Annual Survey of Travel Arrangement and Reservation Services
Detailed information for 2001
Status:
Active
Frequency:
Annual
Record number:
2423
This survey collects financial and operating data needed for the statistical analysis of the travel arrangement industry.
Data release - February 27, 2003
Description
The Annual Survey of Travel Arrangement and Reservation Services collects detailed characteristics such as client base, revenue by type of service, detailed expenses items and employment data from retail travel agencies, tour operators, wholesalers, and other miscellaneous service industries in Canada.
The information is used by various levels of government for marketing and promotional purposes, as well as Tourism Canada which uses the data for planning purposes.
Statistical activity
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
Subjects
- Accommodation and food
- Business, consumer and property services
- Business performance and ownership
- Financial statements and performance
- Travel and tourism
Data sources and methodology
Target population
The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as travel agencies (NAICS 561510) or Tour operators (NAICS 561520) according to the North American Industry Classification System (NAICS) during the reference year.
Instrument design
The annual survey questionnaire covers detailed financial and operating characteristics. In addition, questions on such topics as employment and sources of revenue are asked. The questionnaire was developed in consultation with potential respondents, data users, and questionnaire design specialists.
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 813 collection entities.
Data sources
Data collection for this reference period: June 2002 to September 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. Where possible, data will be verified using alternate sources.
Imputation
Partial records are imputed to make them complete. Data for non-respondents are imputed using donor imputation, administrative data, or historical 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 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.
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 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.
The response rate for this survey was 60% in reference year 2001.
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.
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 but still acceptable CVs (under 25%).
. 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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