Survey of Advanced Technology (SAT)
Detailed information for 2007
Status:
Inactive
Frequency:
Occasional
Record number:
4223
The survey is part of an ongoing program to develop indicators of innovation. Survey results will contribute to a better understanding of innovation activities linked to the modification and creation of technology. The information compiled from the survey can be used to improve existing economic policies and technology strategies or develop new ones.
Data release - June 26, 2008
Description
The objective of this survey is to provide statistics on the technological capabilities of business units in the manufacturing industries. Statistics Canada will create a data base combining individual survey responses with existing Statistics Canada data records. These data will be released in aggregate form only so as to maintain the confidentiality of individual business records. The survey will provide the basis for informed decisions on policies and programs concerning technology adoption in the manufacturing and logging industries. The survey is conducted occasionally, 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.
The data obtained from the survey will be used by both the public and private sectors:
. Governments can use this information in decision-making and the development of national and regional economic policies.
. Firms can use the information for market analysis.
. Industry associations can use the information to study various characteristics of their industry.
. Academic researchers can use the data to perform research to determine underlying principles measuring the modes of use and the business impacts of use of these new technologies.
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.
Subjects
- Innovation
- Manufacturing
- Science and technology
Data sources and methodology
Target population
The target population are all business units (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 at least $250,000 in revenues.
Instrument design
The Survey of Advanced Technology 2007 was designed by Statistics Canada and various partners including Industry Canada, Agriculture and Agri-Food Canada, Natural Resources Canada, the Government of Saskatchewan, Ministry of Industry and Resources; the Government of Alberta, Ministry of Alberta Employment, Immigration and Industry; Atlantic Canada Opportunities Agency; the Institut de la statistique du Québec, as well as the following Québec ministries: the Ministère du Développement économique, de l'Innovation et de l'Exportation; Industrie Canada; Développement économique Canada; the Conseil national de recherches Canada; the Ministère des Finances du Québec; the Conseil de la science et de la technologie du Québec; as well as several Ontario ministries including: the Ministry of Research and Innovation; the Ministry of Finance; the Ministry of Economic Development & Trade; the Ministry of Small Business & Entrepreneurship; the Ministry of Agriculture, Food and Rural Affairs; and the Ministry of Natural Resources with input from researchers, both international and from Canadian universities, and with industry experts offering a range of skills and interests. Cognitive testing of the questionnaire was carried out with prospective respondents, whose comments contributed to the final version.
Sampling
This is a sample survey with a cross-sectional design.
Frame Description
The sample was drawn from Statistics Canada's Business Register (June 2007 version) during July 2007 from a population of 16,590 manufacturing and 622 logging establishments.
Sample unit: Establishment
Stratification: Manufacturing industries
A core set of 23 manufacturing industries were identified in consultation with survey stakeholders (Table 1). 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 49 manufacturing industries for which estimates would be produced (Table 2).
Logging industry
A sample was drawn for the logging industry (NAICS 1133) considering an anticipated 70% response rate stratified by province and establishment size (20-99, 100-249, 250+ employees) with expectation of reliable estimates in each province or territory. An exception to this is for Saskatchewan where a census of logging industries was drawn.
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 base sample was drawn with the anticipation of a 70% response rate and to expect reliable estimates (standard error no greater than 5%) for Canada with the sample stratified by industry, province, establishment size (20-99, 100-249, 250+ employees) and enterprise size (20-99, 100-499, 500+) employees.
This sample was augmented in several provinces to increase industry detail and/or sampling intensity to allow for the expectation of reliable provincial estimates or to carry out a census. A census of manufacturing industries was carried out in each of the four Atlantic Provinces and Saskatchewan. In Quebec, Ontario and Alberta the sample was augmented to allow the expectation of reliable estimates for manufacturing in those provinces. In Ontario, the sample was augmented to include a census of the Pharmaceutical and medicine manufacturing (NAICS 3254) and a sample of motor vehicle parts manufacturing (NAICS 3363). In Quebec, Ontario, Manitoba, British Columbia (there are no ICT manufacturing industries in the territories) the sample was augmented to include a census of ICT manufacturing industries. (Table 3).
In order to provide insight into the forest sector, manufacturing industries associated with the forest sector were sampled with greater industry detail and with greater intensity with the expectation of reliable estimates at the provincial/territorial level. These include the industries presented in Table 4.
Data sources
Data collection for this reference period: 2007-09-24 to 2008-03-31
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 plant manager 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.
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. Question 1 (first column) was identified as the mandatory question as this was used to identify if an establishment was an advanced technology user. The Banff 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 72.8% (79.9% for logging and 72.5% for manufacturing) for a total of 6,733 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 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, Agriculture and Agri-Food Canada, Natural Resources Canada, the Government of Saskatchewan, Ministry of Industry and Resources; Agriculture and Agri-Food Canada; Atlantic Canada Opportunities Agency and the Institut de la statistique du Québec.
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 that 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.
Revisions and seasonal adjustment
This methodology does not apply to this survey.
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).
- Date modified: