Crowdsourcing - Cannabis

Detailed information for third quarter 2019

Status:

Active

Frequency:

Quarterly

Record number:

5263

The data collected are being used in the Canadian system of national accounts to support the creation and validation of measures relating to the importance of the cannabis sector in the Canadian economy.

Data release - October 9, 2019

Description

This crowdsourcing platform provides self-reported information submitted by website visitors on the subject of previous purchases of dried cannabis.

The respondent provides the price, quantity, quality, city and purpose of consumption for their last cannabis transaction. Additional information is sought on how often he/she uses cannabis, and the average monthly quantity of consumption.

Data could be utilized by a variety of public and private sector institutions, including federal, provincial and territorial governments, industry associations and research organizations, along with self-respondents.

Collection period: Collection of the data is ongoing with respondents able to report at any time throughout the year, with weekly updates on overall data submissions.

Subjects

  • Economic accounts
  • Health

Data sources and methodology

Target population

The target population consists of anyone in Canada that have consumed cannabis for either medical or non-medical purposes within the last year.

Instrument design

The crowdsourcing questionnaire collects data on cannabis transactions within Canada with the respondent self-reporting the price, quantity, quality, city and purpose of consumption for their last dried cannabis transaction.

Sampling

Sample-selection is not applicable as responding to this crowdsourcing tool is voluntary for anyone within Canada.

Data sources

Data collection for this reference period: 2019-07-01 to 2019-09-30

Responding to this survey is voluntary.

Data are collected directly from survey respondents through the online crowdsourcing application.

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

Error detection

An outlier detection process is performed on the cannabis crowdsourcing data collected by Statistics Canada' Crowdsourcing cannabis application in order to identify implausible submissions. A robust statistical method called the interquartile method is used, which consists of grouping observations into homogeneous subsets, and flagging as potential outlier's observations that are further away from the group median than set multiples of the distances between the median and the first and third quartiles.

For the cannabis crowdsourcing data, the submissions are grouped by Census Metropolitan Areas (CMA) and by classes of cannabis quantity bought. To ensure that the number of submissions in the groups is large enough to evaluate the quartiles properly, groups with too few submissions are collapsed into larger groups. Furthermore, submissions for which it is strongly suspected that a unit price was reported rather than the total price for the transaction, this is also identified and corrected.

Outlier detection program updates are ongoing to increase the accuracy of the data, affecting estimates that could potentially be outliers and no longer be outliers, or vice versa.

Imputation

This methodology type does not apply to this statistical program.

Estimation

This methodology type does not apply to this statistical program.

Quality evaluation

While the crowdsourced data utilizes an outlier detection process, caution must be exercised when interpreting these data because the census is voluntary and therefore subject to statistical bias.

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.

Revisions and seasonal adjustment

No current revision policy occurs for the data within the crowdsourcing application. Seasonal adjustment is also not applied to this dataset.

Data accuracy

RESPONSE RATES:
This methodology type does not apply to this statistical program.

NON-SAMPLING ERROR:
This methodology type does not apply to this statistical program.

NON-RESPONSE BIAS:
This methodology type does not apply to this statistical program.

COVERAGE ERROR
Coverage errors arise when there are differences between the target population and the observed population.

OTHER NON-SAMPLING ERRORS:
This methodology type does not apply to this statistical program.

Date modified: