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Hierarchical Data

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May 1, 2024 3 minute read

**Hierarchical data** is a data structure that represents a hierarchy of elements. A hierarchy is an arrangement of elements in which each element has a superior element, except for the top-level element. For example, a company's organizational chart is a hierarchical data structure, with the CEO at the top, followed by vice presidents, directors, managers, and employees. Other examples of hierarchical data include file systems, XML documents, and relational databases with parent-child relationships.

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Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Hierarchical Data.
Written by two pioneers in the field of hierarchical linear modeling, this book provides a comprehensive introduction to the topic, covering both theory and applications.
Provides a thorough treatment of probability and statistical inference, which are essential foundations for understanding hierarchical models.
Provides a practical introduction to Bayesian hierarchical modeling using the R programming language.
While not specifically focused on hierarchical data, this classic work provides a comprehensive treatment of Bayesian data analysis, including hierarchical modeling.
Provides a thorough treatment of mixed effects models, a type of hierarchical model that is widely used in various fields.
While not specifically about hierarchical data, this book provides a strong mathematical foundation for understanding the theory behind hierarchical models.
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