Annual Survey of Traveller Accommodation

Detailed information for 1998

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

Data release - February 08, 2001

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

Description

This survey collects business operating information for statistical and economic analysis of establishments classified to sub-sector 721 (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

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.

• Additional documentation

Sampling

This is a sample survey.

The basic objective of the survey is to produce estimates for all industries within Traveller Accommodation. The portion of the population eligible for sampling was defined as all incorporated statistical establishments with revenue above $50,000. Some exceptional unincorporated units were also added to direct data collection if their contribution was deemed significant. A few small, unincorporated units belonging to complex enterprises were also added. The main motivation for the exclusion of unincorporated firms and incorporated firms below $50,000 from direct data collection was to achieve major reductions in the response burden.

The frame is the list that identifies the firms classified to the industry in question. The frame is maintained by Statistics Canada's Business Register, using taxation account information (i.e., income tax, goods and services tax and payroll deductions records) submitted to Revenue Canada.

The survey design covered only the portion of the frame subject to direct data collection. Prior to the selection of a random sample, units are grouped in homogeneous groups defined using industrial (NAICS) and geographic (province/territory) attributes. Similar quality requirements are targeted for each group which is then divided into four sub-groups called strata: must-take, take-all, large take-some and small take-some.

The take-all stratum includes the largest firms in terms of industrial performance which are selected in the sample with certainty making such units self-representing. The must-take stratum is also comprised of self-representing units that have a complex structure (multi-establishments, multi-legal, multi-NAICS or multi-province enterprises). Units in the two take-some strata are subjected to a random sample where each sampled firm represents a number of other, similar firms in the industry/province combination according to the inverse of their probability of selection.

Finally, the size of the sample was increased to compensate for such situations as non-response and firms which cannot be contacted because they have moved or gone out of business.

Imputation

An imputation method is used to estimate the revenue and expense details and other characteristics for the non-responses and for those responses that did not meet the consistency criteria. Wherever possible, information from an adjacent year for the same record was used; when this was not feasible, information was obtained from the nearest neighbour donor within the same industry group (I.e., another record with similar provincial and revenue size group characteristics).

Estimation

The survey data collected from the sample are weighted using the inverse of the probability of selection of each sampled unit to produce estimates representative of the target population. Tax information is used to estimate the portion that is exluded from survey activity (i.e., unincorporated firms and incorporated firms with revenue sess than $50,000).

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

While considerable effort was made to ensure high standards throughout all collection and processing operations, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: sampling and non-sampling .

Non-sampling errors are 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, mistakes in recording, coding and processing of data are other examples of non-sampling errors.

Sampling errors can occur because estimates are derived from a sample of the population rather than the entire population. These errors depend on factors such as sample size, sampling design and the method of estimation. An important property of probability sampling is that sampling errors can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). Over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all the units would be less than twice the coefficient of variation, 95 times out of 100. The sample estimate plus or minus twice the CV is referred to as the 95% confidence interval. For the 1998 Survey of Traveller Accommodation, CVs were calculated for each estimate. Generally, the more commonly reported variables obtained very good CVs (10% or less) while the less commonly reported variables were associated with higher CVs (under 25%). These CVs are available upon request.