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- 1. Targeted random walk designs ArchivedArticles and reports: 12-001-X20060019262Description:
Hidden human populations, the Internet, and other networked structures conceptualized mathematically as graphs are inherently hard to sample by conventional means, and the most effective study designs usually involve procedures that select the sample by adaptively following links from one node to another. Sample data obtained in such studies are generally not representative at face value of the larger population of interest. However, a number of design and model based methods are now available for effective inference from such samples. The design based methods have the advantage that they do not depend on an assumed population model, but do depend for their validity on the design being implemented in a controlled and known way, which can be difficult or impossible in practice. The model based methods allow greater flexibly in the design, but depend on modeling of the population using stochastic graph models and also depend on the design being ignorable or of known form so that it can be included in the likelihood or Bayes equations. For both the design and the model based methods, the weak point often is the lack of control in how the initial sample is obtained, from which link-tracing commences. The designs described in this paper offer a third way, in which the sample selection probabilities become step by step less dependent on the initial sample selection. A Markov chain "random walk" model idealizes the natural design tendencies of a link-tracing selection sequence through a graph. This paper introduces uniform and targeted walk designs in which the random walk is nudged at each step to produce a design with the desired stationary probabilities. A sample is thus obtained that in important respects is representative at face value of the larger population of interest, or that requires only simple weighting factors to make it so.
Release date: 2006-07-20 - 2. Rounding guidelines for post-censal surveys and the Longitudinal Survey of Immigrants to Canada ArchivedArticles and reports: 12-002-X20060019253Description:
Before any analytical results are released from the Research Data Centres (RDCs), RDC analysts must conduct disclosure risk analysis (or vetting). RDC analysts apply Statistics Canada's disclosure control guidelines, when reviewing all analytical output, as a means of ensuring the protection of survey respondents' confidentiality. For some data sets, such as the Aboriginal People's Survey (APS), Ethnic Diversity Survey (EDS), the Participation, Activity and Limitation Survey (PALS) and the Longitudinal Survey of Immigrants to Canada (LSIC), Statistics Canada has developed an additional set of guidelines that involve rounding analytical results, in order to ensure further confidentiality protection. This article will discuss the rationale for the additional rounding procedures used for these data sets, and describe the specifics of the rounding guidelines. More importantly, this paper will suggest several approaches to assist researchers in following these protocols more effectively and efficiently.
Release date: 2006-07-18 - Articles and reports: 75F0002M2006005Description:
The Survey of Labour and Income Dynamics (SLID) is a longitudinal survey initiated in 1993. The survey was designed to measure changes in the economic well-being of Canadians as well as the factors affecting these changes.
Sample surveys are subject to errors. As with all surveys conducted at Statistics Canada, considerable time and effort is taken to control such errors at every stage of the Survey of Labour and Income Dynamics. Nonetheless errors do occur. It is the policy at Statistics Canada to furnish users with measures of data quality so that the user is able to interpret the data properly. This report summarizes a set of quality measures that has been produced in an attempt to describe the overall quality of SLID data. Among the measures included in the report are sample composition and attrition rates, sampling errors, coverage errors in the form of slippage rates, response rates, tax permission and tax linkage rates, and imputation rates.
Release date: 2006-04-06
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- 1. Targeted random walk designs ArchivedArticles and reports: 12-001-X20060019262Description:
Hidden human populations, the Internet, and other networked structures conceptualized mathematically as graphs are inherently hard to sample by conventional means, and the most effective study designs usually involve procedures that select the sample by adaptively following links from one node to another. Sample data obtained in such studies are generally not representative at face value of the larger population of interest. However, a number of design and model based methods are now available for effective inference from such samples. The design based methods have the advantage that they do not depend on an assumed population model, but do depend for their validity on the design being implemented in a controlled and known way, which can be difficult or impossible in practice. The model based methods allow greater flexibly in the design, but depend on modeling of the population using stochastic graph models and also depend on the design being ignorable or of known form so that it can be included in the likelihood or Bayes equations. For both the design and the model based methods, the weak point often is the lack of control in how the initial sample is obtained, from which link-tracing commences. The designs described in this paper offer a third way, in which the sample selection probabilities become step by step less dependent on the initial sample selection. A Markov chain "random walk" model idealizes the natural design tendencies of a link-tracing selection sequence through a graph. This paper introduces uniform and targeted walk designs in which the random walk is nudged at each step to produce a design with the desired stationary probabilities. A sample is thus obtained that in important respects is representative at face value of the larger population of interest, or that requires only simple weighting factors to make it so.
Release date: 2006-07-20 - 2. Rounding guidelines for post-censal surveys and the Longitudinal Survey of Immigrants to Canada ArchivedArticles and reports: 12-002-X20060019253Description:
Before any analytical results are released from the Research Data Centres (RDCs), RDC analysts must conduct disclosure risk analysis (or vetting). RDC analysts apply Statistics Canada's disclosure control guidelines, when reviewing all analytical output, as a means of ensuring the protection of survey respondents' confidentiality. For some data sets, such as the Aboriginal People's Survey (APS), Ethnic Diversity Survey (EDS), the Participation, Activity and Limitation Survey (PALS) and the Longitudinal Survey of Immigrants to Canada (LSIC), Statistics Canada has developed an additional set of guidelines that involve rounding analytical results, in order to ensure further confidentiality protection. This article will discuss the rationale for the additional rounding procedures used for these data sets, and describe the specifics of the rounding guidelines. More importantly, this paper will suggest several approaches to assist researchers in following these protocols more effectively and efficiently.
Release date: 2006-07-18 - Articles and reports: 75F0002M2006005Description:
The Survey of Labour and Income Dynamics (SLID) is a longitudinal survey initiated in 1993. The survey was designed to measure changes in the economic well-being of Canadians as well as the factors affecting these changes.
Sample surveys are subject to errors. As with all surveys conducted at Statistics Canada, considerable time and effort is taken to control such errors at every stage of the Survey of Labour and Income Dynamics. Nonetheless errors do occur. It is the policy at Statistics Canada to furnish users with measures of data quality so that the user is able to interpret the data properly. This report summarizes a set of quality measures that has been produced in an attempt to describe the overall quality of SLID data. Among the measures included in the report are sample composition and attrition rates, sampling errors, coverage errors in the form of slippage rates, response rates, tax permission and tax linkage rates, and imputation rates.
Release date: 2006-04-06
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