Monthly Survey of Smelters and Metal Refineries (MSMR)
Detailed information for July 2020
The purpose of this monthly survey is to obtain information on the quantities of metals produced by Smelters and Metal Refineries in Canada, including Canadian production originating from external sources.
Data release - September 21, 2020
Information from this survey is used for market analysis, industrial and regional development and managing natural resources.
Reference period: Month
Collection period: During the month following the reference period.
- 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 electronic questionnaire used for this survey has been designed to minimize different interpretations.
The survey was also field tested with respondents to ensure that the questions, concepts and terminology are appropriate. Statistics Canada's Questionnaire Design and Resource Centre (QDRC) performed qualitative testing of the questionnaire by conducting cognitive interviews with multiple companies in Ontario and Quebec.
This survey is a census with a cross-sectional design.
Data are collected for all Smelters and Metal Refineries having activities in Canada.
This is a single-phase and single-stage census survey. The sampling unit is at the Establishment level on Statistics Canada's Business Register.
There are presently less than twenty sampling units listed on the Business Register.
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Data is collected using an electronic questionnaire, while providing the option to reply by telephone interview. Follow-up for non-response and for data validation may be conducted by telephone or by email.
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 of a variable does not equal to 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 detected, 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 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 related to the omission, duplication, misclassification, reporting and processing of the data.
In the case of non-response, incomplete answers or when reported data is considered incorrect during the error detection steps, imputation is used to fill in the missing information and to modify the incorrect information. Multiple 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). Initially key variables are imputed and are used as anchors in subsequent steps to impute other related variables.
All units in the observed population as classified by the Business Register are surveyed. Estimation of totals is completed 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 industry groups and provinces or 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.
Survey results are analyzed to ensure comparability with patterns observed in the historical data series and the economic condition of the industry. Information available from other sources such as the media and other governmental organizations are also used in the validation process.
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.
Revisions and seasonal adjustment
The monthly data released for the current reference month present preliminary estimates that are subject to revision. With the release of the current reference month, revised estimates based on late responses are provided for the two previous months.
Estimates for every month of the previous year are revised during the second quarter of the following year to take into account late responses.
The data are not seasonally adjusted; therefore comparisons should only be done on a year-over-year basis.
This is a census survey with a high response rate, averaging 90% over the last year. Hence, under-coverage is minimal and the bias introduced from non-response cases is negligible. The use of historical data for imputation purposes helps in keeping the data aligned with trends to reduce overestimation and maintain accuracy.
The questionnaire is strategically designed to control non-sampling errors with the standardization of terms and the use of edit checks.