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# Cohort simulations
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This repository contains all code files related to our ROSALI/Resiuals Cohort study simulation project. In order to save disk space, data files are not stored on this server and are instead available on https://osf.io.
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## File Structure
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```
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📦 simul_these
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├─ catalogue.md - List and description of scenarios
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├─ 🗂️ Analysis - ANALYSIS RESULTS
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├─ 🗂️ Data - GENERATED DATASETS
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│ ├─ 🗂️ DIF - DATASETS WITH DIF
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│ └─ 🗂️ noDIF - DATASETS WITHOUT DIF
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├─ 🗂️ Modules - R AND STATA MODULES
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│ ├─ 🗂️ rosali_custom - DATASETS WITH DIF
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├─ 🗂️ RProject - R SCRIPTS FOR VARIOUS TASKS
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└─ 🗂️ Scripts - R AND STATA SCRIPTS
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├─ 🗂️ Analysis - PCM ANALYSIS SCRIPTS
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└─ 🗂️ Data_generation - SIMULATION SCENARIO SCRIPTS
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├─ 🗂️ DIF
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└─ 🗂️ noDIF
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```
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## Naming conventions
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### Initial Datasets
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**XXX_N** - Scenario XXX / N individuals per group
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### Analyzed Datasets
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**noDIF_noConf / XXX_N.csv** - Analysis for scenario XXX_N by PCM __without__ accounting for DIF nor confusion
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**DIF_noConf / XXX_N.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for but not confusion
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**ROSALI-DIF_noConf / XXX_N_original.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for after detection by ROSALI but not accounting for confusion
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**RESIDUALS_noConf / XXX_N_original.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for after detection by Andrich & Hagquist's residuals method but not accounting for confusion
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**noDIF / XXX_N.csv** - Analysis for scenario XXX_N by PCM __without__ accounting for DIF and confusion accounted for
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**DIF / XXX_N.xls** - Analysis for scenario XXX_N by PCM __with__ DIF and confusion accounted for
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**noDIF_prop / XXX_N.csv** - Analysis for scenario XXX_N by PCM __without__ accounting for DIF and confusion accounted for by propensity score
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**DIF_prop / XXX_N.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for and confusion accounted for by propensity score
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**ROSALI-DIF_prop / XXX_N_original.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for after detection by ROSALI and confusion accounted for by propensity score
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**RESIDUALS_prop / XXX_N_original.xls** - Analysis for scenario XXX_N by PCM __with__ DIF accounted for after detection by Andrich & Hagquist's residuals method and confusion accounted for by propensity score
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## Reproduction - TO BE MODIFIED
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1. Run **/Scripts/Data_generation/NoDIF/scenarios_noDIF_baseline.do** to simulate no DIF data
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2. Run files in 🗂️ **/Scripts/Data_generation/DIF/** to simulate DIF data
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3. Run **/RProject/Scripts/Analysis/pcm_nodif.R** to analyze without accounting for DIF
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4. Run files in 🗂️ **/Scripts/Analysis/DIF/** to analyze while accounting for DIF
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5. Run **/Scripts/Analysis/DIF-ROSALI/pcm_dif_rosali.do** to analyze data after accounting for DIF as detected by ROSALI
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6. Run **/RProject/Scripts/Analysis/resali_analysis.R** to perform residuals DIF detection and prepare data for PCM analysis.
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7. Run **/Scripts/Analysis/DIF-RESIDUALS/pcm_dif_residus.do** to analyze data after accounting for DIF as detected by the residuals method
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8. Run **/RProject/Scripts/Analysis/aggregation.R** to compile and visualize results
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**OR**
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1. [BASH ONLY] Run **prepare_file_structure.sh** (only on first run) and **autorun.sh** (by default, will take multiple weeks to run. Please modify to run in parrallel if necessary)
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