Passenger Bus & Urban Transit Survey

Detailed information for 2012

The survey collects annual financial, operating and employment data on bus companies operating in Canada.

Data release - March 27, 2014

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

The survey collects annual financial, operating and employment data on bus companies operating in Canada. It also includes municipalities and government agencies that operate urban transit and commuter services. The resulting estimates are used as input to the Canadian System of National Accounts, by Transport Canada, other federal and provincial departments, and by transportation companies, consulting firms, universities and foreign governments. The information is used for the analysis of transportation activity, for marketing and economic studies, as well as industry performance measures.

Statistical activity

This statistical activity is part of a set of surveys measuring various aspects of activities related to the movement of people and goods. These surveys are grouped as follows:

Transportation by air includes records related to the movement of aircraft, passengers and cargo by air for both Canadian and foreign air carriers operating in Canada as well as the financial and operating characteristics of Canadian air carriers. These data are produced by the Aviation Statistics Centre.

Transportation by rail includes records relating to rail transportation in Canada, and between the United States and Canada.

Transportation by road includes records relating to all road transport in Canada. In addition to surveying carriers and owners of registered motor vehicles, certain programs rely on aggregation of provincial and territorial administrative records.

Reference period: Calendar year

Subjects

• Transportation
• Transportation by road

Data sources and methodology

Target population

The industry is defined by six bus industry categories using the North American Industrial Classification System (NAICS). The six categories are urban transit, scheduled intercity, charter, school bus, sightseeing and shuttle. (NAICS code 485110, 485210, 485410, 485510, 485990 and, 487110). Non-bus companies are excluded from the Sightseeing NAICS (e.g. companies that operate sightseeing trains).

Urban transit systems that are included within the financial structure of municipal governments, and which therefore may exist outside one of the six bus industry NAICS, are included as a separate category to provide users with a complete data set by activity. For the Canadian System of National Accounts, the separate category is excluded, to avoid double counting.

• Additional documentation

Instrument design

In accordance with Statistics Canada testing policy, the instrument was tested by visiting several bus companies and by filling in appropriate test data to ensure the integrity of the questionnaire. The questionnaire was modified following the formal test. Many bus companies were also asked to provide input during the design of the questionnaire.

The new annual survey format means it is no longer possible to compare data from 2001 and later years with data from 2000 and earlier. However, the new survey questionnaire is a much better tool, improving the quality of the data collected while at the same time greatly reducing the burden placed on most respondents. Data are provided by NAICS (for each of the 6 bus industry categories) as well as by activity (product lines).

Sampling

This is a sample survey with a cross-sectional design.

The frame consists of companies appearing on the Business Register of Statistics Canada, classified in the six NAICS of interest. The companies are stratified by NAICS6 and province. Only the companies that make up the top 95% of revenues are included in the target population. Using Lavallée-Hidiroglou methodology, the strata are further split into one take-all stratum and up to two take-some strata, based on the sizes (expected revenues as found on the Business Register) of the companies. The Lavallée-Hidiriglou methodology utilizes Neyman allocation to optimize the sample size for a target Coefficient of Variation (13% at the NAICS6 x Province level), minimizing the response burden to small and medium sized companies. In the take-some strata, simple random sampling is used to select the sample of companies.

Data sources

Data collection for this reference period: 2013-03-31 to 2013-10-31

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Operations and Integration Division (OID) of Statistics Canada conducts the mail-out and collection. Data are collected by means of a questionnaire submitted by respondents annually. Telephone, e-mail and fax follow-ups are conducted to resolve edit problems with questionnaires and to collect data from respondents who have not returned the questionnaire.

View the Questionnaire(s) and reporting guide(s) .

Error detection

A series of edits are used during the data collection phase of the survey as well as during the analysis of data. Ratio analysis, comparative analysis, outlier detection are utilized.

At the micro level, several checks are performed on the data to verify internal consistency and identify extreme values. At the macro level, the data are subjected to a detailed quality review process, including a comparative analysis to prior year. Material errors are thereby identified and corrected.

Imputation

Various manual methods for imputation, such as donor imputation, ratio analysis and trend analysis are utilized. Tax data are also used when applicable.

Estimation

The Generalized Estimation System developed at Statistics Canada is used to produce the domain estimates and quality indicators. It is a SAS based application for producing estimates for domains of a population based on a sample and auxiliary information. Estimates are computed at several levels of interest, such as North American Industry Classification System and province, based on the most recent classification information for the statistical entity and the survey reference period.

Quality evaluation

Survey results are analyzed at both the micro and macro level. At the micro level, checks are performed on the data to verify internal consistency and identify extreme values. At the macro level, the data are subjected to a detailed quality review process, including a comparative analysis to prior year. Material errors are thereby identified and corrected.

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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for 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

Annual estimates are provided for the reference year. The data for the previous reference year are revised if necessary. As this is an annual program, seasonal adjustments are not applicable.

Data accuracy

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.

Non-sampling errors are controlled through a careful design of the questionnaire, the use of a minimal number of simple concepts and consistency checks. Coverage error was minimized by using multiple sources to update the frame. Measures such as response rates are used as indicators of the possible extent of non-sampling errors.

The weighted response rate represents the proportion of the total revenue accounted for by units that responded to the survey. Of the sampled units contributing to the estimate, the weighted response rate is calculated, after accounting for firms that have gone out of business, have been reclassified to a different industry, are inactive, or are duplicates on the frame.

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. 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.

CVs were calculated for each estimate. The total revenue estimates for the whole industry were judged to be excellent at the national level (under 5%) and good to excellent at the provincial/territorial level (under 15%). The CVs are available upon request.

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%
Too unreliable to be published: 35.00% or higher

In addition to increase variance, non-response can result in biaised estimates if non-respondents have different characteristics from respondents. Non-response is addressed through a follow-up with respondent, imputation and validation of microdata.

Coverage error was minimized by keeping the frame up to date using survey and administrative sources. Coverage rates are monitored during sampling process.