Class Description Generator
Context
To manage groups of nodes, BayesiaLab offers Classes.
Nodes can be added to Classes manually or automatically. For instance, the Variable Clustering function can assign nodes to new Classes representing latent factors. By default, newly-created Classes have generic names, such as [Factor_0], which carries no meaning.
Finding suitable descriptions for Classes can be time-consuming.
The Class Description function can assist you in finding meaningful summaries of a Class of nodes.
Using Class Description Generator for Groups of Nodes
With the Hellixia Class Description Generator, we can quickly find a useful description for a subset of nodes we select.
In our example, we have a large number of nodes from an auto buyer satisfaction survey.
We are interested in a subset of nodes related to the quality perception of the vehicle interior, i.e.:
Interior Colors
Quality of Interior Materials
Interior Trim & Finish
Quality of Seat Materials
Select these nodes node of interest.
Then select
Main Menu > Hellixia > Class Description
.Specify a Context, if applicable.
Indicate by ticking the checkboxes where the subject matter is stored, i.e., Node Name, Node Long Name, or Node Comment. Check all that apply.
Clicking OK starts generating the Class Description.
The chime confirms when the process is complete.
Opening the Class Editor shows the Class Description that was generated.
Select
Graph Contextual Menu > Edit Classes
The Description column shows the newly-generated Class Description.
Workflow Illustration
Using the Class Description Generator from within the Class Editor
BayesiaLab's Clustering function produces new Factors and associated Classes.
So, having a dozen or more new Classes is quite common in this context.
By default, the newly-generated Classes have generic and non-informative names, like [Factor_0], [Factor_1], etc.
Given that the Factors and Classes are meant to represent meaningful concepts, naming them is important but can be tedious.
In the following example, 57 Factors (and Classes) were created from 240 manifest nodes. Each manifest node measures the degree of agreement or disagreement with statements in a personality test, such as, "I get angry easily" or "I remain calm under pressure."
These original statements are included as Node Comments with every node.
Workflow Illustration
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