Pilot Study on Everyday Well-being (PSEW)
Detailed information for 2021
This pilot study asks Canadians in-the-moment questions about their activities and feelings. Our goal is to gain a better understanding of the factors that influence well-being, particularly arts and culture activities, which are the main focus of this pilot. This initiative is in collaboration with Canada Council for the Arts and Canadian Heritage. The data will provide insight on the connections between activities and well-being, and could be used to develop programs that enhance people's lives.
Data release - February 27, 2023
This initiative is in collaboration with Canada Council for the Arts and Canadian Heritage. Participants in the study are asked to download an app on their smartphones and answer a few questions about their well-being up to a maximum of five times throughout the day over a thirty day period. In addition, where the participant was, who they were with, and what they were doing is asked to situate these responses in context. The data will be used to fill key gaps in national-level subjective well-being. In the future, an app may be a faster and more convenient way to complete other Statistics Canada surveys.
Collection period: November 2021 to March 2022
- Culture and leisure
- Mental health and well-being
Data sources and methodology
Canadians living in the 10 provinces aged 15 years and older, with complementary generic crowdsourcing in addition to targeted crowdsourcing for those participating in arts and cultural events.
Two questionnaires were developed (one to collect preliminary information and one for the detailed experienced sample method diary) and tested via the Questionnaire Design Resource Center.
Testing was broken down into two phases. The first phase took place during the last week of January 2020 and focused on the questionnaire content, the flow and the look and feel of the app using a simulator and paper prototypes. The app was administered to test participants as a self-complete questionnaire using Statistics Canada mobile devices during Phase II, March 2020.
This is a sample survey with a cross-sectional design.
For the probabilistic component, a stratified two-stage sample of one person from each dwelling.
The sampling unit for the first stage is dwellings and the sampling unit for the second stage is people.
Provincial stratification for the probabilistic component.
Sampling and sub-sampling
For the probabilistic component, a field sample of 50,000 units was selected using a Kish allocation method.
Data collection for this reference period: 2021-11-08 to 2022-03-31
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Data were collected via a smartphone application. Initial contact was via a paper introduction letter. Follow-up included one reminder letter. Proxy reporting was not permitted. Respondents were able to respond in English or French.
The length of time to complete the first questionnaire was approximately 3 minutes with subsequent questionnaires taking 1 minute or less.
T1 Family File and T4 Information Slips
Information collected during the study will be linked to personal tax records (T1 Family File or T4 Information slips) of respondents. Household information and respondent's information are key variables for the linkage. Respondents were notified of the linkage during the survey.
View the Questionnaire(s) and reporting guide(s) .
All survey records were subjected to computer edits throughout the course of the interview. The EQ 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.
Personal income questions were not asked as part of the survey. Income information was obtained instead through a linkage to tax data. Missing information was imputed as needed.
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.
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.
ICOS, SAS, GLINK
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 does not apply.
Common sources of these errors are imperfect coverage and non-response. Non-response could occur at several stages in this survey. 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.
Persons without compatible cellular telephones will be unable to participate in the survey.
Coverage errors (or imperfect coverage) arise when there are differences between the target population and 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.