master-thesis
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- Host: GitHub
- Owner: AngelGarciaODiana
- License: cc0-1.0
- Language: R
- Default Branch: main
- Size: 181 MB
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README.md
Master-Thesis 
The repository have all supplementary materials for the Master's thesis submitted to the Universidad Csar Vallejo, as part of the University Teaching Master Program. Please feel free to use any of these files as part of your research project, only give me the correspondent attributions in your citations.
Dissertation slides can be found on quarto repository:
If you use this slides in your work, please cite it using the following metadata:
Anlisis Comparativo de Tcnicas de Estimacin en el Anlisis de Redes Aplicado a la Investigacin en Docencia Universitaria
Comparative Analysis of Estimation Techniques in Network Analysis Applied to Educative Research
El objetivo del estudio fue examinar el desempeo de cuatro mtodos de estimacin de redes (EBICglasso, huge, TMFG, LoGo) y tres algoritmos de deteccin de comunidades (walktrap, leiden, spinglass) en bases de datos de investigacin educativa. Emple un diseo experimental basado en datos con simulaciones Monte Carlo. A partir de dos conjuntos de datos provenientes de escalas ordinales tipo Likert con caractersticas compartidas, se generaron 988,800 casos a travs de diversos tamaos de muestra. Finalmente, fueron un total de 192 modelos estimados a travs cuatro mtodos de estimacin de redes bajo dos estrategias de conversin de datos (normal y no paranormal). Se realiz un anlisis de clsteres para cada modelo, lo que dio lugar a 576 redes nicas. El anlisis final, replicado para dos variables, result en 1,152 estimaciones. Las conclusiones indican que una cuidadosa seleccin de configuraciones mejora la replicabilidad y consistencia, adems que las configuraciones iniciales de estimacin son cruciales para obtener resultados precisos. El diseo de modelos dinmicos y complejos a travs del anlisis de redes psicomtricas puede conducir a nuevos paradigmas pedaggicos y andraggicos, lo que permitir optimizar los mecanismos educativos y comprender el desarrollo integral de los estudiantes.
The aim of the study was to examine the performance of four network estimation methods (EBICglasso, huge, TMFG, LoGo) and three community detection algorithms (walktrap, leiden, spinglass) in educational research databases. It employed a data-driven experimental design with Monte Carlo simulations. From two datasets derived from Likert-type ordinal scales with shared characteristics, 988,800 cases were generated across various sample sizes. Ultimately, a total of 192 models were estimated using four network estimation methods under two data conversion strategies (normal and non-paranormal). A cluster analysis was performed for each model, resulting in 576 unique networks. The final analysis, replicated for two variables, yielded 1,152 estimates. The findings indicate that a careful selection of configurations improves replicability and consistency, and that the initial estimation settings are crucial for obtaining accurate results. The design of dynamic and complex models through psychometric network analysis can lead to new pedagogical and andragogical paradigms, enabling the optimization of educational mechanisms and a better understanding of students' holistic development.
METADATA: Network Analysis, Cluster Analysis, Simulation, System Comparison, Student Engagement, Mathematical Anxiety, Spinglass, Walktrap, Leiden, Louvain, EBICglasso, TMFG, LoGo, huge, Adjusted Rand Index, Network Comparison Test, Monte Carlo Method.
Owner
- Name: Angel A. García O'Diana
- Login: AngelGarciaODiana
- Kind: user
- Location: Perú
- Company: @PsiNet-LAB
- Website: https://allmylinks.com/agarciao10
- Twitter: 035Angel
- Repositories: 2
- Profile: https://github.com/AngelGarciaODiana
CEO @ PsiNet LAB Researcher @ Universidad César Vallejo