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- Articles and reports: 11-522-X202500100021Description: Optimal threshold selection is a critical challenge in probabilistic linkage, with significant implications for the accuracy and reliability of linked datasets. This paper analyzes the performance of the neighbour model, a recently proposed error model which models linkage errors by the number of links from each record. Three threshold selection algorithms utilizing the neighbour model were assessed, highlighting the strengths and limitations of each. Their performance was assessed through simulation studies, which demonstrated that methods using the neighbour model achieved lower relative bias compared to two established methods for threshold selection. Additionally, the practical utility was validated through goodness-of-fit tests conducted on four agricultural datasets, showing the potential of the model for use in real-world applications.Release date: 2025-09-08
- Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 12-001-X202100100004Description: Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.Release date: 2021-06-24
- Articles and reports: 18-001-X2016001Description:
Although the record linkage of business data is not a completely new topic, the fact remains that the public and many data users are unaware of the programs and practices commonly used by statistical agencies across the world.
This report is a brief overview of the main practices, programs and challenges of record linkage of statistical agencies across the world who answered a short survey on this subject supplemented by publically available documentation produced by these agencies. The document shows that the linkage practices are similar between these statistical agencies; however the main differences are in the procedures in place to access to data along with regulatory policies that govern the record linkage permissions and the dissemination of data.
Release date: 2016-10-27 - 5. Statistical matching using fractional imputation ArchivedArticles and reports: 12-001-X201600114539Description:
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent EM sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Release date: 2016-06-22 - Articles and reports: 11-522-X200600110401Description:
The Australian Bureau of Statistics (ABS) will begin the formation of a Statistical Longitudinal Census Data Set (SLCD) by choosing a 5% sample of people from the 2006 population census to be linked probabilistically with subsequent censuses. A long-term aim is to use the power of the rich longitudinal demographic data provided by the SLCD to shed light on a variety of issues which cannot be addressed using cross-sectional data. The SLCD may be further enhanced by probabilistically linking it with births, deaths, immigration settlements or disease registers. This paper gives a brief description of recent developments in data linking at the ABS, outlines the data linking methodology and quality measures we have considered and summarises preliminary results using Census Dress Rehearsal data.
Release date: 2008-03-17 - Articles and reports: 12-001-X20050018088Description:
When administrative records are geographically linked to census block groups, local-area characteristics from the census can be used as contextual variables, which may be useful supplements to variables that are not directly observable from the administrative records. Often databases contain records that have insufficient address information to permit geographical links with census block groups; the contextual variables for these records are therefore unobserved. We propose a new method that uses information from "matched cases" and multivariate regression models to create multiple imputations for the unobserved variables. Our method outperformed alternative methods in simulation evaluations using census data, and was applied to the dataset for a study on treatment patterns for colorectal cancer patients.
Release date: 2005-07-21 - Articles and reports: 11-522-X20010016248Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
The Sawmill Survey is a voluntary census of sawmills in Great Britain. It is limited to fixed mills using domestically-grown timber. Three approaches to assess the coverage of this survey are described:
(1) A sample survey of the sawmilling industry from the UK's business register, excluding businesses already sampled in the Sawmill Survey, is used to assess the undercoverage in the list of known sawmills; (2) A non-response follow-up using local knowledge of regional officers of the Forestry Commission, is used to estimate the sawmills that do not respond (mostly the smaller mills); and (3) A survey of small-scale sawmills and mobile sawmills (many of these businesses are micro-enterprises) is conducted to analyse their significance.
These three approaches are synthesized to give an estimate of the coverage of the original survey compared with the total activity identified, and to estimate the importance of micro-enterprises to the sawmilling industry in Great Britain.
Release date: 2002-09-12 - Articles and reports: 11-522-X20010016249Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.The United States' Census 2000 operations were more innovative and complex than ever before. State population totals were required to be produced within nine months and, using the coverage measurement survey, adjusted counts were expected within one year. Therefore, all operations had to be implemented and completed quickly with quality assurance (QA) that had both an effective and prompt turnaround. The QA challenges included: getting timely information to supervisors (such as enumerator re-interview information), performing prompt checks of "suspect" work (such as monitoring contractors to ensure accurate data capture), and providing reports to headquarters quickly. This paper presents these challenges and their solutions in detail, thus providing an overview of the Census 2000 QA program.
Release date: 2002-09-12 - Articles and reports: 11-522-X20010016270Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
Following the last three censuses in Britain, survey non-response on major government household surveys has been investigated by linking addresses sampled for surveys taking place around the time of the census to individual census records for the same addresses. This paper outlines the design of the 2001 British Census-linked Study of Survey Nonresponse. The study involves 10 surveys that vary significantly in design and response rates. The key feature of the study is the extensive use of auxiliary data and multilevel modelling to identify interviewer, household and area level effects.
