General Social Survey - Social Identity (SI)
Detailed information for 2013 (Cycle 27)
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 - December 23, 2014
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
The main objective of the GSS on Social Identity (SI) is to provide an overall picture of Canadians' identification, attachment, belonging and pride in their social and cultural environment. The key components of the survey include the following topics: Social networks, civic participation and engagement, knowledge of Canadian history, appreciation of national symbols, shared values, confidence in institutions and trust in people.
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.
- Social networks and civic participation
- Society and community
Data sources and methodology
The target population for the 2013 General Social Survey is all non-institutionalized persons 15 years of age or older, living in the ten provinces of Canada.
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 in four cities, representing three provinces, 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.
The pilot test of the data collection methodology and questionnaire was conducted from October to December 2012. Data collection observations of the pilot test were conducted by survey team members. All observation comments and suggestions were fully documented and, along with comments from interviewers, analyzed and implemented into the survey when possible.
This is a sample survey with a cross-sectional design.
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.
Data collection for this reference period: 2013-06-03 to 2014-03-31
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Data were collected using CATI and EQ. First contact was made by telephone. Some cases were followed by e-mails and back to telephone after. No proxy reporting was allowed. The respondents had the choice between French and English.
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 was 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.
Due to the nature of the survey, imputation was not appropriate for most items so missing data were coded as 'Not stated'. However, non-response was not permitted for those items required for weighting. Values were imputed in the rare cases where the sex of the respondent was missing for example.
When a probability sample is used, as was the case for the GSS, 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 2013 GSS SI was a survey of individuals and the analytic files contain questionnaire responses and associated information from 27,695 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.
Quality assurance measures were implemented at every collection and processing step. Measures such as recruitment of qualified interviewers, training provided to interviewers for specific survey concepts and procedures, observations of interviews to correct questionnaire design problems and instruction misinterpretations, procedures to ensure that data captures are minimized and edit quality checks to verify the processing logics. Data are verified to ensure internal consistency and they are also compared to previous survey results to ensure historical continuity. Estimates were compared to previous survey iterations to evaluate the coherence and overall reasonableness of trends.
Statistics Canada is prohibited by law from releasing any information it collects which 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.
For microdata: content is reduced and modified. For tabular data: sensitive cells correction methods such as cell collapsing and suppression are applied.
Revisions and seasonal adjustment
This methodology type does not apply to this statistical program.
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 the 2013 GSS SI. 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 5% level.
- The General Social Survey: An Overview
Last review : January 16, 2017.
- Date modified: