Impacts of COVID-19 on Canadians: Data Collection Series

Detailed information for 2022





Record number:


The collection series collects data on the current economic and social situation, as well as on people's physical and mental health, to effectively assess the needs of communities and implement suitable support measures during and after the pandemic.

Data release - April 7, 2022 (First in a series of releases for this reference period.)


Starting April 3, 2020, Statistics Canada will conduct a collection series on the Impacts of the COVID-19 on Canadians.

The COVID-19 pandemic is currently disrupting the lives and habits of all Canadians. It is therefore necessary to quickly gather information to help understand its impacts on the physical and mental health of individuals, as well as on their social and employment circumstances.

This information will be used by government organizations, such as the Public Health Agency of Canada and Employment and Social Development Canada, and other types of organizations to evaluate the need for health and social services, as well as economic support during and after the pandemic.

Collection period: ad hoc


  • Disability
  • Economic accounts
  • Health
  • Income and expenditure accounts
  • Mental health and well-being

Data sources and methodology

Target population

All Canadians from the ten provinces and three territories are eligible to participate.

Instrument design

The collection series collects data on the current economic and social situation, as well as on people's physical and mental health, to effectively assess the needs of communities and implement suitable support measures during and after the pandemic.

The questionnaires follow standard practices and wording used in a computer-assisted interviewing environment, such as the automatic control of flows that depend upon answers to earlier questions and the use of edits to check for logical inconsistencies and capture errors. The computer application for data collection was tested extensively.


No sample selection is done for this crowdsourcing initiative.

Data sources

Participation in this crowdsourcing initiative is voluntary.

Data are collected directly from participants.

Data collection is conducted exclusively online by participant self-completion in a crowdsourcing application.

View the Questionnaire(s) and reporting guide(s) .

Error detection

An edit on age was built into the online questionnaire to maximize the quality of the data. Further error detection of invalid or missing values was performed at a micro level on the received data for age, gender, and postal code.


Participants who reported a gender of "Other" were randomly assigned a sex of either "Male" or "Female" for analytical purposes.

Valid values of the postal code (or its first three digits or first digit in the absence of a complete postal code) were used to derive a province of residence, and a Census Metropolitan Area (CMA) when possible.

No imputation was performed on questions left unanswered by participants.


Because of the non-probabilistic nature of the crowdsourcing data collection, a probability of selection was not available given the absence of a sample design and a survey weight was not calculated. Furthermore, no adjustment was made for non-response as the concept of a non-response rate is not applicable in the crowdsourcing context.

Demographic projections of the number of people by province/territory, sex, and age groups were used to calculate a benchmarking factor for every participant to compensate for over/underrepresentation. Those benchmarking factors should be used to produce results in the same way weights are used to produce estimates from a probabilistic survey. However, because of the non-probabilistic nature of crowdsourcing, results should be limited to proportions and totals should not be produced. Multiple biases may remain even with the application of the benchmarking factors.

Furthermore, benchmarking factors should not be used to calculate measures of precision (variance, coefficients of variation, margins of error, confidence intervals).

Results obtained from this crowdsourcing pertain only to the participants and should not be used to draw conclusions about the general population.

Quality evaluation

While an outlier detection process is used on the crowdsourced data, caution must be exercised when interpreting these data because data collection is conducted on a voluntary basis and therefore subject to multiple biases.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Revisions and seasonal adjustment

This methodology type does not apply to this survey.

Data accuracy

As crowdsourcing is based on a non-probability sample, measures of sampling error such as variance, coefficients of variation, margins of error, or confidence intervals should not be calculated.

Moreover, since crowdsourcing data are collected from self-selected volunteers, the data are subject to multiple biases that are not completely removed by benchmarking factors.

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