Building Hierarchies of Factors with Disjoint Factor Analysis

Carlo Cavicchia*, Maurizio Vichi

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

Hierarchical and higher-order models are a useful way to assess underlying concepts that involve nested groups of observable variables. By assuming that there is a hierarchical relationship among these observable variables, a broader underlying concept can be represented as a tree-like structure, where each internal node represents a different level of abstraction for the concept being measured. In this chapter, we introduce a novel method for modeling these unknown hierarchical structures of observable variables, called higher-order disjoint factor analysis. This approach is both exploratory and nested and is estimated sequentially. Each subset of observable variables is modeled to be reliable and internally consistent, which means that variables related to a specific factor consistently measure a unique theoretical construct. The new method is employed to build hierarchies of factors for the Holzinger–Swineford 24-variable data set. A final discussion completes the chapter.

Original languageEnglish
Title of host publicationRecent Trends And Future Challenges In Learning From Data, Ecda 2022
EditorsC Davino, F Palumbo, AFX Wilhelm, HA Kestler
PublisherSpringer Science+Business Media
Pages1-10
Number of pages10
ISBN (Print)9783031544675
DOIs
Publication statusPublished - 2024
EventEuropean Conference on Data Analysis, ECDA 2022 - Naples, Italy
Duration: 14 Sept 202216 Sept 2022

Publication series

SeriesStudies in Classification, Data Analysis, and Knowledge Organization
ISSN1431-8814

Conference

ConferenceEuropean Conference on Data Analysis, ECDA 2022
Country/TerritoryItaly
CityNaples
Period14/09/2216/09/22

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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