Survey of Emergency Preparedness and Resilience in Canada (SEPR)
Detailed information for 2014
The purpose of the Survey of Emergency Preparedness and Resilience (SEPR) is to better understand community resilience in Canada by examining how Canadians prepare for and respond to emergencies or disasters, and how they fare on other social and economic factors related to resilience.
Data release - October 28, 2015
The purpose of the Survey of Emergency Preparedness and Resilience (SEPR) is to better understand community resilience in Canada by examining how Canadians prepare for and respond to emergencies or disasters, and how they fare on other social and economic factors related to resilience. The SEPR collects data on factors that impact how well individuals and communities are able to prepare for, mitigate, respond to and recover from a disaster. It is designed to provide estimates of emergency preparedness and resilience at various levels of geography - national, provincial and some level of detail for larger communities (i.e., populations greater than 50,000).
Data produced from the SEPR will allow policy makers, first responders and non-government organizations to establish priorities, allocate funds and better inform the development of emergency management and community safety programs.
This work is funded by Defence Research and Development Canada's, Centre for Security Science with support from Public Safety.
The SEPR will provide useful information on:
- Canadian's perceptions of the types of risks communities are likely to experience
- How prepared Canadians are for a major emergency or disaster
- People's previous experiences with major emergencies or disasters and the impact of these events on their daily activities, long-term effects and recovery
- What types of formal and informal resources Canadians turn to for help in a major emergency or disaster
- How people with past emergency experiences compare to those without in terms of preparedness
- Differences in preparedness that might exist among certain populations or groups - for example, possible differences by region, age, gender, income, or ethnicity.
The survey results can be used to:
- Identify gaps in crisis management services
- Tailor services to better meet the needs of certain populations served
- Map communities or regions according to their level of vulnerability, especially in more diverse communities
- Develop and deliver strategies to improve resilience.
Reference period: Calendar year
- Society and community
Data sources and methodology
The target population for the SEPR includes all persons 15 years of age and older in Canada, excluding:
1. Residents of the Yukon, Northwest Territories, and Nunavut
2. Full-time residents of institutions.
The questionnaire was designed based on research and extensive consultations with key partners and data users. Qualitative testing was conducted by Statistics Canada's Questionnaire Design Resource Center (QDRC). This testing highlighted the questions which worked well and others that needed clarification or redesign. 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.
The target population for the survey is non-institutionalized persons 15 years of age or older, living in the ten provinces. A raw sample of 73,000 households was selected for this survey, using stratified random sampling.
This survey uses Statistics Canada's new telephone sampling frame. The frame contains landline and cellular telephone numbers from the Census and various administrative sources provided to Statistics Canada. To be more efficient, telephone numbers belonging to the same address were grouped together using Statistics Canada's new dwelling frame.
Data collection for this reference period: 2014-01-07 to 2014-06-30
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Data collection is conducted by Computer Assisted Telephone Interviewing (CATI) methods in the ten provinces.
View the Questionnaire(s) and reporting guide(s) .
Edits were developed as part of the data capture application. These edits are applied at the time of collection to ensure data quality. Anomalies in the information reported are confirmed with the respondent right away and corrected if necessary.
For the computer-assisted telephone interviewing (CATI) application, it is not possible for interviewers to enter out-of-range values, and flow errors are controlled through programmed skip patterns. For example, CATI ensures that questions that do not apply to the respondent are not asked. In response to certain types of inconsistent or unusual reporting, warning messages are invoked. In some instances, no corrective action is taken at the time of the interview and edits are instead performed at Head Office after data collection.
Imputation is a process used to determine and assign replacement values to resolve problems of missing, invalid or inconsistent data. Imputation was used to assign values to the variable, PR_CMACA, for respondents with missing geographical information. PR_CMACA has 73 possible values: 63 values to identify the communities targeted by the survey plus 10 values to identify areas outside of the targeted communities (one per province).
