Survey on Giving, Volunteering and Participating (SGVP)

Detailed information for 2023

The General Social Statistics Program (GSSP) has two main objectives: (a) to collect data on social trends in order to monitor changes in the living conditions and well-being of Canadians and (b) to provide updated information on particular social policy issues of current or emerging interest. As part of the GSSP, the 2023 Survey on Giving, Volunteering and Participating (SGVP) provides a comprehensive overview of the contributions Canadians have made by donating their time and money; it also provides data-driven information to the non-profit sector to help organizations strengthen their capacity for action, mobilize funds, recruit volunteers and manage their operations.

Data release - June 23, 2025

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy
• Documentation

Description

This survey is the result of a partnership of federal government departments and voluntary sector organizations that includes Canadian Heritage, Health Canada, Employment and Social Development Canada, the Public Health Agency of Canada, Canada Revenue Agency, Finance Canada, Statistics Canada, the University of Ottawa, Imagine Canada, and Volunteer Canada. This survey is an important source of information on Canadian contributory behavior, including giving, volunteering and participating.

The objectives of the survey are threefold:

1) to collect national data to fill an information gap on individual contributory behaviors including volunteering, charitable giving and civic participation;

2) to provide reliable and timely data to the System of National Accounts;

3) to inform both the public and voluntary sectors in policy and program areas of decisions that relate to the charitable and volunteer sector.

Statistical activity

This record originated in 1985 and is part of the General Social Statistics Program (GSSP). GSSP data are integrated from administrative data, statistical modelling and alternative data collection approaches, in addition to detailed survey data collected on a given topic. The GSSP is comprised of surveys on core topics, using focus or exploratory questions and a standard set of socio-demographic questions used for classification. More recent GSSP surveys have also included some qualitative questions, which explore respondent intentions and perceptions.

Reference period: Past 12 months preceding interview date

Subjects

• Labour
• Society and community
• Unpaid work
• Volunteering and donating

Data sources and methodology

Target population

The target population for the 2023 SGVP is all persons 15 years of age and older in Canada, excluding residents of the Yukon, Northwest Territories, and Nunavut, full-time residents of institutions, and residents of First Nations reserves.

Instrument design

The questionnaire was designed based on research and extensive consultations with data users. Qualitative testing was conducted by Statistics Canada's Questionnaire Design Resource Center (QDRC). In-depth individual interviews were conducted with respondents who were screened in based on representative criteria. The interviews allowed for the identification of questions which worked well and others that needed clarification or redesign. 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.

Sampling

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

The sample design is a stratified two-phase design based on the 2021 Census of Population. The first phase corresponds to the Census itself and the sample of households selected for the long-form questionnaire (about one household out of four, systematically selected across Canada). The second phase corresponds to the sample of persons selected for the survey; proxy responses were not permitted.

Due to the potential difficulties in reaching volunteers as a result of their prevalence in the population, an approach called 'rejective sampling' was chosen as part of the sample design. Rejective sampling works by 'rejecting' a certain portion of the population with a predetermined probability in order to allow more time and effort to be spent trying to find the population of interest, in this case, volunteers. After a respondent is classified as either a volunteer or non-volunteer, sub-sampling is carried out for selected respondents who are not volunteers. All respondents who are volunteers do a complete interview. Those who are NOT volunteers are randomly divided into two groups. One group does a complete interview, while the other group does a partial interview.

Sampling unit:
This is a targeted respondent survey. The sampling unit for the first phase (2021 Census of Population) is the household, while that of the second phase is the person.

Stratification method:
Strata were defined to achieve sufficient sample sizes in each domain of estimation and optimize sample allocation. The frame for the 2023 SGVP was stratified by province and population groups. An oversample of population groups was selected along with the main sample. The domains of estimation consist of the provinces in addition to the population groups of interest.

Sampling and sub-sampling:
Within each stratum, a sample was drawn using systematic sampling, after sorting the frame by collection unit, to reduce the possibility of sampling more than one person per household.

Through a new program called the Disaggregated Data Action Plan (DDAP), Statistics Canada is producing detailed data to address gender gaps, racism and other systemic barriers, to apply fairness and inclusion to decisions that affect all people in Canada.

With DDAP funding, the SGVP has increased its sample size and oversampled some population groups.

The total sample size for the SGVP is 80,000 individuals (60,000 regular sample, 20,000 oversample).

