National Graduates Survey (NGS)

Detailed information for 2020 postsecondary graduates (cycle collected in 2023)




Every 5 years

Record number:


The National Graduates Survey (NGS), Class of 2020 focused on the education and labour market experiences of people who graduated from a Canadian public postsecondary educational institution in 2020. The questions focused on academic path; funding for postsecondary education, including government-sponsored student loans; and the transition into the labour market. The survey also explored the impact of the COVID-19 pandemic on the education and employment of graduates.

Data release - March 22, 2024


Data from this survey will be used to better understand the experiences and outcomes of graduates and to improve government programs. The survey is designed to collect details on topics such as
i) the extent to which graduates of postsecondary programs have been successful in obtaining employment since graduation
ii) the relationship between the graduates' program of study and their subsequent employment
iii) the type of employment obtained and the qualification requirements
iv) sources of funding for postsecondary education
v) government-sponsored student loans and other sources of student debt
vi) the impact of the COVID-19 pandemic on the education and employment of graduates.

The data will be used by Statistics Canada, Employment and Social Development Canada, provincial and territorial ministries of education, researchers, and other interested organizations to examine various topics, such as educational pathways, postsecondary funding, mobility, school-to-work transitions, labour market outcomes and the pursuit of further postsecondary studies.

A major redesign was done for the 2018 collection cycle (2015 graduates). The objectives of the redesign were to revise the sampling method, adopt a new sampling frame, modernize the content and revise the target population. Consultations were conducted with federal, provincial and territorial sharing partners and university researchers. As a result of the redesign, care should be taken when comparing data from previous cycles with data published for the 2018 cycle and beyond.

The 2023 collection cycle (2020 graduates) was built on the content used for the 2018 collection cycle (2015 graduates). It also included new content on online learning and the impact of the COVID-19 pandemic on the education and employment of graduates.

Reference period: Calendar year.

Collection period: April through August of the third year after the reference period.


  • Education, training and learning
  • Employment and unemployment
  • Fields of study
  • Labour
  • Outcomes of education

Data sources and methodology

Target population

The NGS target population for the 2023 cycle consisted of all graduates from a recognized public postsecondary Canadian institution who graduated from their program sometime in 2020 and were living in Canada at the time of the survey.

- Graduates of university programs that lead to a bachelor's, master's or doctoral degree, or that lead to specialized certificates or diplomas
- Graduates of postsecondary programs (that is, programs that normally require a secondary school diploma or its equivalent for admission) in colleges of applied arts and technology, Collèges d'enseignement général et professionnel (technical CEGEP in Quebec), community colleges, technical schools or similar institutions
- Graduates of skilled trades (that is, pre-employment programs that are normally three months or more in duration) from postsecondary institutions (many community colleges and technical institutes offer certificates or diplomas at the trade level)

- Graduates from private postsecondary institutions (for example, computer training and commercial secretarial schools)
- Graduates who completed "continuing education" courses at universities and colleges (unless they led to a degree or diploma)
- Graduates in apprenticeship programs
- Graduates in a postsecondary program of less than three months' duration

Instrument design

The content for the NGS was developed in collaboration with specialists from Statistics Canada, other departments and/or academia. The questions were designed to be answered directly by the respondent using an online electronic questionnaire (EQ) or computer-assisted telephone interviewing (CATI) to track initial non-response. The questions in the EQ were designed to logically follow conditions, such as previous responses and non-responses, according to the modules.

The content of the NGS is composed of groups of questions (called modules), which relate to a particular theme of the educational path and experiences in the labour market. Several new questions were introduced in different modules for the 2020 graduate cycle to meet new data needs, including a series of questions related to online learning, quick training of less than three months (microcredential), work-integrated learning and COVID-19.

To reduce response burden, the EQ excluded the module on reported personal income and benefits paid to graduates in 2019 to 2022 under the Canada Emergency Student Benefit and the Canada Emergency Response Benefit. These data were taken from administrative files from the Canada Revenue Agency.


The NGS is a cross-sectional sample survey with a stratified simple random sample design. Two variables were used for stratification: (i) province or territory of the institution and (ii) level of study (college, bachelor's, master's or doctorate). The sample selection of graduates within strata was done without replacement using a systematic method.

Data sources

Data collection for this reference period: 2023-04-03 to 2023-08-13

Responding to this survey is voluntary.

Data are collected directly from survey respondents and extracted from administrative files.

Survey data collection
- Data collection for the NGS started on April 3, 2023, and ended on August 13, 2023.
- Invitation letters were sent by mail on April 3, 2023.
- Interviewer-administered CATI follow-up data collection began on May 3, 2023.
- Over the course of the four-month period of data collection, three reminder letters, nine reminder emails and three SMS (Short Message Service) were also sent.

Administrative data
A tax data use strategy was implemented to reduce respondent burden and survey costs. This strategy consisted of using tax data rather than survey data. Tax data can also be used to reconcile survey data.

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

Error detection

The EQ included automated logical flows to control the questions that were asked to respondents and a limited number of soft edits that checked for unusual values or inconsistencies.

The collected data were processed using the Social Survey Processing Environment that was developed at Statistics Canada. This included i) the coding of open-ended text entries to the appropriate standard classification system, ii) a series of detailed consistency edits to check for logical inconsistencies or extreme values, and iii) the creation of an extensive set of derived variables.


No imputation was done.


For estimates produced from the survey data to be representative of the target population, and not just of the sample itself, users must incorporate the survey weights into their calculations. A survey weight is given to each person included in the final sample, that is, the sample of people who responded to the survey questions. This weight corresponds to the number of people represented by the respondent for the target population. If the frame used was perfect (covering exactly the population of interest) and all selected units were traced, contacted and completed the survey, then the design weight assigned to each unit, given by the inverse of the probability of selection of each unit in the sample, would represent the number of graduates in the target population accurately and exactly. In this situation, using this weight would yield unbiased estimates. However, this is not the case when surveys are faced with non-response and imperfect frames. Various weight adjustment techniques are available to compensate for these different issues.

It was observed that non-response did not occur randomly or uniformly within the population because different response rates were obtained for different geographical regions or levels of certification. The chosen technique for the NGS Class of 2020 was based on response homogeneous groups (RHGs). RHGs are developed with the premise of identifying factors that influence the likelihood of responding, then grouping together the sample units with similar profiles as defined by these factors. Then, the weights of the non-responding units in an RHG are redistributed among the responding units in the same RHG. The factors used to define these RHGs can come from data from the frame or from the collection process itself.

Final weights were created by calibrating the weights that were obtained after the non-response adjustment to the survey frame within post-stratification groups.

Quality evaluation

Quality assurance measures were implemented at every collection and processing step. Measures included recruiting qualified interviewers, training interviewers for specific survey concepts and procedures, having procedures in place to ensure that coding errors were minimized, and editing quality checks to verify the processing logic. Data were verified to ensure internal consistency and were compared with other sources when available.

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 is permitted under 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.

The survey outputs are disseminated through aggregate-level estimate tables that incorporate suitable rules to suppress estimates that do not meet minimum thresholds. The survey outputs also include a share file and a public-use microdata file, both of which incorporate suitable modifications to individual-level records and variables to fully protect the confidentiality of all individual-level respondent data.

Revisions and seasonal adjustment

This methodology does not apply to this survey.

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: sampling and non-sampling. Frame imperfection and non-response are important sources of non-sampling error.

The sample was designed to yield estimates of a minimal proportion of 5.5% with a maximum coefficient of variation of 11% within any stratum. There were two exceptions to this rule: all doctorate graduates and all graduates from the Yukon and the Northwest Territories (see section on coverage error for discussion of Nunavut) were selected for the sample.

Response rate
The response rate for the NGS Class of 2020 was 46%.

Non-sampling error
There are many sources of non-sampling errors that are not related to sampling but may occur at almost any phase of a survey operation. Interviewers may misunderstand survey instructions, graduates may make a mistake in answering the questions, responses may be recorded in the questionnaire incorrectly, or errors may be made in the processing or tabulation of the data. Quality assurance measures were implemented at each phase of the data collection process to monitor the quality of the data. These measures included precise interviewer training with respect to the survey procedures and questionnaire, extensive editing of respondent data, and statistical quality assurance for all automated and manual coding operations.

Coverage error
The NGS Class of 2020 had some minor undercoverage for graduates of colleges in one province and one territory because the data required to build the frame could not be obtained from two institutions. Graduates from those institutions were therefore not included on the frame and, as a result, could not be selected for the sample. No information could be obtained for Nunavut graduates. As such, the NGS Class of 2020 does not contain information about Nunavut graduates. No adjustment was made at the weighting stage to compensate for this undercoverage.

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