This repository serves as a centralized location for replication files and supplementary materials associated with my research papers published in peer-reviewed journals. Replication files and materials are moved here from the "Research Projects" repository or directly uploaded once a research paper is published.
- Transparency:
- Ensure research reproducibility by providing access to data, scripts, and other relevant materials.
- Centralized Access:
- Consolidate all publication-related materials in a single repository for easy discovery and use.
- Facilitate Collaboration:
- Enable other researchers to build upon published work by offering replication resources.
Each folder in this repository corresponds to a single published article and contains the necessary files to replicate the research. Each folder contains:
Data: Includes all datasets used in the paper, such as survey data, qualitative data, experimental data, or other sources. Each dataset is accompanied by relevant documentation to facilitate reproducibility.Scripts: Contains code for data preparation, analysis (e.g., regression models, machine learning algorithms)Supplementary Materials: Additional files such as appendices, methodological notes, or extended results.
This repository includes following types of data across different publications:
- Survey Data: Nationally representative surveys collected across various countries and years.
- Qualitative Data: Historical records, interviews, and case study analyses.
- Country-Year Data: Macro-level datasets aggregated by country and year for regression analysis.
- Experimental Data: Results from survey and classical experiments.
The publications in this repository employ a diverse range of methods, including:
- Regression Models:
- Linear regression
- Logistic Regression
- Multinomial Regression with Bayesian statistics
- Ordered logistic regression
- Survival analysis
- Other generalized linear models (GLMs)
- Machine Learning Algorithms:
- Supervised models for classification and regression tasks
- Machine learning models tailored for survival analysis such as Random Survival Analysis
- Multilevel Modeling:
- Causal Inference Methods:
- Synthetic control methods
- Matching techniques
- Inverse Propensity Weighting
- Entropy Balancing
- Mixed-Methods:
- Combining qualitative and quantitative approaches.
- Navigate to the folder corresponding to the article of interest.
- Use the provided data, scripts, and supplementary materials to reproduce the findings.
- Results could be easily replicated just by importing the data and running relevant codes(after making sure that all libraries have been installed)
If you have any questions, feedback, or would like to report a potential issue, feel free to reach out:
Email: namigaabbasov@gmail.com