Survey of Innovation

Status:
Inactive
Frequency:
Occasional
Record number:
4218

The information collected by this survey provides information on innovation and innovation activities of Canadian businesses and their characteristics.

Detailed information for 2002-2005

Data release - June 2, 2006

Description

The survey is part of an on-going program to measure innovation in Canada. To meet this objective the survey collects information on new and significantly improved products and processes introduced during a three year time period. The information collected by this survey provides information innovation and innovation activities of Canadian businesses and their characteristics. Some topics can include innovation activities, sources of information, problems and obstacles, impact of innovation, cooperative and collaborative arrangements for innovation, business success factors, intellectual property protection, and use of government support programs. The survey is conducted every 3-4 years, depending on need, and covers a 3-year reference period. Industries surveyed may vary from survey to survey. Coverage is largely determined by client sponsorship.

Estimates produced from the survey are used by:
- firms for market analysis;
- trade associations to study performance and other characteristics of their industries;
- government to develop national and regional economic policies.

Previous innovation surveys at Statistics Canada have included the 1993 Survey of Innovation and Advanced Technology which surveyed manufacturing firms; the Survey of Innovation, 1996 which surveyed the communications, financial services and technical business services industries; the 1999 Survey of Innovation, Advanced Technologies and practices in the Construction and Related Industries Survey; the Survey of Innovation 1999 which surveyed manufacturing and selected natural resource industries for the reference period 1997-1999; the Survey of Innovation 2003 surveyed information and communication technology industries, selected professional, scientific and technical services industries, selected natural resource support service industries, and selected transportation industries. The Survey of Innovation 2005 surveys manufacturing and logging industries for the reference period 2002-2004.

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.

Collection period:
end of fall of reference period

Subjects

  • Innovation
  • Science and technology

Data sources and methodology

Target population

The target population was all plants (establishments) in the manufacturing sector (NAICS 31-33) and logging (NAICS 1133) (North American Industry Classification System, Statistics Canada, 2002) with at least 20 employees and least 250,000 revenues.

Instrument design

The questionnaire was designed by the Science, Innovation and Electronic Information Division of Statistics Canada in collaboration with Industry Canada; Natural Resources Canada; the Institut de la Statistique du Québec; the Ministère du Développement économique, de l'Innovation et de l'Exportation du Québec; Industrie Canada, région du Québec; Développement économique Canada, région du Québec; the Conseil national de recherches Canada, région du Québec; the Ministère des Finances du Québec; the Conseil de la science et de la technologie du Québec; the Ontario Ministry of Economic Development and Trade; and university researchers. The questionnaire was tested by an interview of a small sample of individual plants to ensure that the questions were well understood. Feedback from these firms was incorporated into the questionnaire.

Sampling

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

Frame Description: The sample was drawn from Statistics Canada's Business Register (June 2005 version) during July 2005 from a population of 17,726 manufacturing and 762 logging establishments.

Sample unit: Establishment

Stratification:

Manufacturing industries

A core set of 23 manufacturing industries were identified in consultation with survey stakeholders (Table 1) in the additional documentation link below. These covered the whole of the manufacturing sector allowing for the production of a manufacturing aggregate estimate.

In consultation with survey stakeholders, industries and industry breakouts were added to meet analytic interests. The result was a total of 35 manufacturing industries for which estimates would be produced (Table 2) in the additional documentation link below.

There is an internationally-agreed-to definition of the manufacturing information and communication technology (ICT) sector (Table 3) in the additional documentation link below. All manufacturing ICT industries are included in the list of 35 manufacturing industries allowing for the production of estimates for each component and an aggregate estimate for ICT manufacturing.

Logging industry

Logging (NAICS2002 1133) was sampled.

Sampling strategy

In order to minimize response burden, only establishments satisfying the following criteria were sampled:
- at least $250,000 in revenues; and
- at least 20 employees.

For the manufacturing industries, a stratified random sample was selected in each stratum defined by industry, province (or groups of provinces/territories in the case of the western provinces and the territories) and establishment size. Three size classes based on number of employees per establishment were defined: 20-49 (small), 50-249 (medium) and 250+ (large). An initial sample was drawn to assure the production of estimates with an expected standard error no greater than 5% for percent estimates at the national level for all 35 manufacturing industries (Table 2).

Subsequently, this initial sample was augmented to satisfy the following supplementary criteria:
- A census for Quebec;
- Attainment of percent estimates with an expected standard error no greater than 5% for the core 23 manufacturing industries (Table 1) in Ontario, PEI, New Brunswick, Nova Scotia and Newfoundland;
- Attainment of percent estimates with an expected standard error no greater than 10% for the core 23 manufacturing industries (Table 1) in the combined western provinces and territories (Manitoba, Saskatchewan, Alberta, British Columbia, Yukon, Northwest Territories, Nunavut).

