Are There Differences in FSA Scores Between Subgroups?

Authors: (DSCI 522 Group 304) Anny Chih, Robert Pimentel, & Wenjiao Zou

Using (BC Ministry of Education 2019a) here and (BC Ministry of Education 2017a) here from school years 2007/2008 through 2018/2019, we looked at whether there are differences in exam performance between different subgroups and school types to answer two main inferential questions:

1. Is there a difference in how well BC Public School vs Independent School students perform on the Numeracy and Reading sections of the FSA exam?
2. Is there a difference in how well Aboriginal vs Non Aboriginal students perform on the Numeracy and Reading sections of the FSA exam?

By conducting hypothesis testing using t-test statistics and a 95% confidence interval, we determined that:

1. Yes, there is a difference in how well BC Public School vs Independent School students perform on the Numeracy and Reading portions of the FSA exam; Independent School student scores are higher than Public School student scores.
2. Yes, there a difference in how well Aboriginal vs Non Aboriginal students perform on the Numeracy and Reading portions of the FSA exam; Non Aboriginal student scores are higher than Aboriginal student scores

The dataset used in this project includes two data files that contain FSA scores from BC students in Grades 4 and 7. Only data for the Numeracy and Reading portions of the FSA exam were analyzed (i.e. excludes Writing score analysis; see report for additional details).

Links to Preview Data Source:
- (BC Ministry of Education 2019b) link
- (BC Ministry of Education 2017b) link

Report

The final report can be found here

Usage

To replicate this analysis, you can choose one of the four options listed below.
For a visual map of how each file is connected, please see the Makefile Dependency Diagram below.

Option 1: Using Terminal

1. Clone/Download this GitHub repository
2. Install the dependencies listed below
3. Navigate to the root directory of this project in terminal
4. Run the following commands at the command line/terminal

# Loads Data
Rscript src/load_data.R 'https://catalogue.data.gov.bc.ca/dataset/5554165d-e365-422f-bf85-4f6e4c9167dc/resource/bcb547f0-8ba7-451f-9e11-10524f4d57a0/download/foundation-skills-assessment-2017-18_to_2018-19.csv' --arg2='data/fsa_2017-2018.csv'
Rscript src/load_data.R 'https://catalogue.data.gov.bc.ca/dataset/5554165d-e365-422f-bf85-4f6e4c9167dc/resource/97c6cbf7-f529-464a-b771-9719855b86f6/download/fsa.csv' --arg2='data/fsa_2007-2016.csv'

# Cleans Data
python src/clean_data.py --raw_data1='data/fsa_2007-2016.csv' --raw_data2='data/fsa_2017-2018.csv' --local_path='data/clean_data.csv'

# Creates Data Subset of only schools with both Aboriginal and Non Aboriginal students (based on data in 2018/2019 school year)
python src/filter_schools_both_subgroups.py --clean_data='data/clean_data.csv' --new_data='data/filtered_schools_both_subgroups.csv'

# Appends a column to the clean_data file with info about whether the school has both Aboriginal and Non Aboriginal students
python src/add_subgroup_info.py --clean_data="data/clean_data.csv" --new_data="data/new_clean_data.csv"

# Produces EDA Bar and Line Charts
Rscript src/data_viz_tab.R --data='data/clean_data.csv' --out_dir='img'

# Produces Histograms for Inferential Question 1: Difference Between Public and Independent School Scores
Rscript src/plot_publicindep_histogram.R 'data/clean_data.csv' --arg2='img/' --arg3='fig_pi_histogram_numeracy.png' --arg4='fig_pi_histogram_reading.png' --arg5='fig_pi_histogram_writing.png'

# Produces Histograms for Inferential Question 2: Difference Between Aboriginal and Non Aboriginal Scores
Rscript src/plot_subgroup_histogram.R 'data/clean_data.csv' --arg2='img/' --arg3='fig_ana_histogram_numeracy.png' --arg4='fig_ana_histogram_reading.png' --arg5='fig_ana_histogram_writing.png'

# Renders Report
Rscript -e "rmarkdown::render('doc/report.Rmd')"

Option 2: Using Makefile

1. Clone/Download this GitHub repository
2. Install the dependencies listed below
3. Navigate to the root directory of this project in terminal
4. Run make clean at the command line/terminal
5. Run make all at the command line/terminal

Option 3: Using Shell Script

1. Clone/Download this GitHub repository
2. Install the dependencies listed below
3. Navigate to the root directory of this project in terminal
4. Run bash runall.sh at the command line/terminal

Option 4: Using Docker

1. Install Docker
2. Clone/Download this GitHub repository
3. Navigate to the root directory of this project in terminal
4. Run docker run --rm -v /$(pwd):/home/rstudio/DSCI_522_Group304 annychih/dsci522_group304_docker make -C /home/rstudio/DSCI_522_Group304 clean at the command line/terminal
5. Run docker run --rm -v /$(pwd):/home/rstudio/DSCI_522_Group304 annychih/dsci522_group304_docker make -C /home/rstudio/DSCI_522_Group304 all at the command line/terminal

Makefile Dependency Diagram

Dependencies

• Python 3.6.9 and Python packages:

  • docoptpython >= 0.6.2
  • pandas >= 0.25.3

• R version 3.6.1 and R packages:

  • knitr >= 1.27.2
  • tidyverse >= 1.2.1
  • docopt >= 0.6.1
  • dplyr >= 0.8.3
  • readr >= 1.3.1
  • infer >= 0.5.0
  • repr >= 1.0.1
  • kableExtra >= 1.1.0
  • testthat >= 2.3.1
  • cowplot >= 1.0.0
  • broom >= 0.5.2

• GNU make 3.81

• Docker version 19.03.5

• pandoc version >= 2.9.1.1

References