New Motor Vehicle Registration Survey (NMVRS)
Detailed information for second quarter 2022
This survey collects quarterly data on the number of new motor vehicles registered in Canada.
Data release - October 11, 2022
The objective of this survey is to collect data on the number of new motor vehicles registered in Canada.
Data collected are aggregated with information from other sources to produce official estimates of national and provincial economic production for this industry.
Survey estimates are made available to businesses, governments, investors, associations, and the public. The data are used to monitor industry growth, measure performance, and make comparisons to other data sources to better understand this industry.
Reference period: Quarter 1 (January - March), Quarter 2 (April - June), Quarter 3 (July - September) and Quarter 4 (October - December) of the calendar year.
- Retail and wholesale
- Transportation by road
Data sources and methodology
The target population consists of all new motor vehicles registered in Canada.
This survey is a census with a cross-sectional design.
This is a census of all sampling units in the survey population.
Prior to the selection of a random sample, enterprises are classified into homogeneous groups (i.e. groups with the same industry and same geography) based on the characteristics of their establishments. Then, each group is divided into sub-groups (i.e. small, medium or large) called strata based on a size measure of the enterprise.
Even when conducting a census, units are still stratified into homogeneous groups.
Sampling and sub-sampling:
Following stratification, units from all strata are sampled, resulting in having all take-all strata.
Data are extracted from administrative files.
Data are extracted from administrative files supplied by provincial and territorial governments.
Data are extracted from administrative files supplied by provincial and territorial governments and aggregated to the estimated domains. Data are analyzed for erroneous figures, and data confidentiality analysis is conducted to ensure confidentiality of each enterprise.
Error detection is an integral part of data processing activities. Edits are applied to data records during integration 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 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.
Donor and historical imputation methods were used when records were missing or had erroneous figures.
This methodology type does not apply to this statistical program.
Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses, general economic conditions, coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications or newspaper articles).
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.
In order to prevent any data disclosure, confidentiality analysis is done using primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information.
Census surveys are not subject to sampling errors but still, are subject to non-sampling errors. Non-sampling error may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Undercoverage or overcoverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with respondents when needed to maximize response rates.
When non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate.
The non-response rate is used to come up with a quality rating code similar with other surveys. This code uses letters that ranges from A to F where A means the data is of excellent quality and F means it is unreliable. These quality rating codes can be requested and should always be taken into consideration.