For logging, the sample was drawn at the national level stratified by size with a targeted standard error of 5%. The same three size classes that had been used for manufacturing were used for logging: small, 20-49 employees; medium, 50-249 employees; and large, 250+ employees.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data was collected through respondent completed questionnaires in paper format (mail or fax). All establishments were "pre-contacted" to determine the name and correct mailing address for the respondent, the Chief Executive Officer (CEO) or senior manager at the location. Questionnaires were mailed out with mail, telephone and fax follow ups carried out for to elicit a response from non-respondents. In some cases, respondents completed the questionnaire over the phone with responses entered on a paper questionnaire by the interviewer.

Validity and flow edits were built into the data capture system and were applied during data collection and data entry. Validity edits ensured that responses to particular questions fell within a limited range of possible values.

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

Error detection

All returned questionnaires were reviewed and subjected to flow edits prior to data capture. Validity and flow edits were built into the data capture system and were applied during data entry. Validity edits ensured that responses to particular questions fell within a limited range of possible values. Post collection consistency edits were applied to completed questionnaires.

Imputation

Imputation was employed for missing responses to non-mandatory questions. Questions 9 and 17 were identified as mandatory questions as they were used to identify if an establishment was innovative. The Generalized Edit and Imputation System (GEIS) software was used to select donors.

There are several cases where the relevance of a subsequent set of questions relies on a response to a preceding question. The ability to proceed along a path of questions was reliant on the nature of the response and the subsequent responses are influenced by the firm behaviour indicated by the response to the preceding question. Block imputation (one donor) was used for these correlated questions as a means to avoid edit failures and to ensure logically consistent responses.

Estimation

The response rate for the survey was calculated as the total number of completed questionnaires as a percentage of the total active, in-scope survey sample. The overall response rate for the survey was 71.9%, for a total of 6,143 completed questionnaires.

Given the low rate of non-response to the survey, it was decided that it would be reasonable to assume that the characteristics of the non-response population were the same as the respondent population. Accordingly, it was decided that the contribution of non-response to the estimates was to be accounted for by adjusting the sample weights of the respondent population.

Estimates based upon the responses to the survey questions are population estimates; that is, they represent the percentage of businesses in the population that exhibit a particular characteristic. The population estimates are generated through the accumulation of the product of the response variable and the sample weight for the defined tabulation cells.

Sampling error

As the sample drawn for this survey was only one of many possible samples that could have been drawn, a sampling error was attributed to it. Standard errors were used to provide a guide as to the reliability of the results where estimates are expressed as a percentage. The coefficient of variation was used where estimates are an average of responses. The imputation rate was evaluated and was considered in an evaluation of the reliability of estimates. For questions used to identify whether an establishment was innovative (Questions 9 and 17) there was no imputation. For all remaining questions imputation rates were combined with the standard error or the coefficient of variation, as appropriate, to indicate the reliability of the data. The System for Estimating Variance due to Non-response and Imputation program (SEVANI) was used to complete the calculations. The reliability of the data was reported using the following symbol convention provided in the attached document.

Quality evaluation

The quality of the data has been checked against quality standards at Statistics Canada, namely, data relevance, accuracy, timeliness, accessibility, interpretability and coherence.

Data relevance was insured by the active collaboration in the questionnaire design between the Science, Innovation and Electronic Information Division (SIEID) of Statistics Canada, Industry Canada, the Institut de la statistique du Québec, the Government of Ontario and Natural Resources Canada.

Data accuracy was insured by conducting cognitive interviews in both official languages with potential respondents. Their comments were integrated into the final design and wording of the questionnaire.

From the close of data collection to the first data release three months elapsed, thus insuring data timeliness.

Accessibility to data users is made through a series of venues, including, the sharing of data with provincial statistical agencies, the Facilitated Access program, working papers available on Statistics Canada's web site, CANSIM, and descriptive tables provided to stakeholders.

To help users interpret the data, the definitions of the underlying concepts, classification, data collection methodology, as well as indicators of data accuracy are made available.

Standard Statistics Canada symbols have been used in all data tables thus insuring data coherence.

Disclosure control

Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. 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.

Estimates that were too unreliable to be published (code F) were suppressed.

The reliability of the data was reported using the following convention for quality indicator interpretation. This convention combines the effect of sampling (since we did not do a census) and the imputation rate.

Data accuracy

Data accuracy was insured by conducting cognitive interviews in both official languages with potential respondents. Their comments were integrated into the final design and wording of the questionnaire.

As the sample drawn for this survey was only one of many possible samples that could have been drawn, a sampling error was attributed to it. Standard errors were used to provide a guide as to the reliability of the results where estimates are expressed as a percentage. The coefficient of variation was used where estimates are an average of responses. The reliability of the data was reported using the following symbol convention (see additional documentation).