Resources on Canadian Voting and Policy Attitudes Project

Adding Variables from the Original Surveys

Mon, 01 Jan 0001 00:00:00 +0000

Download the code: adding-variables.R contains all of the R code from this tutorial.

Using an AI assistant? AGENTS.md contains instructions for AI coding assistants such as Claude Code and Codex. Save it in your project folder. Codex reads it automatically; for Claude Code, rename it CLAUDE.md, or ask Claude to read it. It tells the assistant how CVPA’s IDs work, and to check with you before making substantive choices, such as how to recode a question.

CVPA R Package: Getting Started

Mon, 01 Jan 0001 00:00:00 +0000

Download the code: cvpa-r-package.R contains all of the R code from this tutorial.

The cvpa R package is the easiest way to do data analysis in R using the Canadian Voting and Policy Attitudes (CVPA) dataset. The package downloads the necessary data for you, creates response categories and demographic variables as labelled factors, and computes annually-weighted estimates over time, either for the whole population or broken down by demographic group.

This tutorial provides a basic overview of how to install and load the package, identify a question and download its data, compute weighted responses by year, compute weighted responses by year, broken down by group, and plot the results.

Demographic Divides: Sample Teaching Assignment

Mon, 01 Jan 0001 00:00:00 +0000

This sample assignment draws on the Canadian Voting and Policy Attitudes (CVPA) project to help students investigate long-term demographic differences in Canadians’ federal vote intentions. Working in groups, students draw on assigned academic readings to develop theoretical explanations for differences in voting behaviour, then use CVPA figures to evaluate long-term patterns in Canadians’ vote intentions from 1945 to 2026. The exercise culminates in a written report and an in-class presentation.

Demographic Divides: Where to Start

Mon, 01 Jan 0001 00:00:00 +0000

The CVPA dataset has more than 600 policy questions, each of which can be broken down by gender, age, education, language, region, religion, union membership, and community size. If you’re interested in one of these groups and aren’t sure where to begin, the questions below offer some good starting points for exploration.

These questions work well alongside our interactive app, which lets you explore long-term changes in policy attitudes and voting without writing any code. In the app, you choose a policy domain first and then an issue within it. For each question below we give both, in the form Domain → Issue.

Raw Data: Download, Open, and Use

Mon, 01 Jan 0001 00:00:00 +0000

Download the code: raw-data.R contains all of the R code from this tutorial.

The full Canadian Voting and Policy Attitudes (CVPA) dataset is a single file, dataset.parquet, stored in our GitHub repository. This tutorial shows how to download that file, open it in R, understand how it is organized, and pull out a single question for analysis.

If you mainly want weighted estimates over time, the cvpa R package does all of this for you. The raw file is for when you want to work with the individual responses directly.

Rebuilding the Dataset from Scratch

Mon, 01 Jan 0001 00:00:00 +0000

You almost certainly don’t need this. The published dataset is exactly what the steps below produce, and the R package and the interactive app are built on it. If you want a question the dataset doesn’t include, see Adding Variables from the Original Surveys, which doesn’t require rebuilding anything.

This page is for anyone who wants to reproduce the dataset for themselves: to download the original survey files, run our code, and confirm that every value in CVPA comes from those files the way our documentation says it does.

Shifting Opinion: Where to Start

Mon, 01 Jan 0001 00:00:00 +0000

Canadian public opinion on some issues has barely moved in fifty years. On others it has shifted dramatically, sometimes steadily over decades and sometimes within a few years. The questions below are good places to look for change. Some changed steadily, some reversed direction, and some moved sharply around a particular event.

These questions work well alongside our interactive app, which lets you explore long-term changes in policy attitudes and voting without writing any code. In the app, you choose a policy domain first and then an issue within it. The headings below are the app’s domains, and each question is listed by its issue name in the app.