This repository contains the analysis workflow, organized outputs, data files, and report materials for a biostatistical consulting project based on the Optimal Learning Project, a four-year Alberta education initiative examining implementation of the Professional Practice Standards.

Project scope

The repository is organized around four analytical sections:

1. Descriptive and exploratory analyses
2. Group comparison analyses (MANOVA/ANOVA)
3. Structural equation modeling (SEM)
4. Longitudinal context and trend interpretation

These sections follow the staged analysis workflow described in the project documents in docs/.

Research questions covered

Section 1: Descriptive and exploratory questions

• Overall levels and distributions of teacher competency implementation across Teacher Quality Standard domains by year
• Year 4 comparison of implementation and professional learning need across competency domains
• Correlation structure among Year 4 teacher competency domains

Section 2: Group comparison questions

• Whether competency profiles differ by grade level taught in Year 4
• Whether competency profiles differ by subject specialization in Year 4
• Whether competency profiles differ by teaching experience in Year 4
• Whether implementation advancement differs jointly across the four survey years

Section 3: Structural relationship questions

• Relationship between perceived competency implementation and corresponding professional learning needs within each TQS domain
• SEM analyses using latent constructs and domain-specific indicators

Section 4: Longitudinal context questions

• Change in professional learning needs over time
• Whether Year 4 patterns are consistent with trends from earlier years

Repository structure

pps-alberta/
├── README.md
├── LICENSE
├── .gitignore
├── BIST601-biostat-consulting-project.Rproj
├── data/
│   ├── raw/
│   ├── processed/
│   └── metadata/
├── docs/
├── scripts/
├── outputs/
│   ├── section1/
│   ├── section2/
│   ├── section3/
│   └── section4/
└── report/

Contents

The contents of the repo is at follow:

• Original data files in data/raw/
• Original project instructions in docs/
• Original R scripts are structured for clearer execution order
• Original outputs in section-specific figures, tables, text_results, logs, summary_figures, and report_drafts
• The main report in report/pps_alberta_report.pdf

Script map

Section script	Repository script
section1.R	01_section1_descriptive.R
section2.R	02_section2_group_comparisons.R
section3.R	03_section3_sem.R
section4.R	04_section4_longitudinal.R

Outputs map

Each section follows an internal structure:

• figures/ for PNG figures
• tables/ for CSV result tables
• text_results/ for TXT statistical summaries and notes
• logs/ for session information and log-like artifacts
• summary_figures/ for section-level presentation-ready summary figures
• report_drafts/ for section-level draft report documents

Notes

• This repository package was organized to make it user-friendly and easier for reproducibity of the analysis.
• Temporary system files are .DS_Store, __MACOSX, .Rhistory, .RData, and .Rapp.history.

Source project documents

The client project context is included in:

• docs/project_questions.docx
• docs/scientific_background.docx

Main report

The final integrated report is located at:

• report/pps_alberta_report.pdf

Reproducibility

This repository is organized so that the analysis can be reproduced from the project root using relative paths. The workflow is divided into four main sections: descriptive exploration, group comparisons, structural equation modeling, and longitudinal interpretation.

Requirements

This project was developed in R and is intended to be run in RStudio using the included .Rproj file.

Install the required R packages before running the scripts.

install.packages(c(
  "tidyverse",
  "readxl",
  "psych",
  "lavaan",
  "semPlot",
  "ggplot2",
  "dplyr",
  "tidyr",
  "stringr",
  "purrr",
  "tibble"
))

Depending on your local setup and the exact script content, you may also need:

install.packages(c(
  "here",
  "janitor",
  "scales",
  "patchwork"
))

Project setup

1. Clone the repository:

git clone https://github.com/YOUR_USERNAME/pps-alberta.git
cd pps-alberta

2. Open the project in RStudio by clicking the .Rproj file.

3. Make sure the required data files are available in the expected folders under data/.

4. Run the scripts from the repository root so that all relative paths resolve correctly.

Recommended execution order

Run the scripts in the following order:

source("scripts/00_setup.R")
source("scripts/00_helpers.R")
source("scripts/01_section1_descriptive.R")
source("scripts/02_section2_group_comparisons.R")
source("scripts/03_section3_sem.R")
source("scripts/04_section4_longitudinal.R")

You may also run them individually from the terminal:

Rscript scripts/01_section1_descriptive.R
Rscript scripts/02_section2_group_comparisons.R
Rscript scripts/03_section3_sem.R
Rscript scripts/04_section4_longitudinal.R

Data availability

Some raw or processed data files may not be included because of project restrictions or confidentiality requirements. In that case:

• place the authorized raw files in data/raw/
• place any required cleaned files in data/processed/
• use the metadata files in data/metadata/ to match filenames, variables, and scoring structure

Expected outputs

Running the scripts should generate section-specific outputs under:

• outputs/section1/
• outputs/section2/
• outputs/section3/
• outputs/section4/

These outputs may include figures, summary tables, model results, and logs.

Notes

• All scripts should be run from the repository root.
• Relative paths are preferred throughout the project for portability.
• If a script fails because of a missing package, install the package and rerun the script.
• If a script fails because of a missing file, verify that the required input data are located in the correct data/ subfolder.