Inference and foundations

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  • Articles and reports: 11-522-X201300014251
    Description:

    I present a modeller's perspective on the current status quo in official statistics surveys-based inference. In doing so, I try to identify the strengths and weaknesses of the design and model-based inferential positions that survey sampling, at least as far as the official statistics world is concerned, finds itself at present. I close with an example from adaptive survey design that illustrates why taking a model-based perspective (either frequentist or Bayesian) represents the best way for official statistics to avoid the debilitating 'inferential schizophrenia' that seems inevitable if current methodologies are applied to the emerging information requirements of today's world (and possibly even tomorrow's).

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014252
    Description:

    Although estimating finite populations characteristics from probability samples has been very successful for large samples, inferences from non-probability samples may also be possible. Non-probability samples have been criticized due to self-selection bias and the lack of methods for estimating the precision of the estimates. The wide spread access to the Web and the ability to do very inexpensive data collection on the Web has reinvigorated interest in this topic. We review of non-probability sampling strategies and summarize some of the key issues. We then propose conditions under which non-probability sampling may be a reasonable approach. We conclude with ideas for future research.

    Release date: 2014-10-31

  • Surveys and statistical programs – Documentation: 11-522-X201300014259
    Description:

    In an effort to reduce response burden on farm operators, Statistics Canada is studying alternative approaches to telephone surveys for producing field crop estimates. One option is to publish harvested area and yield estimates in September as is currently done, but to calculate them using models based on satellite and weather data, and data from the July telephone survey. However before adopting such an approach, a method must be found which produces estimates with a sufficient level of accuracy. Research is taking place to investigate different possibilities. Initial research results and issues to consider are discussed in this paper.

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014280
    Description:

    During the last decade, web panel surveys have been established as a fast and cost-efficient method in market surveys. The rationale for this is new developments in information technology, in particular the continued rapid growth of internet and computer use among the public. Also growing nonresponse rates and prices forced down in the survey industry lie behind this change. However, there are some serious inherent risks connected with web panel surveys, not least selection bias due to the self-selection of respondents. There are also risks of coverage and measurement errors. The absence of an inferential framework and of data quality indicators is an obstacle against using the web panel approach for high-quality statistics about general populations. Still, there seems to be increasing challenges for some national statistical institutes by a new form of competition for ad hoc statistics and even official statistics from web panel surveys.This paper explores the question of design and use of web panels in a scientifically sound way. An outline is given of a standard from the Swedish Survey Society for performance metrics to assess some quality aspects of results from web panel surveys. Decomposition of bias and mitigation of bias risks are discussed in some detail. Some ideas are presented for combining web panel surveys and traditional surveys to achieve controlled cost-efficient inference.

    Release date: 2014-10-31

  • Articles and reports: 12-001-X201400114004
    Description:

    In 2009, two major surveys in the Governments Division of the U.S. Census Bureau were redesigned to reduce sample size, save resources, and improve the precision of the estimates (Cheng, Corcoran, Barth and Hogue 2009). The new design divides each of the traditional state by government-type strata with sufficiently many units into two sub-strata according to each governmental unit’s total payroll, in order to sample less from the sub-stratum with small size units. The model-assisted approach is adopted in estimating population totals. Regression estimators using auxiliary variables are obtained either within each created sub-stratum or within the original stratum by collapsing two sub-strata. A decision-based method was proposed in Cheng, Slud and Hogue (2010), applying a hypothesis test to decide which regression estimator is used within each original stratum. Consistency and asymptotic normality of these model-assisted estimators are established here, under a design-based or model-assisted asymptotic framework. Our asymptotic results also suggest two types of consistent variance estimators, one obtained by substituting unknown quantities in the asymptotic variances and the other by applying the bootstrap. The performance of all the estimators of totals and of their variance estimators are examined in some empirical studies. The U.S. Annual Survey of Public Employment and Payroll (ASPEP) is used to motivate and illustrate our study.

    Release date: 2014-06-27

  • Articles and reports: 12-001-X201300211887
    Description:

    Multi-level models are extensively used for analyzing survey data with the design hierarchy matching the model hierarchy. We propose a unified approach, based on a design-weighted log composite likelihood, for two-level models that leads to design-model consistent estimators of the model parameters even when the within cluster sample sizes are small provided the number of sample clusters is large. This method can handle both linear and generalized linear two-level models and it requires level 2 and level 1 inclusion probabilities and level 1 joint inclusion probabilities, where level 2 represents a cluster and level 1 an element within a cluster. Results of a simulation study demonstrating superior performance of the proposed method relative to existing methods under informative sampling are also reported.

    Release date: 2014-01-15
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  • Articles and reports: 11-522-X201300014251
    Description:

    I present a modeller's perspective on the current status quo in official statistics surveys-based inference. In doing so, I try to identify the strengths and weaknesses of the design and model-based inferential positions that survey sampling, at least as far as the official statistics world is concerned, finds itself at present. I close with an example from adaptive survey design that illustrates why taking a model-based perspective (either frequentist or Bayesian) represents the best way for official statistics to avoid the debilitating 'inferential schizophrenia' that seems inevitable if current methodologies are applied to the emerging information requirements of today's world (and possibly even tomorrow's).

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014252
    Description:

    Although estimating finite populations characteristics from probability samples has been very successful for large samples, inferences from non-probability samples may also be possible. Non-probability samples have been criticized due to self-selection bias and the lack of methods for estimating the precision of the estimates. The wide spread access to the Web and the ability to do very inexpensive data collection on the Web has reinvigorated interest in this topic. We review of non-probability sampling strategies and summarize some of the key issues. We then propose conditions under which non-probability sampling may be a reasonable approach. We conclude with ideas for future research.

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014280
    Description:

    During the last decade, web panel surveys have been established as a fast and cost-efficient method in market surveys. The rationale for this is new developments in information technology, in particular the continued rapid growth of internet and computer use among the public. Also growing nonresponse rates and prices forced down in the survey industry lie behind this change. However, there are some serious inherent risks connected with web panel surveys, not least selection bias due to the self-selection of respondents. There are also risks of coverage and measurement errors. The absence of an inferential framework and of data quality indicators is an obstacle against using the web panel approach for high-quality statistics about general populations. Still, there seems to be increasing challenges for some national statistical institutes by a new form of competition for ad hoc statistics and even official statistics from web panel surveys.This paper explores the question of design and use of web panels in a scientifically sound way. An outline is given of a standard from the Swedish Survey Society for performance metrics to assess some quality aspects of results from web panel surveys. Decomposition of bias and mitigation of bias risks are discussed in some detail. Some ideas are presented for combining web panel surveys and traditional surveys to achieve controlled cost-efficient inference.

    Release date: 2014-10-31

  • Articles and reports: 12-001-X201400114004
    Description:

    In 2009, two major surveys in the Governments Division of the U.S. Census Bureau were redesigned to reduce sample size, save resources, and improve the precision of the estimates (Cheng, Corcoran, Barth and Hogue 2009). The new design divides each of the traditional state by government-type strata with sufficiently many units into two sub-strata according to each governmental unit’s total payroll, in order to sample less from the sub-stratum with small size units. The model-assisted approach is adopted in estimating population totals. Regression estimators using auxiliary variables are obtained either within each created sub-stratum or within the original stratum by collapsing two sub-strata. A decision-based method was proposed in Cheng, Slud and Hogue (2010), applying a hypothesis test to decide which regression estimator is used within each original stratum. Consistency and asymptotic normality of these model-assisted estimators are established here, under a design-based or model-assisted asymptotic framework. Our asymptotic results also suggest two types of consistent variance estimators, one obtained by substituting unknown quantities in the asymptotic variances and the other by applying the bootstrap. The performance of all the estimators of totals and of their variance estimators are examined in some empirical studies. The U.S. Annual Survey of Public Employment and Payroll (ASPEP) is used to motivate and illustrate our study.

    Release date: 2014-06-27

  • Articles and reports: 12-001-X201300211887
    Description:

    Multi-level models are extensively used for analyzing survey data with the design hierarchy matching the model hierarchy. We propose a unified approach, based on a design-weighted log composite likelihood, for two-level models that leads to design-model consistent estimators of the model parameters even when the within cluster sample sizes are small provided the number of sample clusters is large. This method can handle both linear and generalized linear two-level models and it requires level 2 and level 1 inclusion probabilities and level 1 joint inclusion probabilities, where level 2 represents a cluster and level 1 an element within a cluster. Results of a simulation study demonstrating superior performance of the proposed method relative to existing methods under informative sampling are also reported.

    Release date: 2014-01-15
Reference (1)

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  • Surveys and statistical programs – Documentation: 11-522-X201300014259
    Description:

    In an effort to reduce response burden on farm operators, Statistics Canada is studying alternative approaches to telephone surveys for producing field crop estimates. One option is to publish harvested area and yield estimates in September as is currently done, but to calculate them using models based on satellite and weather data, and data from the July telephone survey. However before adopting such an approach, a method must be found which produces estimates with a sufficient level of accuracy. Research is taking place to investigate different possibilities. Initial research results and issues to consider are discussed in this paper.

    Release date: 2014-10-31
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