Survey on Quality of Employment (SQE)

Detailed information for 2020




One Time

Record number:


The purpose of this survey is to gain a better understanding of job quality in Canada, from the perspective of the worker, including both employees and the self-employed. Questions will be asked about employment status, work schedules, multiple jobs, job security, reasons for self-employment, compensation and benefits, and training.

Data release - March 22, 2021


The information collected in this survey will be used to fill important data gaps related to employment quality. The data will inform policy and provide a current snapshot across Canada.


  • Hours of work and work arrangements
  • Labour

Data sources and methodology

Target population

The target population for the survey is non-institutionalized persons 15 years of age or older who have worked in a job or business in the past two years, living in Canada's ten provinces, who are not living in a collective dwelling or on a reserve, and are not members of the Canadian military.

Instrument design

The content for the Survey on Quality of Employment electronic questionnaire was drafted in consultation with Employment and Social Development Canada.

The questionnaire underwent cognitive testing in the form of in-depth interviews in both of Canada's official languages, conducted by Statistics Canada's Questionnaire Design Resource Centre. The goal of the qualitative study was to test the survey content.


The Dwelling Universe File (DUF) was used to create the frame for SQE. Dwellings which were outside of the 10 provinces, as well as collective dwellings, military bases, institutions and dwellings located on reserves were excluded from the frame.

The SQE frame was stratified by province and a simple random sample of dwellings was selected independently within each province. An initial sample of 12,000 dwellings was selected and sent to collection.

The SQE sample has a two-stage design: the sampling unit for the first stage is the dwelling, and the sampling unit for the second stage is an eligible member of the household. Within each household, a person was selected using the age order selection method.

Data sources

Data collection for this reference period: 2020-02-21 to 2020-03-21

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Data are collected directly from survey respondents either through an electronic questionnaire (EQ) or through CATI (computer assisted telephone interviewing).

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

Error detection

Electronic files containing the daily transmissions of completed respondent survey records were combined to create the "raw" survey file. Before further processing, verification was performed to identify and eliminate potential duplicate records and to drop nonresponse and out-of-scope records.

In addition, some out-of-scope respondent records were found during the data clean-up stage. All respondent records that were determined to be out-of-scope and those records that contained no data were removed from the data file.

After the verification stage, editing was performed to identify errors and modify affected data at the individual variable level. The first editing step was to identify errors and determine which items from the survey output needed to be kept on the survey master file. Subsequent to this, invalid characters were deleted and the remaining data items were formatted appropriately.


This methodology type does not apply to this statistical program.


The estimation of population characteristics from a sample survey is based on the premise that each person in the sample represents a certain number of other persons in addition to themselves. This number is referred to as the survey weight. The process of computing survey weights for each survey respondent involves several steps.

1) Each selected dwelling is given an initial weight equal to the inverse of its selection probability from the sampling frame (DUF). Dwellings identified as out-of-scope during collection are dropped from the sample.

2) The weights for responding households are adjusted to represent the households that did not respond. Adjustment factors are calculated separately by province and dwelling type (single-detached house / other).

3) The household weights are calibrated so that the sum of the weights match province level household size demographic counts.

4) Person weights are computed by multiplying the household level weights by the inverse of the probability of selecting the person within the household. Respondents who were selected, but screened out due to not having worked within the reference period were kept until after the calibration step.

5) The person weights are calibrated so that the sum of the weights match demographic population counts at the province by age group by gender level. The weights are also calibrated to demographic counts for large Census Metropolitan Areas (CMAs).
6) Following calibration of the person weights to population totals, respondents who were screened out based on not working within the reference period were dropped from the final person level file.

Variance estimation is based on a resampling method called the bootstrap.

The Generalized Estimation System (G-Est) was used to generate the survey weights and bootstrap weights.

Quality evaluation

While quality assurance mechanisms are applied at all stages of the statistical process, the validation and review of data by statisticians is the final verification of quality prior to release. Validation measures implemented include verification of estimates through cross-tabulations and the confrontation of estimates with data from other Statistics Canada surveys such as the Labour Force Survey (LFS).

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

Since the SQE is a sample survey, all estimates are subject to both sampling and non-sampling errors.

Non-sampling error can be defined as errors arising during the course of virtually all survey activities, apart from sampling. These include coverage errors, nonresponse errors, response errors, interviewer errors, coding errors, and other types of processing errors.

Coverage errors arise when there are differences between the target population and the observed population. The observed population is persons living in dwellings with mailable addresses on the frame. Approximately 95% of the dwellings on the frame had mailable addresses. To the extent that the excluded population differs from the rest of the target population, the results may be biased.

The overall household response rate for the Survey on Quality of Employment is 40.0%. A number of persons selected from households were screened out of the survey. The approximated response rate for in-scope persons for SQE is 37.2%. Non-respondents often have different characteristics from respondents, which can result in bias. As a result of COVID-19 restrictions, there were limitations on interviewer resources during the SQE collection period. As a result, collection via telephone interviewing for SQE ended earlier than planned, the resulting response rate was lower than the 50.0% projected, and the possibility of nonresponse bias was increased. Attempts were made to reduce the potential nonresponse bias as much as possible through weighting adjustments.

Since there are no control totals for the target population of this survey (see the above section on estimation), the sum of weights represent an estimate of the target population. Because of this, producing estimates of totals should be avoided, as they might not be similar to totals computed from another survey with the same target population. Statistics such as proportions should be prioritized.

Sampling error is defined as the error that arises because an estimate is based on a sample rather than the entire population. Sampling error can be expressed through a confidence interval (CI) or coefficient of variation (CV).

The following are approximate sampling error estimates for Canada level estimates. These are based on average results; these are not results for a specific variable.

- Approximate length of 95% confidence intervals for a proportion of 50% (Canada level): 5.5%
- Approximate length of 95% confidence intervals for a proportion of 10% (Canada level): 3.3%

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