General Social Survey - Canadians' Safety (GSS)

Detailed information for 2019 (General social survey: Canadians' Safety)




Every 5 years

Record number:


The two primary objectives of the General Social Survey (GSS) are:
- to gather data on social trends in order to monitor changes in the living conditions and well-being of Canadians over time; and
- to provide information on specific social policy issues of current or emerging interest.

Data release - May 12, 2021


The main objective of the GSS on Canadians' Safety is to better understand how Canadians perceive crime and the justice system and to capture information on their experiences of victimization.

This survey is the only national survey of self-reported victimization and is collected in all provinces and territories. The survey allows for estimates of the numbers and characteristics of victims and criminal incidents. As not all crimes are reported to the police, the survey provides an important complement to officially recorded crime rates. It measures both crime incidents that come to the attention of the police and those that are unreported. It also helps to understand the reasons behind whether or not people report a crime to the police.

Survey results will be used by police departments, all levels of government, victim and social service agencies, community groups and researchers not only to better understand the nature and extent of victimization in Canada, but also to study Canadians' perceptions of their safety, the levels of crime in their neighbourhoods, and their attitudes toward the criminal justice system.

Statistical activity

This record is part of the General Social Survey (GSS) program. The GSS originated in 1985. Each survey contains a core topic, focus or exploratory questions and a standard set of socio-demographic questions used for classification. More recent cycles have also included some qualitative questions, which explore intentions and perceptions.

Reference period: Calendar year


  • Crime and justice
  • Society and community
  • Victims and victimization

Data sources and methodology

Target population

The target population for the GSS on Canadians' Safety is the Canadian population aged 15 and over, living in the provinces and territories. Canadians residing in institutions are not included.

Instrument design

The questionnaire was designed based on research and extensive consultations with data users. Qualitative testing, conducted by Statistics Canada's Questionnaire Design Resource Center (QDRC), was carried out, with respondents who were screened in based on representative criteria. Questions which worked well and others that needed clarification or redesign were highlighted. QDRC staff compiled a detailed report of the results along with their recommendations. All comments and feedback from qualitative testing were carefully considered and incorporated into the survey when possible.


This is a sample survey with a cross-sectional design.

This survey uses a frame that combines landline and cellular telephone numbers from the Census and various administrative sources with Statistics Canada's dwelling frame. Records on the frame are groups of one or several telephone numbers associated with the same address (or single telephone number in the case a link between a telephone number and an address could not be established). This sampling frame is used to obtain a better coverage of households with a telephone number.

The sample is based on a stratified design employing probability sampling. The stratification is done at the province/census metropolitan area (CMA) level. Information is collected from one randomly selected household member aged 15 or older, and proxy responses are not permitted.

Specific geography areas were targeted for an oversample of the Indigenous population.

The sample in the territories was drawn from an area frame of dwellings which had been or were still in the Labour Force Survey (LFS).

Sampling Unit:
GSS uses a two-stage sampling design. The sampling units in the provinces are the groups of telephone numbers. The sample units in the territories are the dwellings. The final stage units are individuals within the identified households. Note that GSS only selects one eligible person per household to be interviewed.

Stratification method:
In order to carry out sampling, the ten provinces of the target population are divided into strata (i.e. geographic areas). Many of the Census Metropolitan Areas (CMAs) are each considered separate strata. This was the case for St. John's, Halifax, Saint John, Montreal, Quebec City, Toronto, Ottawa (Ontario part of Ottawa - Gatineau CMA), Hamilton, Winnipeg, Regina, Saskatoon, Calgary, Edmonton, Vancouver and Victoria. CMAs not on this list are located in New Brunswick, Quebec, Ontario and British Columbia. For Quebec, Ontario and British Columbia, three more strata were formed by grouping the remaining CMAs in each of these three provinces. Next, the non-CMA areas of each of the ten provinces were grouped to form ten more strata. Moncton is included with the non-CMA group for New Brunswick. This resulted in 27 strata in all.

