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• 1. Methodological Document on the 2011 Census Language Data Archived

  Surveys and statistical programs – Documentation: 98-314-X2011051

  Description:

  Readers will find a complete analysis of factors affecting the comparability of Language results between the censuses in the Methodological Document on the 2011 Census Language Data.

  Release date: 2013-05-03

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• 2. Methodological challenges in a survey on the ethnic and cultural diversity of the Canadian population Archived

  Articles and reports: 11-522-X20040018734

  Geography: Canada

  Description:

  The Ethnic Diversity Survey generated methodological challenges like choosing the sampling plan, developing the questionnaire, collecting the data, weighting the data and estimating the variance.

  Release date: 2005-10-27

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• 3. Assessing comprehension of translated questionnaires with qualitative methods Archived

  Articles and reports: 11-522-X20040018746

  Description:

  This document discusses the qualitative testing of translated questionnaires, the problems typically identified, and the challenges in finding solutions that preserve the intent of the original instrument, while addressing dialect.

  Release date: 2005-10-27

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• 4. Accuracy estimation with clustered dataset Archived

  Articles and reports: 11-522-X20020016737

  Description:

  If the dataset available to machine learning results from cluster sampling (e.g., patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. In this technical paper, an adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random sampling hypothesis, is compared with the true value. The results highlight the impact of the sampling design on inference: clearly, clustering has a significant impact; the repartition between learning set and test set should result from a random partition of the clusters, not from a random partition of the examples. With cluster sampling, standard cross-validation underestimates the generalization error rate, and is deficient for model selection. These results are illustrated with a real application of automatic identification of spoken language.

  Release date: 2004-09-13

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Analysis (3) ((3 results))

• 1. Methodological challenges in a survey on the ethnic and cultural diversity of the Canadian population Archived

  Articles and reports: 11-522-X20040018734

  Geography: Canada

  Description:

  The Ethnic Diversity Survey generated methodological challenges like choosing the sampling plan, developing the questionnaire, collecting the data, weighting the data and estimating the variance.

  Release date: 2005-10-27

  ---

• 2. Assessing comprehension of translated questionnaires with qualitative methods Archived

  Articles and reports: 11-522-X20040018746

  Description:

  This document discusses the qualitative testing of translated questionnaires, the problems typically identified, and the challenges in finding solutions that preserve the intent of the original instrument, while addressing dialect.

  Release date: 2005-10-27

  ---

• 3. Accuracy estimation with clustered dataset Archived

  Articles and reports: 11-522-X20020016737

  Description:

  If the dataset available to machine learning results from cluster sampling (e.g., patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. In this technical paper, an adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random sampling hypothesis, is compared with the true value. The results highlight the impact of the sampling design on inference: clearly, clustering has a significant impact; the repartition between learning set and test set should result from a random partition of the clusters, not from a random partition of the examples. With cluster sampling, standard cross-validation underestimates the generalization error rate, and is deficient for model selection. These results are illustrated with a real application of automatic identification of spoken language.

  Release date: 2004-09-13

Reference (1) ((1 result))

• 1. Methodological Document on the 2011 Census Language Data Archived

  Surveys and statistical programs – Documentation: 98-314-X2011051

  Description:

  Readers will find a complete analysis of factors affecting the comparability of Language results between the censuses in the Methodological Document on the 2011 Census Language Data.

  Release date: 2013-05-03