General Social Survey - Giving, Volunteering and Participating (GSS GVP)
Detailed information for 2018 (Cycle 33)
Every 5 years
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 - Scheduled for...
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
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, Statistics Canada, 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 a void of information about 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 decisions that relate to the charitable and volunteer sector.
This record is part of the General Social Survey (GSS) program. The GSS originated in 1985. Each cycle 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: Past 12 months preceding interview date
Collection period: Every 5 years, from September to December
- Society and community
- Unpaid work
- Volunteering and donating
Data sources and methodology
The target population for the GSS Giving, volunteering and participating includes all persons 15 years of age and older living in the ten provinces of Canada. It excludes full-time (residing for more than six months) residents of institutions.
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.
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. After a respondent is classified as a volunteer or not, sub-sampling is carried out for selected respondents who are not volunteers. All respondents who are volunteers do a long interview. Those who are NOT volunteers are randomly divided into two groups. One group does a long interview, while the other group does a short interview.
GSS uses a two-stage sampling design. The sampling units are the groups of telephone numbers. The final stage units are individuals within the identified households. Note that GSS only selects one eligible person per household to be interviewed.
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 (Québec part of Ottawa - Gatineau CMA of is in Quebec-Other-CMAs). 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.
For the survey, a single eligible member of each sampled household is randomly selected.
A field sample of approximatively 50,000 units was used. Among them, about 40,000 invitation letters to the electronic questionnaire were sent to selected households across Canada. A completion of 24,000 questionnaires was expected.
Data collection for this reference period: 2018-09-04 to 2018-12-28
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 or through CATI (computer assisted telephone interviewing). No proxy reporting is allowed. The respondents has the choice between French and English. The average time to complete the survey is estimated at 44 minutes.
The information collected during the 2018 GSS has been 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) are key variables for the linkage.
Respondents were notified of the planned linkage before and during the survey. Any respondents who objected to the linkage of their data had their objections recorded, and no linkage to their tax data took place.
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, the incomes reported by respondents are usually rough estimates. Linking will allow getting such information without having to ask questions.
View the Questionnaire(s) and reporting guide(s) .
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 system.
The CATI 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 system 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 system as well as the more detailed edits discussed previously.
Except in a few cases, all imputations were made using donor records selected through a score function. Certain characteristics on each record with item or partial non response (also called a recipient record) were compared with the characteristics on all donor records. When a characteristic was the same on the donor record and the recipient record, the donor's score increased. The donor record with the highest score was deemed the "nearest" donor and was chosen to fill in the missing information of the non respondent. If more than one donor record had the highest score, one record was randomly selected. The pool of donor records was made up in such a way that the imputed value assigned to the recipient, in conjunction with other non imputed items from the recipient, would still pass the edits. Where donor imputation could not be used, mean imputation among a pool of donors was used.
Imputation was carried out in nine steps. The first step consisted of imputing personal income and family income. The next three steps involved imputing the formal volunteering variables in the master file. Steps five and six were imputing the informal volunteering variables in the master file. Finally, the last three steps involved imputing variables in the donation file and the solicitation methods in the master file.
In 2018, personal income questions were not asked in the survey. Income information was obtained by linking to the tax data of respondents who had not objected to the linkage. Personal income data were obtained from the 2017 T1FF for 81.9% of respondents. Missing information for other respondents was imputed. As in the 2017 GSS, family income (obtained through direct linkage with a variable from the T1FF that corresponds with census family income) was used for the 2018 GSS instead of household income. Overall, a value for family income was obtained for 81.7% of households. Missing information for the other respondents was imputed.
The GVP 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.
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. 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 GVP matched the 2017 CIS distribution by province
The 2018 GSS is a survey of individuals and the analytic files contain questionnaire responses and associated information from the respondents.
A weighting factor is 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.
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). To the extent that the characteristics are correlated with those independent estimates, this adjustment can improve the precision of estimates.
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.
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.
The methodology of this survey has been designed to limit the number of errors and to reduce their potential effects. However, the results of the survey remain subject to both sampling and non-sampling error.
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 2018 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.
The overall response rate is 41.9%.
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.
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 2018 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.
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 2018 GSS significant effort was made to minimize bias by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control.
- The General Social Survey: An Overview
Last review : January 7, 2021