General Social Survey : Canadians at Work and Home (GSS)

Detailed information for 2016 (Cycle 30)

Status:

Active

Frequency:

Every 5 years

Record number:

5221

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.

The mandate of the GSS "Canadians at Work and Home" is to explore people's views about work, home, leisure and well-being, and the relationships between these. Data from this survey will help decision makers select the programs and policies that will best serve Canadians.

Data release - November 14, 2017

Description

Canada's rapidly changing demographic profile, along with its accompanying social and economic issues, has led to much discussion concerning the relationship between work, lifestyle and well-being. Gauging the quality of life at work can help diagnose issues relating to productivity, morale, efficiency and equity. Charting patterns of home and leisure activities can take the temperature of Canadian culture. Bringing these two together will provide insight on the health and well-being of Canadians as they meet the challenges of the future.

The General Social Survey Program's new cycle,Canadians at Work and Home, takes a comprehensive look at the way Canadians live by incorporating the realms of work, home, leisure, and overall well-being into a single unit. Data users have expressed a strong interest in knowing more about the lifestyle behaviour of Canadians that impact their health and well-being both in the workplace and at home. The strength of this survey is its ability to take diverse information Canadians provide on various facets of life and combine them in ways not previously possible with surveys that covered one main topic only.

The survey includes a multitude of themes. In the work sphere, it explores important topics such as work ethic, work intensity and distribution, compensation and employment benefits, work satisfaction and meaning, intercultural workplace relations, and bullying and harassment. On the home front, questions include family activity time, the division of labour and work-life balance. The survey also covers eating habits and nutritional awareness, the use of technology, sports and outdoor activities, and involvement in cultural activities. New-to-GSS questions on purpose in life, opportunities, life aspirations, outlook and resilience complement previously asked ones on subjective well-being, stress management and other socioeconomic variables.

Within Canada, all levels of government, academics and not-for-profit organizations have expressed interest in the results. Data from this survey will assist with program and policy decisions and research of all kinds interested in exploring the workplace, home life and leisure activities of Canadians from all areas of life. In addition, some of the data from this survey will be comparable internationally.

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.

Subjects

  • Culture and leisure
  • Ethnic diversity and immigration
  • Information and communications technology
  • Labour
  • Society and community

Data sources and methodology

Target population

The target population for the survey is non-institutionalized persons 15 years of age or older, living in the 10 provinces. For the survey, a single eligible member of each sampled household is randomly selected by the application to complete the questionnaire, after the completion of the roster.

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 in four cities, 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 whenever possible.

Sampling

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.

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.

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 by the application to complete the questionnaire, after the completion of the roster.

A field sample of approximatively 43,000 units was used. Among them, about 35,000 invitation letters to the electronic questionnaire were sent to selected households across Canada. A completion of 20,000 questionnaires was expected.

Sampling Unit
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.

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 (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 by the application to complete the questionnaire, after the completion of the roster. A field sample of approximatively 43,000 units will be used. Among them, about 35,000 invitation letters to the electronic questionnaire are being sent to selected households across Canada. A completion of 20,000 questionnaires is expected.

Data sources

Data collection for this reference period: 2016-08-02 to 2016-12-23

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Tax derived files (CSDD environment).

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

The information collected from the 2016 GSS will be linked to the personal tax records (T1, T1FF or T4) of respondents. Household information (address, postal code, and telephone number) and respondent's information (social insurance number, surname, name, date of birth/age, sex) will be key variables for the linkage.

Linking to the tax files will ensure better quality data, lower respondent burden and decreased costs.

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

Imputation

In 2016, personal income questions were not asked as part of the survey. 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 2015 T1FF for 90.6% of the respondents. Missing information for all other respondents was imputed. Contrary to GSS 2015, the family income (i.e., linking directly to a variable on the T1FF that corresponds to the census family income) was used for GSS 2016 instead of the household income. In total, a family income value was obtained for 89.9% of households. Missing information for all other respondents was imputed.

Estimation

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.

GSS Cycle 30 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.

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

Disclosure control

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.

Revisions and seasonal adjustment

This methodology does not apply to this survey program.

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 GSS Cycle 30. 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 is 50.8%.

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 2016 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 dialling strategies used in the past. All respondents in the ten provinces were rostered by telephone and 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 2016 GSS significant effort was made to minimize bias by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control.

Documentation

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