Survey of Electronic Commerce and Technology (SECT)

Detailed information for 2006





Record number:


The objective of this survey is to measure the use of various technologies by Canadian businesses and the extent to which the Internet is used to buy and sell goods and services.

Data release - April 20, 2007


The Survey of Electronic Commerce and Technology (SECT) measures the use of various information and communications technologies (ICTs) by Canadian businesses and the extent to which the Internet is used to buy and sell goods and services. The survey also measures the perceived benefits of conducting business over the Internet.

The information collected provides categorical data on the use of information and communication technologies (ICTs) (including the use of computers, the Internet, web sites) and electronic commerce among private and public sector enterprises.

Electronic commerce represents more than a technology; it is a pervasive phenomenon built around the applications of ICTs and plays a catalytic role impacting on every aspect of the value chain for products and services. Issues related to electronic commerce pose numerous challenges to both businesses and policy makers.

The data from this survey are used by businesses and policy makers to monitor the performance of the various ICTs and assess their impact on the economy and international organizations such as the Organization for Economic Co-operation and Development (OECD) to study the development and the influence of this sector on the global information economies.

Statistical activity

Science and technology (S&T) and the information society are changing the way we live, learn and work. The concepts are closely intertwined: science generates new understanding of the way the world works, technology applies it to develop innovative products and services and the information society is one of the results of the innovations.

People are looking to Statistics Canada to measure and explain the social and economic impacts of these changes.

The purpose of this Program is to develop useful indicators of S&T activity in Canada based on a framework that ties them together in a coherent picture.

Reference period: 12 month fiscal period for which the final day occurs on or between January 1st and December 31st of the reference year.

Collection period: November to February


  • Business and government internet use
  • Information and communications technology

Data sources and methodology

Target population

This survey covers most industrial sectors with the exception of local governments. The collection entity for the survey is the enterprise which is the organizational unit of a business that directs and controls the resources relating to its domestic organization and for which consolidated financial and balance sheet accounts are maintained. The implication is that the survey collects data on transactions that occur between enterprises, while it specifically excludes intra-firm transactions, i.e. Internet transactions that may occur between two establishments or companies within the same enterprise.

The industrial classification assigned to the enterprise engaged in electronic commerce is the industrial classification of the establishment with the highest value-added within that enterprise. For instance, if an Internet transaction were conducted in a retail establishment within a manufacturing enterprise, that activity would be classified as a sale of the manufacturing sector.

Instrument design

The survey content was developed in concert with Industry Canada and it employs definitions of electronic commerce that were developed by the Organisation for Economic Co-operation and Development (OECD). Many of the questions used in this survey are adopted from those developed by the OECD and its member countries. With the same questions asked on various national surveys, international comparisons of information and communications technologies and electronic commerce use is feasible.


This is a sample survey with a cross-sectional design.

The survey focuses on the use of information and communications technologies such as personal computers, e-mail and the Internet and distinguishes between private and public sector usage from a sample of Canadian enterprises. This is the target population of the survey.

The frame consists primarily of the Business Register (BR) developed by Statistics Canada. The sampling unit is the enterprise. An administrative list is also used to cover some sectors such as the public sector, a part of the mining sector and the oil and gas sector.

The sample is first stratified by NAICS at the level required for estimation. Within each industrial level, three strata by size are built: large units, which are sampled with certainty, and medium and small units, in which the sampling is conducted using a probability of selection. The size variable is the Gross Business Income for the private enterprises and the Number of Employees for the public enterprises.

The method used is the Lavallée-Hidirouglou algorithm, which does the stratification and the sample allocation to strata by minimizing the sampling size while attaining the target CV based on the size variable.

A sample of around 21,000 enterprises targets a CV of 3.5% in the majority of industries except for two new sectors covered by the survey (agriculture and construction) where a CV of 8% is targeted.

