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 language | English |
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Title of host publication | Recent Trends And Future Challenges In Learning From Data, Ecda 2022 |
Editors | C Davino, F Palumbo, AFX Wilhelm, HA Kestler |
Publisher | Springer Science+Business Media |
Pages | 1-10 |
Number of pages | 10 |
ISBN (Print) | 9783031544675 |
DOIs | |
Publication status | Published - 2024 |
Event | European Conference on Data Analysis, ECDA 2022 - Naples, Italy Duration: 14 Sept 2022 → 16 Sept 2022 |
Publication series
Series | Studies in Classification, Data Analysis, and Knowledge Organization |
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ISSN | 1431-8814 |
Conference
Conference | European Conference on Data Analysis, ECDA 2022 |
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Country/Territory | Italy |
City | Naples |
Period | 14/09/22 → 16/09/22 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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