Release date: 2002-09-12
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- Articles and reports: 11-522-X202500100021Description: Optimal threshold selection is a critical challenge in probabilistic linkage, with significant implications for the accuracy and reliability of linked datasets. This paper analyzes the performance of the neighbour model, a recently proposed error model which models linkage errors by the number of links from each record. Three threshold selection algorithms utilizing the neighbour model were assessed, highlighting the strengths and limitations of each. Their performance was assessed through simulation studies, which demonstrated that methods using the neighbour model achieved lower relative bias compared to two established methods for threshold selection. Additionally, the practical utility was validated through goodness-of-fit tests conducted on four agricultural datasets, showing the potential of the model for use in real-world applications.Release date: 2025-09-08
- Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 12-001-X202100100004Description: Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.Release date: 2021-06-24
- Articles and reports: 18-001-X2016001Description:
Although the record linkage of business data is not a completely new topic, the fact remains that the public and many data users are unaware of the programs and practices commonly used by statistical agencies across the world.
This report is a brief overview of the main practices, programs and challenges of record linkage of statistical agencies across the world who answered a short survey on this subject supplemented by publically available documentation produced by these agencies. The document shows that the linkage practices are similar between these statistical agencies; however the main differences are in the procedures in place to access to data along with regulatory policies that govern the record linkage permissions and the dissemination of data.
Release date: 2016-10-27 - 5. Statistical matching using fractional imputation ArchivedArticles and reports: 12-001-X201600114539Description:
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent EM sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.
Release date: 2016-06-22 - Articles and reports: 11-522-X200600110401Description:
The Australian Bureau of Statistics (ABS) will begin the formation of a Statistical Longitudinal Census Data Set (SLCD) by choosing a 5% sample of people from the 2006 population census to be linked probabilistically with subsequent censuses. A long-term aim is to use the power of the rich longitudinal demographic data provided by the SLCD to shed light on a variety of issues which cannot be addressed using cross-sectional data. The SLCD may be further enhanced by probabilistically linking it with births, deaths, immigration settlements or disease registers. This paper gives a brief description of recent developments in data linking at the ABS, outlines the data linking methodology and quality measures we have considered and summarises preliminary results using Census Dress Rehearsal data.
Release date: 2008-03-17 - Articles and reports: 12-001-X20050018088Description:
When administrative records are geographically linked to census block groups, local-area characteristics from the census can be used as contextual variables, which may be useful supplements to variables that are not directly observable from the administrative records. Often databases contain records that have insufficient address information to permit geographical links with census block groups; the contextual variables for these records are therefore unobserved. We propose a new method that uses information from "matched cases" and multivariate regression models to create multiple imputations for the unobserved variables. Our method outperformed alternative methods in simulation evaluations using census data, and was applied to the dataset for a study on treatment patterns for colorectal cancer patients.
Release date: 2005-07-21 - Articles and reports: 11-522-X20010016248Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
The Sawmill Survey is a voluntary census of sawmills in Great Britain. It is limited to fixed mills using domestically-grown timber. Three approaches to assess the coverage of this survey are described:
(1) A sample survey of the sawmilling industry from the UK's business register, excluding businesses already sampled in the Sawmill Survey, is used to assess the undercoverage in the list of known sawmills; (2) A non-response follow-up using local knowledge of regional officers of the Forestry Commission, is used to estimate the sawmills that do not respond (mostly the smaller mills); and (3) A survey of small-scale sawmills and mobile sawmills (many of these businesses are micro-enterprises) is conducted to analyse their significance.
These three approaches are synthesized to give an estimate of the coverage of the original survey compared with the total activity identified, and to estimate the importance of micro-enterprises to the sawmilling industry in Great Britain.
Release date: 2002-09-12 - Articles and reports: 11-522-X20010016249Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.The United States' Census 2000 operations were more innovative and complex than ever before. State population totals were required to be produced within nine months and, using the coverage measurement survey, adjusted counts were expected within one year. Therefore, all operations had to be implemented and completed quickly with quality assurance (QA) that had both an effective and prompt turnaround. The QA challenges included: getting timely information to supervisors (such as enumerator re-interview information), performing prompt checks of "suspect" work (such as monitoring contractors to ensure accurate data capture), and providing reports to headquarters quickly. This paper presents these challenges and their solutions in detail, thus providing an overview of the Census 2000 QA program.
Release date: 2002-09-12 - Articles and reports: 11-522-X20010016270Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
Following the last three censuses in Britain, survey non-response on major government household surveys has been investigated by linking addresses sampled for surveys taking place around the time of the census to individual census records for the same addresses. This paper outlines the design of the 2001 British Census-linked Study of Survey Nonresponse. The study involves 10 surveys that vary significantly in design and response rates. The key feature of the study is the extensive use of auxiliary data and multilevel modelling to identify interviewer, household and area level effects.
Release date: 2002-09-12
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