Sampling and sub-sampling:
For each province, minimum sample sizes were determined that would ensure certain estimates would have acceptable sampling variability at the stratum level. Once these stratum sample size targets had been met, the remaining sample was allocated to the strata in a way that balanced the need for precision of both national-level and stratum-level estimates. This sample was representative of all households in the ten provinces and the three territories.

For the survey, a single eligible member of each sampled household is randomly selected to complete the questionnaire.

A field sample of approximatively 60,500 units was used in the provinces. Among them, about 38,000 invitation letters were sent to selected households. A completion of 25,035 questionnaires was expected. A field sample of approximatively 3,700 units was used in the territories. Among them, about 3,200 invitation letters were sent to selected households. A completion of 2,080 questionnaires was expected.

Data sources

Data collection for this reference period: 2019-04-15 to 2020-03-31

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

For respondents in the provinces data are collected either through an electronic questionnaire or through CATI (computer assisted telephone interviewing). No proxy reporting is allowed. The respondents have the choice between French and English. Interviews are approximately 45 minutes.

In the territories, data are collected through an electronic questionnaire, telephone interviews or in-person interviews.

Tax derived files (CSDD environment):

By linking data, we are aiming to obtain better quality data for income (personal and household).

Questions relating to income show rather high non-response rates, or the incomes reported by respondents are usually rough estimates. Linking allows getting such information without having to ask questions.

The information collected during the 2019 GSS was linked to the personal tax records (T1, T1FF or T4) of respondents, and tax records of all household members. Household information (address, postal code, and telephone number), respondent's information (social insurance number, surname, name, date of birth/age, sex) and information on other members of the household (surname, name, age, sex and relationship to respondent) were key variables for the linkage.

Respondents are notified of the planned linkage before and during the survey. Any respondents who object to the linkage of their data have their objections recorded, and no linkage to their tax data takes place.

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

Error detection

Processing used the SSPE set of generalized processing steps and utilities to allow subject matter and survey support staff to specify and run the processing of the survey in a timely fashion with high quality outputs.

It used a structured environment to monitor the processing of data ensuring best practices and harmonized business processes were followed.

Edits were performed automatically and manually at various stages of processing at macro and micro levels. They included family, consistency and flow edits. Family relationships were checked to ensure the integrity of matrix data. A series of checks were done to ensure the consistency of survey data. An example was to check the respondent age against the respondent birth date. Flow edits were used to ensure respondents followed the correct path and fix off-path situations. Error detection was done through edits programmed into the CATI and CAPI systems.

The CATI and CAPI data capture program allowed a valid range of codes for each question and built-in edits, and automatically follows the flow of the questionnaire.

All survey records were subjected to computer edits throughout the course of the interview. The CATI and CAPI systems principally edited the flow of the questionnaire and identified out of range values. As a result, such problems were immediately resolved with the respondent. If the interviewer was unable to correctly resolve the detected errors, the interviewer bypassed the edit and forwarded the data to head office for resolution. All interviewer comments were reviewed and taken into account by head office editing.

Head office performed the same checks as the CATI and CAPI systems as well as the more detailed edits discussed previously.


In 2019, personal income questions were not asked to respondents who live in the provinces. Income information was obtained instead through a linkage to tax data for respondents who did not object to this linkage. Income information was obtained from the 2018 T1FF for 87.0% of the respondents. Missing information for all other respondents was imputed. Since GSS 2016, the family income (i.e., linking directly to a variable on the T1FF that corresponds to the census family income) is used instead of the household income. In total, a family income value was obtained for 86.9% of households for GSS 2019.


When a probability sample is used, as was the case for this survey, the principle behind estimation is that each person selected in the sample represents (in addition to himself/herself) several other persons not in the sample. For example, in a simple random sample of 2% of the population, each person in the sample represents 50 persons in the population (himself/herself and 49 others). The number of persons represented by a given respondent is usually known as the weight or weighting factor.

The 2019 GSS was a survey of individuals and contains two analytical files (main analytical file and incident analytical file). The microdata files from the main survey contain questionnaire responses and associated information from 22,412 respondents.