Once the stratification and the allocation are done, there is an increase in the sample size in some strata when necessary in order to obtain a minimum sampling fraction of 1% and a minimum of five units by stratum when possible.

All units are selected with certainty in the take-all strata while a random sample is selected in the take-some strata under the constraint of maximizing the overlap with the previous year's sample. The Kish and Scott method is used and allows an overlap of 63% with the last sample.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The SECT collects information at the enterprise level and produces estimates at the enterprise level. Since the management of electronic services and electronic commerce are generally maintained at the enterprise level.

Units that do not respond or only partially respond or that fail consistency edits and outliers are subject to mail, telephone and fax follow-up to ensure the data were obtained or corrected if needed. Also, the Internet was used to identify if certain enterprises had a web site.

Finally, the follow-ups are prioritized by taking into account the size of the enterprise, the importance of the missing variables, the kind of inconsistencies on the questionnaire and the coverage by industrial sector.

View the Questionnaire(s) and reporting guide(s) .

Error detection

At data collection, some edits are applied to each questionnaire such as rules of consistency and validity. At the time of data processing statistical and historical edits are applied. Outlier detection is done on the variable "Sales over Internet". The detection is made within two groups: public sector and private sector. A method using the distance between observations is used.


Deterministic imputation is used when answers from questions related to the question needing imputation lead to only one possible answer. Imputation using administrative data is used to impute the question referring to the number of employees. Historical imputation is used to impute some stable questions over time when the enterprise positively responded the year before.

Donor imputation is used in the remaining cases to replace missing or inconsistent values with those of the nearest respondent according to characteristics such as size, industrial classification and key variables from the questionnaire. Imputation is conducted within homogeneous groups, the initial imputation group corresponding to the stratum. If there is not at least 10 potential donors and 25% of donors in a group, or if imputation from all available donors would result in questionnaire inconsistencies, we move to a more aggregated imputation group.

Outlier enterprises are excluded from the donor pool. When imputation is done, the sales value over the Internet is adjusted by the ratio of imputed and donor's revenue.

When imputation is over, the initial edit rules are reapplied to assure the consistency of all the questionnaires going into the estimation process.


Statistics Canada's Generalized Estimation System (GES) is used. The estimation is done in two phases: the first phase sample is the initial sample and the second phase sample is the respondents. The same stratification is used at the first and the second phase by assuming no bias of non-response based on the results from the previous survey. There are three types of estimates produced: in the case of percentage variables (P), a ratio are used to derive an estimate, in the case of categorical variables (C), again a ratio are used and in the case of numerical variables (Y), the usual estimator of the total are used.

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 for a specific industry or variable may be suppressed (along with that of a second industry or variable) if the number of enterprises in the population is too low.

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting SECT 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 interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors.

Coverage error results from inadequate representation of the intended population. This error may occur during selection of the survey population, or during data collection and processing. In order to avoid such errors, a number of sources describing the population of the industry are used and compared.

Response error may be due to many factors, including faulty design of the questionnaire, interviewers' or respondents' misinterpretation of questions, or respondents' faulty reporting. Frequent changes in company personnel may also lead to response error. Several features are in place to help respondents complete the questionnaire, including logic and consistency checks, and a glossary of terms and concepts. Responses are compared from year to year and any significant deviations are queried by analysts to ensure their accuracy. However, even with these checks, the quality of data depends on the respondent's willingness to consult their records.

Non-response error occurs because not all respondents cooperate fully. To alleviate the impact on the survey, respondents are usually asked to provide key variables and the others are estimated.

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. The sampling error is measured by a quantity known as the standard deviation. The latter indicates the expected variability of the estimate that would be produced if we sampled repeatedly. The actual value of the standard deviation is unknown, but it can be estimated from the sample.

When the estimates are disseminated, a scale distinguishes between the various qualities of accuracy. It combines the effect of sampling (using the CV) and the imputation rate (each imputed value adds to the uncertainty of the results). The scale is presented in the table below.


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