Personal Protective Equipment Survey (PPES)

Detailed information for August 2020

Status:

Active

Frequency:

Every 2 months

Record number:

5332

Survey results will help to identify possible future supply needs in order for businesses to return to the workplace, as well as addressing PPE needs required to respond to new provincial and federal guidelines for health and safety in relation to the COVID-19 pandemic.

Data release - Scheduled for October 7, 2020

Description

This survey aims to collect information on the supply, demand and inventories of PPE (Personal Protective Equipment).

Collection period: During the month following the reference month.

Subjects

  • Families, households and housing
  • Health
  • Prices and price indexes
  • Society and community

Data sources and methodology

Target population

The target population consists of all active establishments with at least one employee, operating in Canada, from the following targeting NAICS group: manufacturers, wholesalers, retailers, service industries, and multiple industries that will require ongoing inventories of PPE for use in business operations. The observed population is taken from the Business Register (BR) and consists of establishments from the target population.

Instrument design

Questionnaire design is ongoing. Methodologies and questionnaire design were researched and developed based on international practices and improved through the efforts of Statistics Canada's Questionnaire Design Resource Centre and Survey Partners. Focus groups with respondents were also conducted.

Sampling

This is a sample survey with a cross-sectional design and a longitudinal follow-up.

The sampling frame is extracted from Statistics Canada's Business Register (BR) for establishments with supplementary administrative sources.

The sampling unit is the establishment, as defined in the Business Register.

Stratification method:
The units on the frame are stratified NAICS (mix of NAICS2, NAICS3 and NAICS4) and by groups of provinces (Atlantic provinces, Québec, Ontario, Manitoba, Saskatchewan, Alberta, BC, Territories). However, the frame was not stratified by size since a (stratified) probability-proportional-to-size (PPS) sampling design was used.

Sampling and sub-sampling:
The same sample of 8,000 units (panel) will be sent to data collection every two months, starting in July 2020. For the first month (June 2020), a subsample of 1,500 units was collected.

Data sources

Data collection for this reference period: 2020-08-31 to 2020-09-21

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Collection Method:
EQ, Telephone

Capture Method:
Respondent capture, Interviewer capture and through BCP.

Initial Contact:
Telephone

Follow-up method:
Telephone

Error detection

Data editing is the application of rules to detect missing, invalid or inconsistent entries or to identify data records that are potentially in error. In the survey process, data editing is done at two different time periods.

First, editing is done during electronic questionnaire collection. Edits during data collection generally consist of validity and some simple consistency edits. Follow-up with respondents occurs to validate potential erroneous data following any failed preliminary edit check of the data. Once validated, the collected data is regularly transmitted to the head office in Ottawa.

Second, editing known as statistical editing is also done after data collection. Units with large deviations from a predictive frame variable are not used at the imputation stage. Furthermore, small outliers indicate a misselection of the unit of measure and are converted into the most plausible unit of measure. Large and small outliers cannot be used at the imputation stage.

Imputation

In the case of partial non-response, imputation is used to fill in information not provided by the respondent. Imputation makes it possible to have a complete set of data if one cannot collect it during the collection period.

The imputation models used are based on linear regression (weighted and unweighted) using one or multiple explanatory variables. A small number of variables are imputed deterministically, based on pre-specified rules.

Estimation

Estimating the characteristics of a population from a survey is based on the assumption that each sampled unit represents a certain number of non-sampled units in the population. An initial weight is assigned to each unit to indicate the number of units in the population represented by that unit in the sample. Very important or otherwise unique units are assigned a weight of one to ensure that they only represent themselves.

Adjustments are made to the initial weights to improve the representativity of the sample and the reliability of the estimates. The weights are adjusted to compensate for total non-response. They are also calibrated to benchmark establishment counts and employment totals to totals from administrative sources. For this survey, the adjustments are done together through calibration.

Quality evaluation

Prior to publication, survey results are analyzed for comparability. This includes a detailed review of the estimates, and a comparison to other industry sources, historical trends and general economic conditions.

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.

Revisions and seasonal adjustment

This methodology does not apply.

Data accuracy

The estimates obtained from sample surveys are subject to both sampling and non-sampling errors.

Non-sampling errors may occur throughout a survey for many reasons, such as non-response, coverage and classification errors, differences in the interpretation of the question, incorrect information from respondents, as well as mistakes during data capture, coding, and processing. Efforts to reduce non-sampling errors include careful design of questionnaires, editing of data, follow-up, imputation for non-responding units, and thorough control of processing operations.

The use of sampling frames results in coverage errors, notably undercoverage. Undercoverage occurs when the information on an establishment is incomplete in the Business Register. This normally happens in the case of new establishments that have not yet filed tax forms with the Canada Revenue Agency.

Sampling errors occur because observations are obtained from a sample rather than from the entire population. Estimates based on a sample can differ from statistics that would have been obtained if a complete census had been taken using the same instructions, interviewers and processing techniques. This difference is called the sampling error of the estimate.

The true sampling error is unknown. However, it can be estimated from the sample itself by using a statistical measure called the standard error. The standard error can be used to build a confidence interval for the estimate. When the standard error is expressed as a percentage of the estimate, it is known as the relative standard error or the coefficient of variation (CV).

Estimates from this survey are assigned a quality indicator in the form of a letter to indicate their quality level (A-best, F-worst). The indicators take into account various factors that affect the quality of the data, notably the CV, the non-response errors, and the imputation errors. These indicators are updated each cycle to reflect the quality of the current estimates.

Non-sampling error
Non-sampling errors may occur throughout a survey for many reasons, such as non-response, coverage and classification errors, differences in the interpretation of the question, incorrect information from respondents, as well as mistakes during data capture, coding, and processing. Efforts to reduce non-sampling errors include careful design of questionnaires, editing of data, follow-up, imputation for partial non-response, and thorough control of processing operations.

Non-response Bias
The units in the sample are assigned initial weights at the sampling step, to make them represent a certain number of units in the population. The weights of the responding units are further increased to make these responding units represent non-responding units, as well as the population units they were meant to represent initially.

In order to reduce possible non-response bias, this weight adjustment is performed on groups of units sharing the same characteristics as much as possible ("homogeneous response groups") - in term of geography, industry and size, for example.

Coverage error
The use of sampling frames results in coverage errors, notably undercoverage. Undercoverage occurs when the information on an establishment is incomplete in the Business Register. This normally happens in the case of new establishments that have not yet filed tax forms with the Canada Revenue Agency.

The Business Register is kept up to date continuously.

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