Annual Survey of Engineering Services

Detailed information for 2002

Status:

Active

Frequency:

Annual

Record number:

2439

The survey objective is the collection and publication of data necessary for the statistical analysis of the engineering services industry.

Data release - December 16, 2003

Description

The survey objective is the collection and publication of data necessary for the statistical analysis of the engineering services industry.

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 other users involved in research or policy making.

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.

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 establishments classified to the engineering services industry (NAICS 541330) according to the North American Industry Classification System (NAICS) during the reference year. This industry comprises establishments primarily engaged in applying principles of engineering in the design, development and utilization of machines, materials, instruments, structures, processes and systems. The assignments undertaken by these establishments may involve any of the following activities: the provision of advice, the preparation of feasibility studies, the preparation of preliminary and final plans and designs, the provision of technical services during the construction or installation phase, the inspection and evaluation of engineering projects and related services.

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.

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-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 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. The resulting sample size for this survey after removing firms that should not have been included in the frame (out of scope, duplicate records or out of business) was 483 companies.

Data sources

Data collection for this reference period: November 18, 2002 to July 26, 2003

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

Even though the basic objective of the survey is to produce estimates for the whole industry-all incorporated and unincorporated businesses-not all businesses are surveyed. Rather, a sample is surveyed and the portion eligible for sampling is defined as all statistical establishments with revenue above a certain threshold. (Note: the threshold varies between surveys and sometimes between provinces in the same survey). The excluded portion represents a substantial proportion of the industry in terms of number of establishments (74%), but its contribution to the overall industry revenue is only about 10%. These excluded establishments are accounted for in the final estimates through the use of administrative data. However, only basic information is obtained from administrative sources; i.e., total revenue, expenses, depreciation and salaries, wages and benefits. Detailed characteristics such as client base, revenue by type of service, and detailed expense items are collected only for surveyed establishments.

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 administrative data (e.g., income tax, goods and services tax, payroll deductions records, industry and trade association 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 65% in reference year 2002.

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.

For the Annual Survey of Engineering Services, 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%). The CVs are available upon request.

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