Canadian Survey on Business Conditions (CSBC)

Detailed information for March 2020





Record number:


The purpose of this survey is to collect information on businesses in Canada related to emerging issues. This iteration of the survey focused on the impact of COVID-19 on businesses in Canada.

Data release - April 29, 2020


Information collected is intended to help governments, chambers of commerce and business associations across Canada devise strategies and mechanisms to foster the survival and continuity of businesses in Canada.


  • Business adaptation and adjustment
  • Business performance and ownership
  • Government
  • Labour
  • Revenue and expenditures
  • Wages, salaries and other earnings

Data sources and methodology

Target population

The target population for this survey was all businesses in Canada.

Instrument design

The collection instrument for this survey was an electronic questionnaire. The questionnaire was developed with the Canadian Chamber of Commerce and was the result of the collective input from several chambers of commerce and boards of trade.


Data were crowdsourced from businesses in Canada. All businesses in Canada were eligible to participate in the survey. Unlike other surveys conducted by Statistics Canada, crowdsourcing data are not collected under a sample design using a probability-based sampling.

Data sources

Data collection for this reference period: 2020-04-03 to 2020-04-24

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Data were collected using an electronic questionnaire.

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

Error detection

Error detection is an integral part of data processing activities. Prior to imputation, a series of edits are applied to the collected data to identify errors and inconsistencies. Errors and inconsistencies in the data are reviewed and resolved by referring to data for similar units in the survey and information from external sources. If a record cannot be resolved, it is flagged for imputation. Finally, edit rules are incorporated into the imputation system to detect and resolve any remaining errors, as well as to ensure that the imputed data are consistent.


After microdata verification, a variable was created for each of the survey variables to identify those that had either failed the verification rules or had missing values. Imputation was performed to reduce the amount of missing, inconsistent or incomplete data. The missing data were imputed using a randomly selected donor inside the imputation class. These imputation classes were formed of similar size units (employment), in the same geography and in the same industry.

A minimum number of units was required within each imputation class. When imputation classes were too small, larger classes were created by combining several classes together.

Imputation of survey variables was performed in an automated way using BANFF, a generalized system designed by Statistics Canada.


Since data were crowdsourced from businesses across Canada, the results were not weighted to the entire population. Estimation are based on a simple aggregation of the reported or imputed data by businesses that answered the survey. No adjustment was done to account for total non-response or to address the representativeness of the respondents.

Quality evaluation

Estimates were reviewed to ensure that the findings are logical and quality checks were carried out to ensure that estimates are consistent. Atypical results were flagged for investigation and were corrected as necessary.

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 statistical program.

Data accuracy

There are two types of errors which can impact the data: sampling errors and non-sampling errors. Since no sampling was done, there were no sampling errors. However, there is some bias associated with self-selection of the responding units.

Non-sampling errors may occur for various reasons during the collection and processing of the data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire and verification of the survey data.

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