The postal code provided by the respondent was used to derive PR_CMACA. Of the 32,171 respondents, 2,097 (6.5%) had missing or invalid postal codes. The missing values were imputed using the CMA/CA code from the SEPR sampling frame or the AR. To validate the imputation performed, the PR_CMACA of the respondents who provided a valid postal code was compared to the CMA/CA code on the SEPR sampling frame or the AR: we observed equal values for 88% of respondents.
The principle behind estimation in a probability sample is that each unit in the sample "represents", besides itself, several other units not in the sample. For example, in a simple random 2% sample of the population, each unit in the sample represents 50 units in the population.
The weighting phase is a step which calculates, for each record, what this number is. This weight appears on the microdata file, and must be used to derive meaningful estimates from the survey.
The following section provides the details of the method used to calculate sampling weights for the SEPR. The weights were calculated in several steps:
1) Design Weight
Each unit within a stratum was assigned a design weight calculated as follows:
"stratum population size (number of records on sampling frame)"/"stratum sample size"
2) Removal of the Out-of-Scope Units
Telephone numbers belonging to businesses, institutions or other out-of-scope dwellings, as well as numbers not in service or any other non-working numbers are considered out-of-scope for this survey. Out-of-scope units were removed from the weighting process, leaving only in-scope units in the sample.
3) Adjusted Design Weight
The design weight of the in-scope units were adjusted to account for households that are represented on the frame more than once. Respondents were asked to provide a list of the telephone numbers that reach their household. The adjustment was based on this list of numbers.
4) Household Nonresponse
Weights for responding households were adjusted to represent the households that did not respond. The adjustment factors were calculated separately within each stratum.
5) Person Level Weights
Person level weights were calculated by multiplying the household level weights by the number of persons in the household eligible for the survey.
6) Person Nonresponse
Person nonresponse is defined as units with a complete household roster, but with no questionnaire. The adjustment factors were calculated within weighting classes formed using the roster information.
7) Weight Trimming
The weights of a few respondents were trimmed because they had very large weights relative to the other respondents in the same CMA/CA (PR_CMACA). These were units where the design CMA/CA was different from the CMA/CA derived from the collected postal code.
The weights were calibrated so that the sum of the SEPR weights matches demographic population counts, at the province by age group by gender level. The weights were also calibrated to CMA/CA demographic counts for the 63 communities targeted by the survey.
The final (person level) weights for the master file are called WTPM.
The SEPR was conducted by Statistics Canada interviewers. Project managers and senior interviewers are responsible for ensuring that the SEPR interviewers were familiar with the survey's concepts and procedures.
In the event that a respondent refused to be interviewed, Statistics Canada interviewers are trained in basic refusal conversion techniques. If a respondent is adamant, the interviewer is instructed to obtain as much information as possible about the respondent (such as why they are refusing to participate) and refer the case to the senior interviewer. The senior interviewer then attempts to contact the respondent and convert the case. If the senior interviewer is unable to do so, a letter is sent to the respondent as a final effort to convert the case.
The survey application
The use of CAI allows for complex flows and edits to be built into the questionnaire, helping with data quality and ensuring that respondents answer only the questions appropriate to their situations. The survey application underwent testing at Statistics Canada to ensure that it functioned properly. During collection, review screens, range edits, flow pattern edits and general consistency edits, were used for quality control.
Once the data were collected, they were processed according to the Social Survey Processing Environment (SSPE) to produce a final clean file. Each processing step includes verification steps to ensure the data on the final file are of sound quality.
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. They can be classified into two main categories: non-sampling errors and sampling errors.
Sampling error is defined as the error that arises because an estimate is based on a sample rather than the entire population. The bootstrap method was used to estimate the sampling variability of the estimates produced for the survey. Estimates with high sampling variability are indicated in the survey publication.
Non-sampling errors can be defined as errors arising during the course of virtually all survey activities, apart from sampling. They are present in both sample surveys and censuses (unlike sampling error, which is only present in sample surveys). 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.
The overall response rate for the SEPR was 53%. Some non-response occurred at the household level, and some at the individual level. Survey estimates have been adjusted (i.e. weighted) to account for non-response. To the extent that the non-responding households and persons differ from the rest of the sample, the results may be biased.
Other types of non-sampling errors can include response errors and processing errors.
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