Survey of Digital Technology and Internet Use (SDTIU)

Detailed information for 2019





Record number:


The purpose of the 2019 Survey of Digital Technology and Internet Use (SDTIU) is to measure the impact of digital technologies, including the Internet and specific information and communication technologies (ICTs), on the operations of Canadian enterprises.

Data release - November 23, 2020


The 2019 Survey of Digital Technology and Internet Use (SDTIU) is designed to measure the impact of digital technologies on the operations of Canadian enterprises.

The information gathered helps to better understand how enterprises use the Internet, including their online presence, involvement in e-commerce, use of specific information and communication technologies (ICTs) and interaction with federal government online services. The survey also examines skills and employment in ICT-related jobs. The data from this survey are used by government departments to develop policies and programs that help improve Canada's innovation system and strengthen the overall economy.

The 2019 iteration of the SDTIU is sponsored by Innovation, Science and Economic Development Canada (ISED). Numerous other government departments also provided input during the questionnaire content development phase.

The results from this survey are also monitored by international organizations such as the Organization for Economic Co-operation and Development (OECD) for benchmarking purposes and to study the development and the influence of the digital economy.

Reference period: The 12-month calendar year

Collection period: November of the reference year through March of the next year


  • Business and government internet use
  • Information and communications technology

Data sources and methodology

Target population

The target population was derived from Statistics Canada's Business Register (BR). The BR is an information database on the Canadian business population and serves as a frame for all Statistics Canada business surveys. It is a structured list of businesses engaged in the production of goods and services in Canada.

This survey covers enterprises operating in Canada in almost all industrial sectors. The industries in the target population are based on the 2017 North American Industry Classification System (NAICS). Sectors 21, 22, 23, 31, 32, 33, 41, 44, 45, 48, 49, 51, 52, 53, 54, 55, 56, 61, 62, 71, 72 and 81 were included. The following entities were excluded to arrive at the target population:

- Government entities;
- Enterprises with less than 5 employees;
- Agriculture, forestry, fishing and hunting (11), specialty trade contractors (238), head offices (551114), private households (814), and public administration (91);
- Enterprises from sectors 23, 48, 49, 53, 54, 56, 61, 62, 71, 72 and 81 with a revenue less than $100K;
- Enterprises from sectors 21, 22, 31, 32, 33, 41, 44, 45, 51, 52 and 55 with a revenue less than $250K.

Instrument design

The survey data was collected using an electronic questionnaire.

The questionnaire was significantly redesigned from the previous version in 2013 to meet the policy needs of the sponsoring partner.

Cognitive testing of the questionnaire content was carried out in two phases in conjunction with the Questionnaire Design Resource Centre based at Statistics Canada in both official languages. The first round of testing concentrated on validating respondents' understanding of concepts, questions, terminology, the appropriateness of response categories and the availability of requested information. During the second round, the content was tested through one-on-one interviews with 18 potential respondents that took place in Gatineau, Montreal and Toronto. The resulting comments and analysis of these interviews led to further revision of the questionnaire to make the questions more relevant to respondents and easier to answer.


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

The survey frame was constructed by selecting all enterprises from the BR that met the definition of the target population. The number of enterprises within the target population is currently 339,207 enterprises.

Enterprises are stratified by industry and size categories. The size category is based on the number of employees of the enterprise. Three size categories were built: small (5-49 employees), medium (50-249 employees) and large (250 or more employees).

The size of the survey sample was determined based on the following parameters:

- Proportional allocation was used assuming a reported proportion of 50% for each variable of interest;
- Domains were specified at the NAICS3 level for sectors: 23, 31, 32, 33, 41, 44, 45, 48, 49, 51, 52, 53, 56, 62, 71 and 72;
- Domains were specified at the NAICS4 level for sector 54;
- All other sectors had their domains specified at the NAICS2 level;
- A minimum response rate of 65% was expected.

The probability of a particular enterprise being selected in the survey sample was determined based on the proportion of the survey sample allocated to its domain, the number of enterprises in its domain in the population and its revenue.

