Annual Survey of Traveller Accommodation

Detailed information for 2001

Status:

Active

Frequency:

Annual

Record number:

2418

This survey collects business operating information for statistical and economic analysis for traveller accommodation services.

Data release - February 26, 2003

Description

This survey collects business operating information for statistical and economic analysis of establishments classified to sub-sector 7211 (Traveller Accommodation) according to the North American Industrial Classification System (NAICS) (e.g, hotels, motels, resorts, bed and breakfasts, outfitters, camping grounds, and other establishments providing accommodation for travellers). 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 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: Calendar year

Collection period: April to July

Subjects

  • Accommodation and food
  • Business, consumer and property services
  • Business performance and ownership
  • Financial statements and performance

Data sources and methodology

Target population

The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Accommodation Services (NAICS 721) according to the North American Industry Classification System (NAICS) during the reference year.

Instrument design

The survey questionnaires comprise financial and operating characteristics, and were developed with extensive consultations with the Canadian Tourism Commission and industry representatives.

Sampling

This is a sample survey.

The survey design was based on probability sampling and only covered the portion of the frame subject to direct data collection.

Prior to the selection of a random sample, establishments are classified into homogeneous groups (i.e., groups with the same NAICS codes, same geography (province/territory), and same business type (incorporated/unincorporated) attributes). 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 includes the largest firms in terms of performance (based on revenue) in an industry. Every firm is sampled, which means each firm represents itself and is given a weight of one. The must-take stratum is also comprised of self-representing units, but these are selected on the basis of complex structure characteristics (multi-establishment, multi-legal, multi-NAICS, or multi-province enterprises). Units in the take-some strata are subjected to simple random sampling.

Finally, the sample size is increased, mostly to compensate for firms that no longer belong in the industry; i.e., they have gone out of business, changed their primary business activity, they are inactive, or are duplicates on the frame. After removing such firms, the sample size for 2001 was 2,869 collection entities.

Data sources

Data collection for this reference period: December 2001 to June 2002

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

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).

Error detection

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. Several checks are performed on the collected data. These checks look for internal consistency such as: section totals must be equal to the components; if employees are reported, personnel costs must be greater than zero; the main source of income must be consistent with the assigned NAICS code; identification of extreme values; etc.

Imputation

Where information is missing, imputation is performed using a "nearest neighbor" procedure (donor imputation), using historical data when available, using averages based on responses from a set of similar establishments, or using administrative data as a proxy for reported data.

Estimation

As part of the estimation process, survey data are weighted and combined with administrative data to produce final industry estimates.

Quality evaluation

Prior to dissemination, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for the largest companies), general economic conditions, historic trends, and comparisons with other data sources.

Disclosure control

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.

Data accuracy

Of the sampled units contributing to the estimate the weighted response rate was 80% in reference year 2001.

The sample of traveller accommodation establishments represents 94% of the estimated industry revenues. The remainder of the estimate was derived from administrative data sources.

Since this survey was based on probability sampling, the potential for error caused by sampling can be measured. A standard measure of sampling error is the coefficient of variation (CV). 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

CVs were calculated for each estimate. Generally, the more commonly reported variables obtained very good CVs, while the less commonly reported variables were associated with higher but still acceptable CVs (under 25%). The CVs are available upon request.

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