Two weighting factors are available on the microdata file:

WGHT_PER: This is the basic weighting factor for analysis at the person level, i.e. to calculate estimates of the number of persons (non-institutionalized and aged 15 or over) having one or several given characteristics.

WGHT_ABU: This weighting factor is required to estimate the number of victimization incidents that occurred over the past 12 months within certain violent relationships, namely those with spouse/ex-spouse or partner/ex-partner violence.

The second microdata file is the Incident Analytical File. The 6,359 records on this file contain reports of victimization incidents. Each victimization incident experienced by a respondent of the survey is included on one of the file's records, excluding spousal/ex-spousal and partner/ex-partner violent victimization incidents which are included in the main file.

Each record of the Incident Analytical File can be thought of as representing a number of victimization incidents experienced by persons in the overall population. This number is given by the weighting factor WGHT_VIC.

In addition to the estimation weights, bootstrap weights have been created for the purpose of design-based variance estimation.

Estimates based on the survey data are also adjusted (by weighting) so that they are representative of the target population with regard to certain characteristics (each month we have independent estimates for various age-sex groups by province or territory). To the extent that the characteristics are correlated with those independent estimates, this adjustment can improve the precision of estimates.

Quality evaluation

While rigorous quality assurance mechanisms are applied across all steps of the statistical process, validation and scrutiny of the data by statisticians are the ultimate quality checks prior to dissemination. Many validation measures were implemented. They include:

a. Analysis of changes over time;
b. Verification of estimates through cross-tabulations;
c. Confrontation with other similar sources of data.

Data in the GSS - Canadians' Safety were compared with data in its previous iteration (2014), the Survey of Safety in Public and Private Spaces (SSPPS), and the 2016 Census to identify changes in respondents' characteristics and whether such changes can affect the current survey's estimates.

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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

As the data are based on a sample of persons, they are subject to sampling error. That is, estimates based on a sample will vary from sample to sample, and typically they will be different from the results that would have been obtained from a complete census. More precise estimates of the sampling variability of estimates can be produced with the bootstrap method using bootstrap weights that have been created for this survey. The bootstrap method was used to estimate the sampling variability for all of the estimates produced based on the data from the 2019 GSS. Estimates with high sampling variability are indicated in this publication and all of the highlighted differences between subgroups of the population are significant at the 95% level.

Response rate:
The overall response rate for the provinces is 36.4% (41.0% for the main sample in the provinces and 22.2% for the oversample of the Indigenous population). The response rate for the territories is 57.0%.

Non-sampling error:
Common sources of these errors are imperfect coverage and non-response. Coverage errors (or imperfect coverage) arise when there are differences between the target population and the surveyed population. Households without telephones, as well as households with telephone services not covered by the current frame, represent a part of the target population that was excluded from the surveyed population. To the extent that the excluded population differs from the rest of the target population, the results may be biased. In general, since these exclusions are small, one would expect the biases introduced to be small. Non-response could occur at several stages in this survey. There were two stages of information collection: at the household level and at the individual level. Some non-response occurred at the household level, and some at the individual level. Survey estimates will be adjusted (i.e. weighted) to account for non-response cases. Other types of non-sampling errors can include response errors and processing errors.

Non-response bias:
The main method used to reduce nonresponse bias involved a series of adjustments to the survey weights to account for nonresponse as much as possible. For the 2019 GSS, an additional adjustment was added where basic characteristics of non-responding households, such as income and household composition, were extracted from administrative sources and then used to model and adjust nonresponse.

Coverage error:
The frame for GSS was created using several linked sources, such as the Census, administrative data and billing files. Coverage was improved (over coverage and under coverage may still exist) if we compare it to the random digit dialing strategies used in the past. All respondents in the ten provinces were interviewed by telephone or self-completed an electronic questionnaire. Households without telephones were therefore excluded from the survey population. Survey estimates were adjusted (weighted) to represent all persons in the target population, including those not covered by the survey frame.

Other non-sampling errors:
For the 2019 GSS significant effort was made to minimize bias by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control.


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