This study examines the relationships between education and health across respondents' sex. Using the 2016 General Social Survey, analyses find that the health-benefits of earning more than a secondary degree vary between male and female respondents. Below, information is included about how to use the files provided to reproduce the analyses leading to this conclusion.
Data come from the 2017 (cycle 31) Canadian General Social Survey. These data are made available through ODESI, a service provided by the Ontario Council of University Libraries. Access is restricted to those users who have a DLI License and can be used for statistical and research purposes. The terms of the license can be viewed here.
As part of McGill University, the CAND3 initiative has a license to use the data for the purposes of training. Those outside of McGill university should not use the data provided through CAND3's training activities for purposes not related to their CAND3 training. Trainees who belong to another DLI institution should re-download the data using the ODESI site using the login provided by their institution if they wish to make use of the data for other purposes.
CODEBOOK AND SUMMARY STATISTICS
The codebook and summary statistics for these data are publicly available here.
CITATIONS
Statistics Canada. 2020. General Social Survey, Cycle 31, 2017 [Canada]: Family (version 2020-09). Statistics Canada [producer and distributor], accessed September 10, 2021. ID: gss-12M0025-E-2017-c-31
| File Name | Purpose |
|---|---|
| 'Shell File.Rmd' | Calls Scripts to Conduct All Analyses |
| 'Cleaning Data.R' | Cleans and Codes Variables |
| 'Handling Missing Data.R' | Uses Listwise Deletion to Create Analytic Sample |
| 'Table 1.R' | Creates Descriptive Table |
| 'Table 2.R' | Creates Regression Table |
To reproduce the analyses conducted here, download the zip file of this project. The zip file will create the folder "CAnD3-Data-Activity." This folder will be your working directory. Once you obtain access to the dataset, rename it "gss_2017.csv" and save it in the working directory (CAnD3-Data-Activity). Open and run (knit) the shell file ('Shell File.Rmd'). This file calls on the individual script files to clean the data, manage missing data, and produce the descriptive and regression tables.
The following software programs are required to reproduce these analyses:
- R and Rstudio (version 1.4.1717) and the following packages as of 9/22/21
- tidyverse
- haven
- skimr
- naniar
- Hmisc
- sjlabelled
- gt
- gtsummary
These analyses were conducted using Mac OS Catalina (version 10.15.7):
- 2.9 GHz Dual-Core Intel Core i7 Processor
- 8 GB 1600 MHz DDR3 Memory