecting all the proposed relationships, use moderation with categorical moderator. More specifically, in this session we address the modeling and interpretation of an interaction effect that occurs when a moderator variable is assumed to influence specific relationships.
The session guides step by step on how to report moderation analysis results from SmartPLS4. Reporting data analysis results can be a significant issue for research scholars. The series of sessions on the channel apart from this video also guide research scholars on how to report data analysis and results section based on the output from SmartPLS4.