The final sample size was 14,127 enterprises.

Data sources

Data collection for this reference period: 2019-11-28 to 2020-03-04

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected through an electronic questionnaire. Businesses are initially contacted by telephone during a pre-contact phase to identify an appropriate contact within the enterprise to respond to the survey.

Follow-up because of non-response, inconsistent or missing data is done by phone on a priority basis.

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

Error detection

Error detection is an integral part of both collection and data processing activities. Automated edits are applied to data records during collection to identify and correct reporting errors.

The processing phase of the survey was for the most part concerned with applying edits for consistency and validity to each record. Consistency edits ensure that data reported in one question does not contradict information reported in another question. Validity edits ensure that the data reported is valid (e.g., percentages do not exceed 100%).

Outlier detection checks were also conducted for key variables during data processing. Some outliers that could not be validated were replaced by imputed values.


The imputation of non-responses and erroneous records was performed using the nearest neighbour donor imputation procedure in the BANFF generalized system. This procedure uses a nearest neighbour approach to find, for each record requiring imputation, a valid record that is most similar to it and that will ensure that the imputed record does not violate any of the logical flows and input restrictions of the questionnaire.

Similar records are found by defining imputation classes which take into account other variables that are correlated with the missing or erroneous values. When a nearest neighbour cannot be found for some records in the most specific imputation classes, the definition of the imputation classes are expanded and the data are reprocessed. This imputation processing continues using a predetermined sequence until nearest neighbour donors are assigned to all records requiring imputation.


The response values for sampled units were multiplied by a final weight in order to provide an estimate for the entire population. The final weight was calculated using several factors, including the probability for a unit to be selected in the sample and an adjustment to represent the units that could not be contacted or that refused to respond. Using a statistical technique called calibration, the final set of weights was adjusted in such a way that the sample represents as closely as possible the entire population.

Sampling error was measured by the standard error (SE) for proportions and by the coefficient of variation (CV) for numerical responses. These measures represent the proportion of the estimate that comes from the variability associated with sampling. The SEs and CVs were calculated and are indicated in the data tables by quality flags.

Quality evaluation

Prior to the data release, combined survey results are analyzed for comparability. This analysis includes a detailed review of:
- individual responses (especially for the largest organizations);
- coherence with results from other surveys and studies related to digital technologies and Internet use, including previous iterations of the survey; and
- information from other external sources (e.g. annual reports, news articles).

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that 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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

Errors may occur for various reasons during the collection and processing of the data. For example, non-response is one possible source of error. Under or over-coverage of the population, differences in interpretation of questions and mistakes in recording and processing data are other possible errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire and verification of the survey data.

The data accuracy indicators used for the SDTIU are the standard error and the coefficient of variation. The standard error is a commonly used statistical measure indicating the error of an estimate associated with sampling. The coefficient of variation is the standard error expressed as a percentage of the estimate.

Data quality indicators for the survey are based on the standard error (SE) or the coefficient of variation (CV), and the imputation rates. Quality indicators indicate the following for SE: A is excellent (SE up to 2.5%); B is very good (SE 2.5% up to 5.0%); C is good (SE 5.0% up to 7.5%); D is acceptable (SE 7.5% up to 10.0%); E is use with caution (SE 10.0% up to 12.5%); and F is too unreliable to be published (SE 12.5% or higher). Quality indicators indicate the following for CV: A is excellent (CV up to 5%); B is very good (CV 5% up to 10%); C is good (CV 10% up to 15%); D is acceptable (CV 15% up to 20%); E is use with caution (CV 20% up to 25%); and F is too unreliable to be published (SE 25% or higher).

Response rates:
The response rate at the estimation phase was 77%.

Non-response bias:
In addition to increasing variance, non-response can result in biased estimates if non-respondents have different characteristics from respondents. Non-response is addressed through survey design, respondent follow-up, reweighting, and verification and validation of microdata. Other indicators of quality such as the response rate are also provided.

Coverage error:
The Business Register was used as the frame.

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