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STS493 Stéphane D. et al.
                     The best example of an effective new communication strategy is the one
                  used for the 2016 Census of Population. Based on results from the previous
                  Census  of Population, the population  of  Canada could be  divided  into  five
                  different  groups,  each  with  its  own  unique  communication  strategy.  The
                  groupings were based on the likelihood of a fast response to the census. One
                  group, the easiest to reach, got relatively light communication. People who are
                  more difficult to reach, on the other hand, received communications at various
                  stages of collection. This segmentation strategy is an important reason why the
                  2016 Census of Population was considered the best ever in Canada, with the
                  highest  response  rate  on  record,  and  with  an  impressive  cost  and  quality
                  performance.

                  3.  Phase 2: Experimenting with new data collection methods
                     As  mentioned  earlier,  focusing  only  on  optimizing  Statistics  Canada’s
                  current collection operations would be insufficient. New ways of collecting data
                  must be explored to reflect the new reality of a population less interested in
                  completing surveys and to take advantage of technologies now available that
                  could  transform  primary  data  collection  operations.  This  section  presents
                  Statistics  Canada’s  research  focus  areas,  considering  the  anticipated
                  operational implementation feasibility.

                  Developing a crowdsourcing service
                     Crowdsourcing  has  been  an  early  success  in  the  introduction  of  new
                  primary  data  collection  techniques.  Crowdsourcing  involves  asking  the
                  population to proactively provide information rather than wait to be contacted
                  when selected as a respondent. The risk of such an operation is obvious to the
                  statistician— crowdsourcing data quality is difficult to assess, with metrics on
                  quality near-impossible.
                     Nevertheless, Statistics Canada began to experiment with crowdsourcing in
                  areas deemed relatively low-risk. The technique was first used for a project to
                  improve the available information about dwellings in Canada. The “crowd” was
                  asked to provide GPS locations for a set number of dwellings posted on the
                  Statistics Canada website. That drew considerable interest from the population,
                  who  provided  the  requested  information  faster  than  expected.  Secondly,
                  Statistics Canada crowdsourced the price of cannabis through its StatsCannabis
                  web application in the months before the legalization of cannabis in Canada in
                  fall 2018, when cannabis consumption was still illegal (except for approved
                  medical use). This resulted in over 20,000 entries to  the questionnaire, and
                  reasonable price estimates (i.e., within expectations, upon validation).
                     Subsequently,  Statistics  Canada  implemented  a  crowdsourcing  service
                  within its survey operations branch. The service is relatively simple—a short e-
                  questionnaire, accessible to all website visitors (i.e., there are no barriers such

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