Business Conditions Survey for the Traveller Accommodation Industry (BCS-TA)
Detailed information for second quarter 2010
This survey seeks opinions about current and future business conditions for the Traveller Accommodation Industry. The information is used to produce an early broadly-based summary of business opinion regarding impediments to trade, current and future business activity, and employment.
Data release - May 5, 2010 (This is the last instalment of this survey as the data series terminates with this release of second quarter 2010 data.)
- Questionnaire(s) and reporting guide(s)
- Data sources and methodology
- Data accuracy
This survey seeks informed opinions of industry participants about current and future conditions in their industry. Our objective is to produce good co-incident and leading indicators for the traveller accommodation industry specifically, and the tourism sector in general. These business tendency indicators measure impediments to trade, current business activity and business activity outlook for the next three months. Measurable indicators such as occupancy rates and employee hours are used as business activity indicators. This project was funded by industry partners, the Canadian Tourism Commission, the Ontario Ministry of Tourism, Saskatchewan Tourism, the Nova Scotia Department of Tourism, Culture and Heritage and Industry Canada.
The data are used by business operators and associations for market analysis and assessment of industry performance, operating characteristics and trends; by governments to develop national and regional economic policies; by agencies such as the Canadian Tourism Commission and the Ontario Ministry of Tourism for analysis and policy making and for providing valuable statistics and information feedback to the industries; and by Statistics Canada for maintaining important data input to the preparation of the Canadian System of National Accounts.
Reference period: Quarter
- Accommodation and food
- Business, consumer and property services
- Business performance and ownership
- Current conditions
Data sources and methodology
The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Traveller Accommodation (NAICS 7211) according to the North American Industry Classification System (NAICS) during the reference year. For a brief description of Traveller Accommodation, please refer to the link below.
The survey questionnaire was developed with extensive consultations with the Canadian Tourism Commission, Ontario Ministry of Tourism and industry representatives.
This is a sample survey with a cross-sectional design and a longitudinal follow-up.
The survey has been designed as the second phase sub-sample of the corresponding annual Traveller Accommodation sample. Please refer to survey 2418 for additional information pertaining to how the sample is devised.
The effective sample size for this survey was 1,414 collection entities.
Data collection for this reference period: 2010-03-23 to 2010-04-14
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Data are collected through a fax-out/fax-back process, while providing respondents with the option of telephone. Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time.
View the Questionnaire(s) and reporting guide(s) .
Data are examined for inconsistencies and errors using automated edits coupled with analytical review. Every effort is made to minimize the non-sampling errors of omission, duplication, reporting and processing.
This methodology type does not apply to this statistical program.
As part of the estimation process, survey data are weighted by revenue to produce final industry estimates and are based on a sub-sample of the Annual Survey of Traveller Accommodation. The weights are based on the revenue size reported to the Annual Survey of Traveller Accommodation.
The balance of opinion is determined by subtracting the proportion of traveller accommodation businesses that stated their business activity would be "lower" from the proportion who believed their activity would be "higher".
The final data sets are subject to rigorous analysis that includes comparison to historical series and comparisons to other sources of data in order to put the economic changes in context.
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.
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 sampled units contributing to the estimate, the weighted response rate was 72.8% in March 2010.
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 standard error. The assumption is that over repeated surveys, the 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 standard error. 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 standard error. First, we calculate the standard error. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.
For the Quarterly Business Conditions Survey for the Traveller Accommodation industry, the standard errors were calculated for each estimate. The standard errors range from very good to excellent. The standard errors are available upon request.
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