Annual Survey of Architectural Services
Detailed information for 2005
Status:
Active
Frequency:
Annual
Record number:
2420
The survey objective is the collection and dissemination of data necessary for the statistical analysis of the Architectural Services.
Data release - December 11, 2006
Description
The survey objective is the collection and dissemination of data necessary for the statistical analysis of the Architectural Services.
The information from the survey can be used by businesses for market analysis, by trade associations to study performance and other characteristics of their industry, by government to develop national and regional economic policies, and by others involved in research or policy making.
Statistical activity
The survey is administered as part of the Unified Enterprise Survey program (UES). The UES program has been designed to integrate, gradually over time, the approximately 200 separate business surveys into a single master survey program. The UES aims at collecting more industry and product detail at the provincial level than was previously possible while avoiding overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content. The unified approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts.
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
Collection period: February to September
Subjects
- Business, consumer and property services
- Business performance and ownership
- Financial statements and performance
- Professional, scientific and technical services
Data sources and methodology
Target population
The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Architectural Services (NAICS 541310) according to the North American Industry Classification System (NAICS) during the reference year.
Instrument design
The survey questionnaires comprise generic modules that have been designed to cover several service industries. These modules include revenues, expenses, and employment, as well as an industry-specific module designed to ask for financial and non-financial characteristics that pertain specifically to this industry.
In order to reduce respondent burden, smaller firms receive a characteristics questionnaire (shortened version) which does not include the revenue and expense modules. For smaller firms, revenue and expense data are extracted from administrative files.
Sampling
This is a sample survey with a cross-sectional design.
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, 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-group called strata: must-take, take-all, large take-some and small 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 2005 was 381 collection entities.
Data sources
Data collection for this reference period: 2006-01-30 to 2006-08-04
Responding to this survey is mandatory.
Data are collected directly from survey respondents and extracted from administrative files.
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.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information.
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 77% in reference year 2005.
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%).
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