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Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
[Construction of hierarchical models in applied contexts]
Guillermo Vallejo Seco1, Jaime Arnau Grass, Roser Bono Cabré
1Facultad de Psicología, Universidad de Oviedo, Oviedo, Spain. gvallejo@uniovi.es
Psicothema
|October 23, 2008
Summary
Hierarchical linear models (HLMs) offer unbiased estimates for nested data structures. This study demonstrates constructing 3- and 4-level HLMs for intervention program evaluation in primary schools.
Area of Science:
- Statistics
- Multilevel Modeling
Context:
- Hierarchical data structures are common in social and educational research.
- Analyzing nested data requires specialized statistical techniques.
Purpose:
- To illustrate the construction of hierarchical linear models (HLMs).
- To demonstrate HLMs in cross-sectional (3-level) and longitudinal (4-level) data contexts.
- To evaluate an intervention program's efficiency using HLMs.
Summary:
- The article details the process of building and applying HLMs.
- It showcases the analysis of a primary school math intervention program.
- Statistical modeling is performed using SAS and SPSS software.
Impact:
- Provides practical guidance on implementing HLMs.
- Offers a reproducible example for educational intervention research.
- Facilitates unbiased estimation of variance components in multilevel data.
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