Full script of paper: Zhang, C., & Pampaka, M. (upcoming). Applying Bayesian Item Response Theory for Small-Scale Datasets: An Example Workflow for Measuring Multidimensional Mathematics Teachers' Belief about Equity.

📖 Rendered tutorial (start here)

The full tutorial, with all brms code, output, and a navigable table of contents, is published via GitHub Pages:

👉 https://lukezed.github.io/BIRT_tutorial_belief/

The analysis is designed to be modular:

• Core brms modeling operations are documented in analysis.Rmd.
• Data cleaning, processing, and plotting are located in the scripts/ folder.

Please follow the numbered scripts in the scripts/ folder for reproduction if needed.

Project Structure

.
├── BIRT_tutorial_belief.Rproj
├── analysis.Rmd
├── data
│   ├── processed
│   │   ├── data_1.rds
│   │   ├── data_2.rds
│   │   └── data_3.rds
│   └── raw
│       └── belief_clean.csv
├── figures
│   ├── fig3_response_distribution.png
│   ├── fig4_wright_map.png
│   ├── fig5_model_comparison.png
│   ├── fig6_final_diagnostics.png
│   └── fig7_combined_dif.png
├── models
│   ├── bf_gpcm_new.rds
│   ├── bf_grm_dif.rds
│   ├── bf_grm_dif2.rds
│   ├── bf_grm_new.rds
│   ├── bf_grsm.rds
│   ├── bf_grsm_new.rds
│   ├── bf_rsm.rds
│   ├── ct_gpcm_new.rds
│   ├── ct_grm_new.rds
│   ├── ct_grsm.rds
│   ├── ct_grsm_new.rds
│   ├── ct_rsm.rds
│   ├── merge_rsm.rds
│   └── null_rsm.rds
├── paper
└── scripts
    ├── 00_packages.R
    ├── 01_data_cleaning.R
    ├── 02_category_collapse.R
    ├── 03_fig3.R
    ├── 04_fig4.R
    ├── 05_fig5.R
    ├── 06_data_refinement.R
    ├── 07_fig6.R
    ├── 08_fig7.R
    ├── 09_reliability.R
    └── 10_itemfit.R