Survey on Before and After School Care in Canada (SBASCC)
Detailed information for 2022
The purpose of this survey is to address child care in Canada for children who are attending school (i.e. ages 4 to 12). The survey will ask about the different types of learning and child care arrangements used by families, difficulties some families may face when looking for care, as well as reasons for not using child care.
Data release - October 14, 2022
Statistics Canada is gathering information from families who use before and after school care as well as those who do not. The survey, which addresses before and after school care in Canada for children who are attending school (i.e. ages 4 to 12 years old), asks about the different types of before and after school care arrangements that families use, difficulties some families may face when looking for care, as well as reasons for not using before and after school care. The survey will also cover the COVID-19 pandemic and its impact on before and after school care.
Data sources and methodology
The target population is children across the 10 provinces of Canada aged 4 to 12 who are attending school. Children living on reserves are excluded from the target population.
The content for the Survey on Before and After School Care in Canada electronic questionnaire was drafted in consultation with Employment and Social Development Canada (ESDC) and external subject matter experts.
The questionnaire underwent cognitive testing in the form of in-depth interviews in both of Canada's official languages, conducted by Statistics Canada's Questionnaire Design Resource Centre. The goal of the qualitative study was to test the survey content.
This is a sample survey with a cross-sectional design.
This is a targeted respondent survey. The sampling unit is the person knowledgeable about the before and after school child care arrangements for a child who lives in their household and is aged 4 to 12 years.
The sample frame for the Survey on Before and After School Care in Canada was stratified by province and a systematic sample was selected independently within each province.
Sampling and sub-sampling:
Sufficient sample was allocated to each of the provinces so that the survey could produce provincial level estimates. An initial sample of 15,000 targeted respondents was selected and sent to collection. The selection of a sample unit is done in one stage. First a list of children aged 4 to 12 years whose parent or guardian has applied for the Canada Child Benefit (CCB) is sorted by province, city, mailing address, and birth date. Next, a systematic sample of children is drawn within each province, and the CCB applicant corresponding to each selected child is added to the sample file.
Data collection for this reference period: 2022-05-30 to 2022-07-15
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Data are collected directly from survey respondents either through an electronic questionnaire (EQ) or through CATI (computer assisted telephone interviewing).
View the Questionnaire(s) and reporting guide(s) .
Electronic files containing the daily transmissions of completed respondent survey records were combined to create the "raw" survey file. Before further processing, verification was performed to identify and eliminate potential duplicate records and to drop non-response and out-of-scope records.
In addition, some out-of-scope respondent records were found during the data clean-up stage. All respondent records that were determined to be out-of-scope and those records that contained no data were removed from the data file.
After the verification stage, editing was performed to identify errors and modify affected data at the individual variable level. The first editing step was to identify errors and determine which items from the survey output needed to be kept on the survey master file. Subsequently, invalid characters were deleted, and the remaining data items were formatted appropriately.
No imputation was done.
The estimation of population characteristics from a sample survey is based on the premise that each person in the sample represents a certain number of other persons in addition to themselves. This number is referred to as the survey weight. The process of computing survey weights for each survey respondent involves several steps.
1) Each selected respondent is given an initial weight equal to the inverse of its selection probability from the sampling frame (CCB). Respondents identified as out-of-scope during collection are dropped from the sample.
2) The respondents' weights are then adjusted to account for non-response based on the province, the age of the respondent's child, and the respondent's CCB eligibility.
3) The person (child) weights are calibrated so that the sum of weights match demographic population counts at the province by age group level.
Variance estimation is based on a re-sampling method called bootstrap estimation.
The Generalized Estimation System from Statistics Canada(G-Est) was used to generate the survey weights and bootstrap weights.
While rigorous quality assurance mechanisms are applied at all stages of the statistical process, the validation and detailed review of data by statisticians is the ultimate verification of quality prior to release. Many validation measures were implemented, they include:
a. Verification of estimates through cross-tabulations
b. Consultation with stakeholders internal to Statistics Canada
c. Consultation with external stakeholders.
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 type does not apply to this statistical program.
Survey errors come from a variety of different sources. One dimension of survey error is sampling error. Sampling error is defined as the error that arises because an estimate is based on a sample rather than the entire population. Sampling error can be expressed through a confidence interval (CI) or coefficient of variation (CV).
The following are approximate sampling error estimates for Canada level estimates. These are based on average results; these are not results for a specific variable.
-Approximate length of 95% confidence intervals for a proportion of 50% (Canada level): 3.1%
-Approximate length of 95% confidence intervals for a proportion of 10% (Canada level): 2.0%
-Approximate coefficients of variation a proportion of 10% (Canada level): 5.1%
The response rate for this survey was 42.9%
Non sampling error
The first type of errors treated were errors in questionnaire flow. For skips based on answered questions, all skipped questions were set to "Valid skip" (6, 96, 996, etc.). For skips based on "Non-response", all skipped questions were set to "Not stated" (9, 99, 999, etc.). The remaining empty items were filled with a numeric value (9, 99, 999, etc., depending on variable length). These codes are reserved for processing purposes and mean that the item was "Not stated".
The survey estimates are adjusted to account for non-response through the survey weights. To the extent that the non-responding persons differ from the rest of the sample, the results may be biased.
Coverage errors arise when there are differences between the target population and the observed population. The target respondent is the person knowledgeable (aged 15 years and older) about the before and after school child care arrangements for a child aged 4 to 12 who lives in the household and is attending school. The survey frame is the Canada Child Benefit (CCB) file, and it contains every parent who is registered to receive a benefit. It is estimated that the frame represents 96% of the children population of all ages. To the extent that the excluded population differs from the rest of the target population, the results may be biased.
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