Annual Survey of Service Industries: Repair and Maintenance Services
Detailed information for 2007
This survey collects the financial and operating data needed to develop national and regional economic policies and programs.
Data release - April 1, 2009
This annual sample survey collects data required to produce economic statistics for the Repair and Maintenance Services in Canada.
Data collected from businesses 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.
The survey is administered as part of the Unified Enterprise Survey program (UES). The UES program has been designed to integrate, gradually over time, the approximately 200 separate business surveys into a single master survey program. The UES aims at collecting more industry and product detail at the provincial level than was previously possible while avoiding overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content. The unified approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts.
This survey is part of the Service Industries Program. The survey data gathered are used to compile aggregate statistics for over thirty service industry groupings. Financial data, including revenue, expense and profit statistics are available for all of the surveys in the program. In addition, many compile and disseminate industry-specific information.
Reference period: Calendar year
- Business, consumer and property services
- Business performance and ownership
- Financial statements and performance
- Repair and maintenance
Data sources and methodology
The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Repair and Maintenance (NAICS 811) according to the North American Industry Classification System 2007 (NAICS 2007) during the reference year. The Repair and Maintenance sector covers five NAICS 2007: Automotive Mechanical and Electrical Repair and Maintenance (NAICS 81111), Automotive Body, Paint, Interior and Glass Repair (NAICS 81112), Other Automotive Repair and Maintenance (NAICS 81119), Electronic and Precision Equipment Repair and Maintenance (NAICS 81121) and Commercial and Industrial Machinery and Equipment (except Automotive and Electronic) Repair and Maintenance (NAICS 81131).
This subsector comprises establishments primarily engaged in repairing and maintaining motor vehicles, machinery, equipment and other products. These establishments repair or perform general or routine maintenance on such products, to ensure that they work efficiently.
The survey questionnaires comprise generic modules that have been designed to cover several service industries. These modules include revenues, expenses, and employment, as well as an industry-specific module designed to ask for financial and non-financial characteristics that pertain specifically to this industry.
This is a sample survey with a cross-sectional design.
The frame is the list of establishments from which the portion eligible for sampling is determined and the sample is taken. The frame provides basic information about each firm including address, industry classification, and information from administrative data sources. The frame is maintained by Statistics Canada's Business Register and is updated using administrative data.
The basic objective of the survey is to produce estimates for the whole industry - incorporated and unincorporated businesses. The data come from two different sources: a sample of all businesses with revenue above or equal to a certain threshold (note: the threshold varies between surveys and sometimes between industries and provinces in the same survey) for which either survey or administrative data may be used; and administrative data only for businesses with revenue below the specified threshold. It should be noted that only financial information is available from businesses below the threshold; e.g., revenue, and expenses such as depreciation and salaries, wages and benefits. Detailed characteristics are collected only for surveyed establishments.
Prior to the selection of a random sample, establishments are classified into homogeneous groups (i.e., groups with the same NAICS codes and same geography). Quality requirements are targeted, and then each group is divided into sub-groups called strata: take-all, must-take, and take-some.
The take-all stratum represents the largest firms in terms of performance (based on revenue) in an industry. The must-take stratum is comprised of units selected based on complex structural characteristics (multi-establishment, multi-legal, multi-NAICS, or multi-province enterprises). All take-all and must-take firms are selected to the sample. Units in the take-some strata are subject to simple random sampling.
The effective sample size for reference year 2007 was 4,042 collection entities.
Data collection for this reference period: 2008-02-28 to 2008-08-05
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
Data are collected through a mail-out/mail-back process, while providing respondents with the option of telephone or other electronic filing methods.
Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period.
View the Questionnaire(s) and reporting guide(s).
Data are examined for inconsistencies and errors using automated edits coupled with analytical review. Where possible, data will be verified using alternate sources.
Partial records are imputed to make them complete. Data for non-respondents are imputed using donor imputation, administrative data, or historical data.
As part of the estimation process, survey data are weighted and combined with administrative data to produce final industry estimates.
Prior to dissemination, combined survey results are analyzed for overall quality; in general, this includes a detailed review of individual responses (especially for the largest companies), an assessment of the general economic conditions portrayed by the data, historic trends, and comparisons with other data sources.
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
There is no seasonal adjustment. Data from previous years may be revised based on updated information.
While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.
Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.
Of the units contributing to the estimate, the weighted response rate was 82.6%.
Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The assumption is that over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all units in the universe would be less than twice the CV, 95 times out of 100. The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. First, we calculate the standard error by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.
The qualities of CVs are rated as follows:
. Excellent 0.01% to 4.99%
. Very good 5.00% to 9.99%
. Good 10.00% to 14.99%
. Acceptable 15.00% to 24.99%
. Use with caution 25.00% to 34.99%
. Unreliable 35.00% or higher
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