General Social Survey - Time Use (GSS)

Detailed information for 2015 - 2016 (Cycle 29)




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 - June 1, 2017


This survey monitors changes in time use to better understand how Canadians spend and manage their time and what contributes to their well-being and stress.

The data collected provides information to all level of governments when making funding decisions, developing priorities and identifying areas of concern for legislation, new policies and programs. Researchers and other users use this information to inform the general Canadian population about the changing nature of time use in Canada such as:
- Are we working too many hours and spending too much time commuting?
- Do we have flexible work schedules?
- Do we have enough time to play sports, participate in leisure activities or volunteer?
- Are we spending enough quality time with our children, our families and our friends?
- How has the internet and social media affected the way we spend our time?
- Are we satisfied with our lives?

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.


  • Commuting to work
  • Labour
  • Society and community
  • Time use
  • Unpaid work

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 key time use partners and data users. Qualitative testing on new content, conducted by Statistics Canada's Questionnaire Design Resource Center (QDRC), was carried out with respondents in four cities, representing three provinces. 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. Discussions on how changes would be implemented were taken in consultation with QDRC.


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.

Data sources

Data collection for this reference period: 2015-04-07 to 2016-04-06

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Data were collected using CATI and EQ.

The capture was done at the same time as collection.

First contact was made by telephone, but for cases where an address was associated with the telephone number, a contact letter was mailed prior to the start of collection.

Some cases were followed by e-mails and back to telephone and others were only followed up by telephone.

No proxy reporting was allowed.

The respondents had the choice between French and English, but is some regions where interviewers were fluent in a third language, they were allowed to complete the diary in this language as it uses a conversational style of interview.

The average time to complete the survey was 45 minutes.

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, the incomes reported by respondents are usually rough estimates. Linking allows getting such information without having to ask questions on income.

The information collected during the 2015 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 were notified of the planned linkage before or during the survey. Any respondents who objected to the linkage of their data had their objections recorded, and no linkage to their tax data has taken 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.


Values were imputed in the rare cases where the sex of the respondent was missing. The imputation was based on a detailed examination of the data and the consideration of any useful data such as the age and sex of other household members, and the interviewer's comments. In 2015, 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 2014 T1FF for 88.6% of the respondents. Missing information for all other respondents was imputed. A similar approach was used for household income information. Income information was obtained through a linkage to tax data for all other household members. In total, a household income value could be derived for 84.4% of households. Imputation was used if income information was missing for at least one member aged 15 years or older.


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.

GSS Cycle 29 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

Extensive validation and comparisons with distributions of variables collected in other surveys were done. In addition, a cross-survey validation process was undertaken after the final weights were applied to ensure the quality of the information.
For more information please refer to the 2015 Time Use Survey Technical Note catalog number 89-658-X

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 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 29. 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.
For more information please refer to the 2015 Time Use Survey Technical Note catalog number 89-658-X

The overall response rate is 38.2%.

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 2015 TUS, 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.
For more information please refer to the 2015 Time Use Survey Technical Note catalog number 89-658-X

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.

For the 2015 TUS significant effort was made to minimize bias by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control, and by following up with households that did not initially respond to the survey.


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