Data sources

Data collection for this reference period: 2023-09-15 to 2024-03-30

Responding to this survey is voluntary.

Data are collected directly from survey respondents either through an electronic questionnaire (EQ) or through CATI (computer assisted telephone interviewing). Respondents were interviewed in the official language of their choice. Proxy interviews were not permitted. The average time to complete the survey is estimated at 45 minutes.

In 2023, income questions were not asked in the survey. Income information was obtained by linking to the tax data of respondents who had agreed to the linkage to the 2022 T1 Family File (T1FF). Missing information for other respondents was imputed.

The information collected during the 2023 SGVP was also linked with the Longitudinal Immigration Database for completed cases. The linkage falls under the omnibus record linkage authority (Type B) as the linkage will be used for data replacement purposes.

Respondents were notified of the planned linkage during data collection. Any respondents who objected to the linkage of their data had their objections recorded, and no linkage to their administrative data took place.

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

Error detection

The 2023 SGVP used the Social Survey Processing Environment (SSPE), a 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. The SSPE is a structured environment that monitors the processing of data, ensuring best practices and harmonized business processes are followed.

Edits were performed automatically and manually at various stages of processing at macro and micro levels. Data verification was carried out using consistency and flow edits. A series of checks were done to ensure the consistency of the survey data, for example, checking the respondent's reported age against the date of birth coming from the sample file. Flow edits were used to ensure respondents followed the correct path and fix off-path situations.

Most error detection was done through pre-determined edits programmed into the EQ system, which allows for a valid range of codes for each question and built-in edits, and automatically follows the flow of the questionnaire.

Head office performed the same checks as the EQ system as well as more specific validation of edits that are beyond the scope of automated flow and consistency edits. Records with missing or incorrect information were, in a small number of cases, completed, corrected deterministically, or imputed from other information on the questionnaire.

Imputation

Except in a few cases, all imputations were made using donor imputation. This method uses donor records selected through a score function to impute missing values. Recipient records (records with item or partial non-response) were matched with donor records based on shared characteristics. The donor with the highest score filled in the missing information. If multiple donors had the highest score, one was randomly selected. Mean imputation was used when donor imputation could not be used.

Imputation was carried out in 4 blocks:

1) imputation of personal income and family income;
2) imputation of variables related to donations;
3) imputation of the formal volunteering variables; and
4) imputation of the informal volunteering variables.

The SGVP imputation process worked well and helped to fill incomplete responses with the experience of other respondents with similar or identical characteristics. This adds to the number of units used in any analysis performed by researchers.

Estimation

When a probability sample is used, as is the case for the 2023 SGVP, the principle behind estimation is that each person selected in the sample represents (in addition to himself or herself) several other persons not in the sample. For example, in a simple random sample of 2% of a population size of 1000, each person in the sample represents 50 persons in the population. The number of persons represented by a given person in the sample is usually known as the weight or weighting factor. Furthermore, in order to adjust for the 'rejecting' of a proportion of respondents that are not volunteers, the person weight for respondents that are not 'rejected' and are not volunteers is multiplied by a factor. Finally, the weights were adjusted so that the weighted income distribution of 2023 SGVP matched the 2022 Canadian Income Survey (CIS) distribution by province.

A weighting factor is made available to analysts 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 and over) having one or several given characteristics.

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. 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:

1) analysis of changes over time;
2) verification of estimates through cross-tabulations; and
3) confrontation with other similar sources of data.

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 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 the estimates produced based on the data from the 2023 SGVP.

Response rate:

The overall response rate was 40.9% (42.2% for the regular sample and 37.1% for the oversample).

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. Persons without good contact information 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. 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 non-response bias involved a series of adjustments to the survey weights to account for nonresponse as much as possible. Information was extracted from the frame and used to model and adjust for non-response.

Coverage error:

The SGVP 2023 frame was based on the 2021 long-form Census of Population to ensure adequate coverage of groups of interest, such as population groups. The additional socio-demographic questions from the long-form content made it possible to target individuals based on their population group. Coverage was improved (over coverage and under coverage may still exist) if we compare using several linked sources. All respondents in the ten provinces were interviewed by telephone or self-completed an electronic questionnaire. 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 2023 SGVP, significant effort was made to minimize bias by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control.

Documentation

• The General Social Survey: An Overview
  Last review : January 7, 2021
  • Format: The General Social Survey: An Overview - HTML[HTML]