General Social Survey Cycle 30: Canadians at Work and Home
hdl:11272/10612UNF:5:oGDPHDXK/wAUIljY05VLlA==
Version: 2 – Released: Wed Oct 10 09:57:10 PDT 2018
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TitleGeneral Social Survey Cycle 30: Canadians at Work and Home
Study Global IDhdl:11272/10612
Other IDStatistics Canada: 12M0030X
ProducerStatistics Canada (Statcan)
Production DateJune, 2018
Production PlaceOttawa
DistributorStatistics Canada (Statcan)
Distribution DateJune 07, 2018
Deposit DateJune 12, 2018
SeriesGSS, General Social Survey
Versionv1.0, June 07, 2018
Original Dataverse
Description and Scope
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.

Description DateJune 12, 2018
KeywordsCountry::Canada (ACV); Social Indicators (ACV)
Time Period CoveredAugust 02, 2016 - December 23, 2016
Date of CollectionAugust 02, 2016 - December 23, 2016
Country/NationCanada (CA)
Geographic CoverageProvinces of Canada
Geographic UnitProvince
Geographic BoundingWest Bounding Longitude: -141.018 East Bounding Longitude: -52.6194 North Bounding Latitude: 83.1355 South Bounding Latitude: 41.6814
Unit of AnalysisIndividuals
UniverseThe 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.
Kind of DataSurvey data
Data Collection / Methodology
Time MethodCross-sectional
Data CollectorStatistics Canada
FrequencyAnnual
Sampling Procedure

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.

Collection Mode

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.

Type of Research InstrumentStructured
Data SourcesTax derived files (CSDD environment).
Weighting

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.

Cleaning Operations

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

Study Level Error Notes

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.

Response RateThe overall response rate is 50.8%.
Estimates of 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 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.
Data Availability
Number of Files 16
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APPENDIX II

End-use Licence Agreement for Postal CodeOM Conversion File, Postal Codes OM by Federal Ridings File and Postal CodeOM Conversion File Plus (“data product”)

The Government of Canada (Statistics Canada) is the owner or a licensee of all intellectual property rights in this data product. With your payment of the requisite fee, you (hereinafter referred to as 'the Licensee') are granted a non-exclusive, non-assignable and non-transferable licence to use this data product subject to the terms below. This licence is not a sale of any or all of the rights of the owner(s). The data product includes information taken with permission from © Canada Post Corporation. All rights reserved. Information taken with permission from Canada Post Corporation does not form part of the Government of Canada open data portal.

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(i) addressing mail;

(ii) presorting addressed mail;

(iii) preparing unaddressed mail by householder count for delivery

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Appendix A – Approved Postal Code Data Matching Uses for DLI Accredited Canadian Post Secondary Institution

Matching Postal Code data to geography for:

  • Teaching and learning purposes.

    E.g.: Students can download PCCF on their laptop to do their assignments. This includes projects, maps, analytical papers, etc. Faculty can download and use the PCCF in teaching exercises.
  • Research purposes

    E.g.: Can be used in analysis to write articles that are published in journals. The data is not shared but the results are published. This also includes thesis for Masters or Doctorate where results are required to be public.
  • Planning purposes – where the institution can use the information in planning student recruitment activities or find out where these students are coming from.

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