Survey on Research Activities and Commercialization of Intellectual Property in Higher Education (SRACIPHE)

Detailed information for 2023

The survey on Research Activities and Commercialization of Intellectual Property in Higher Education collects information on how research conducted and produced in the higher education sector is transferred to the private sector.

Data release - September 29, 2025

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

The objective of the Survey on Research Activities and Commercialization of Intellectual Property in Higher Education is to ensure the availability of relevant information in order to understand science and technology (S&T) related activities in the higher education sector, to support the development of S&T policy and to understand how science and technology activities within Canada's higher education sector contributes to economic growth, business development and employment growth.

The survey topics cover the research and technology development activities conducted, partnerships and collaborations on S&T activities, intellectual property outputs, protection, management and commercialization, as well as commercial start-ups and spin-offs from the technologies developed in the higher education sector. The higher education sector covers colleges, CEGEPs, universities and research hospitals in Canada.

The data will be used to better understand the outcomes and economic benefits of public sector research. Data users include the federal and provincial governments, institutional administrators and researchers.

In 2022, the Canadian government provided funding to Innovation, Science and Economic Development Canada (ISED) to launch a survey, in collaboration with Statistics Canada, to evaluate government investments in science and research and how knowledge created at postsecondary institutions generates commercial outcomes. Building on previous work, such as the Survey of Intellectual Property Commercialization in the Higher Education Sector (SIPCHES), which was last conducted for the 2009 reference year, this new survey aims to develop coherent indicators for science and technology activities in the Canadian postsecondary sector. It is designed to evolve, incorporating new insights to reflect the unique contributions of each institution type over time.

Reference period: The 12-month fiscal period for which the final day occurs on or between April 1st of the reference year and March 31st of the following year.

Collection period: September through November

Subjects

• Education indicators
• Education, training and learning
• Innovation
• Research and development
• Science and technology

Data sources and methodology

Target population

The SRACIPHE is a census that includes all universities and colleges that do not offer exclusively theological degrees, as well as hospitals or specialized health institutions that have research and development activities.

Institutions that did not perform over $1 million in research and development activities in any of the last 3 years are excluded.

Instrument design

The survey data are collected using an electronic questionnaire (EQ).

The SRACIPHE questionnaire was developed in collaboration with Innovation, Science, and Economic Development Canada (ISED).

It builds on previous work conducted both in Canada and internationally, including the 2009 Survey of Intellectual Property Commercialization in the Higher Education Sector (SIPCHES), the System on Science, Technology, and Innovation (SICTI) of the Spanish System of Science, Technology, and Innovation (SECTI), the Higher Education - Business and Community Interaction survey by UK Research and Innovation (UKRI), and the Association of University Technology Managers (AUTM) Licensing Activity Survey.

Additionally, SRACIPHE incorporates the results of consultations held with representatives from universities, colleges, research hospitals, and field experts to identify further data gaps related to knowledge mobilization and to develop indicators to address these gaps.

EQs are the principal mode of collection, and these were tested with institution respondents in English and French to confirm respondents' understanding of terminology, concepts and definitions, as well as their ability to provide the requested data and to navigate the EQ applications. This round of testing concentrated on validating respondents' understanding of concepts, questions, terminology, the appropriateness of response categories and the availability of requested information.

Sampling

This survey is a census with a longitudinal design.

The frame is formed of active units from the Business Register (BR) in the categories of interest. For universities, all institutions recognized or authorized by a province to grant university degrees were included, except for those providing solely theology degrees. Similarly, for colleges, all institutions recognized or authorized by a province to grant DECs, diplomas or certificate degrees were included, unless they provided solely theology degrees. Hospitals were identified using NAICS 622111, 622112, 622310, and 622210 from the BR. Clinics and other practices with fewer than 50 employees were excluded. Institution that did not perform over $1 million in research and development activities in any of the last 3 year are excluded.

All units constituting the frame previously described will be collected making this a census. Although the frame definition is cross-sectional by design it is believed that it will remain fairly constant through time making this resembling the first wave of a longitudinal survey.

Sampling unit
Enterprises from the Business Register, belonging to one of the three types of institutions as defined in the frame.

Data sources

Data collection for this reference period: 2023-04-01 to 2024-03-31

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected through an EQ. Institutions are initially contacted by telephone during a pre-contact phase to identify an appropriate contact within the institutions 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 data processing activities. Prior to imputation, a series of edits are applied to the collected data to identify errors and inconsistencies. Errors and inconsistencies in the data are reviewed and resolved by referring to data for similar units in the survey and information from external sources. If a record cannot be resolved, it is flagged for imputation. Finally, edit rules are incorporated into the imputation system to detect and resolve any remaining errors, as well as to ensure that the imputed data are consistent.

Imputation

Imputation is performed to treat non-response items.

The imputation of non-response items is performed using the nearest neighbour donor imputation procedure in the generalized system BANFF. This procedure finds, for each record requiring item imputation, its most similar valid record thereby allowing the imputed recipient record to pass the specified imputation edits and post edits rules.

These similar records are found by taking into account other variables that are correlated with the missing values via the customized imputation classes and matching variables (if required) for each item (variable) to be imputed. If nearest neighbour donors are not found for all recipients, then it is necessary to be less restrictive by extending the imputation classes and reprocessing the data. This imputation process continues using a predetermined sequence until nearest neighbour donors are assigned to all records requiring imputation or until no nearest neighbour donors are available. During imputation, edits and post edits are applied to ensure that the resulting record does not violate any of the specified edits.

Estimation

Estimation is a process by which Statistics Canada obtains values for the population of interest so that it can draw conclusions about that population based on information gathered from only a sample of the population. For this survey, the sample used for estimation comes from a single-phase sampling process.

An initial sampling weight (the design weight) is calculated for each unit of the survey and is simply the inverse of the probability of selection. The weight calculated for each sampling unit indicates how many other units it represents.

However, since some of the selected units did not answer the survey, reweighting is performed on the responding units so that their final weights still represent the whole target population. The response mechanism can be considered as a second-phase of the sampling process.

After the reweighting is performed, a calibration process is performed so that the weighted totals per calibration groups equal the population totals.

Estimation of proportions is done using the calibrated weights to calculate the population totals in the domains of interest.

Quality evaluation

Estimates were reviewed to ensure that the findings are logical and quality checks were carried out to ensure that estimates are consistent. Atypical results were flagged for investigation and were corrected as necessary.

Disclosure control

The metadata will be provided upon release.

Revisions and seasonal adjustment

The metadata will be provided upon release.

Data accuracy

There are two types of errors which can impact the data: sampling errors and non-sampling errors.

Estimates are subject to sampling error. This error can be expressed as a standard error. For example, the proportion of businesses in the target population that would respond YES to a given question is estimated to be 50%, with a standard error of 4%. In repeated sampling, the estimate would be expected to fall between 42.16% and 57.84%, nineteen times out of twenty (50% +/- 7.84%). The following rules based on the standard error (SE) are used to assign a measure of quality to all of the estimates of percentages.

A = Excellent (0.00% to less than 2.50%)
B = Very good (2.50% to less than 5.00%)
C = Good (5.00% to less than 7.50%)
D = Acceptable (7.50% to less than 10.00%)
E = Use with caution (10.00% to less than 15.00%)
F = Too unreliable to be published (Greater than or equal to 15%, data are suppressed)

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