Monthly Survey of Smelters and Metal Refineries (MSMR)
Detailed information for July 2017
The purpose of this monthly survey is to obtain information on the quantities of metal being produced by smelters and metal refineries in Canada, including Canadian production originating from external sources.
Data release - September 20, 2017
Information from this survey is used for market analysis, industrial and regional development, establishing trade and tariff policies, and managing natural resources.
Reference period: Month
Collection period: Data collection for this reference period: the first 12 days following the reference month
- Natural resources
- Non-metallic mineral and metal
Data sources and methodology
The target population is represented by all Smelters and Metal Refineries having activities in Canada. The observed population is represented by the Smelters and Metal Refineries having activity in Canada known and pre-identified by analysts in Statistics Canada.
The electronic questionnaire used for this survey has been designed to minimize different interpretations.
The survey was field tested with respondents to ensure the questions, concepts and terminology were appropriate. Statistics Canada's Questionnaire Design and Resource Centre (QDRC) performed qualitative tests of the questionnaire by conducting cognitive interviews with 6 companies in Ontario and Quebec.
This survey is a census with a cross-sectional design.
A cross-sectional monthly census of all Smelters and Metal Refineries having activities in Canada known and pre-identified by analysts in Statistics Canada.
Single-phase and single-stage census survey. The sampling unit being Establishment level on the Business Register.
Strata are built from all provinces and territories in Canada, but all units are selected since it is a census survey.
The census contains 16 sampling units known by the analysts and these units are saved on the sampling frame.
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data is collected through electronic questionnaire, while providing the option to reply by telephone interview. Follow-up for non-response and for data validation is conducted by telephone.
View the Questionnaire(s) and reporting guide(s) .
Error detection is an integral part of both collection and data processing activities. Automated edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals, and ratios that exceed tolerance thresholds, as well as identify problems in the consistency of collected data (e.g. a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g. Value > 0, Value > -500, Value = 0), linear equality edits (e.g. Value1 + Value2 = Total Value), linear inequality edits (e.g. Value1 >= Value2), and equivalency edits (e.g. Value1 = Value2). When errors are found, they can be corrected using the failed edit follow up process during collection or via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.
When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated, statistical techniques used to impute the missing data include: deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample (known as donor imputation). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related variables.
All units in the observed population are being surveyed. Estimation of totals is done by simple aggregation of the values of all estimation units that are found in the domain of estimation. Estimates are computed for several domains of estimation such as industrial groups and provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period. It should be noted that this classification information may differ from the original sampling classification since records may have changed in size, industry or location. Changes in classification are reflected immediately in the estimates.
Statistics Canada is prohibited by law from releasing any information it collects which 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.
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