Statistical journal of the IAOS 

About: Statistical journal of the IAOS is an academic journal published by IOS Press. The journal publishes majorly in the area(s): Official statistics & Computer science. It has an ISSN identifier of 1874-7655. Over the lifetime, 771 publications have been published receiving 3361 citations. The journal is also known as: Statistical journal of the International Association for Official Statistics.

Multiple imputation in the Survey of Consumer Finances

Abstract: The views presented in this paper are those of the author alone and do not necessarily reflect those of the Board of Governors or the Federal Reserve System. The author wishes to thank Kevin Moore and Amy Stubbendick for a very high level of research assistance. The author also wishes to thank Roderick Little, Donald Rubin, and Fritz Scheuren for guidance and inspiration in originally undertaking multiple imputation in the SCF. Any errors remaining are the responsibility of the author alone. As Arnold Zellner remarked at a session on multiple imputation at the 1997 Joint Statistical Meetings, one should always try to get the actual data one needs rather than trying to create a proxy later. Missing information usually reflects failures in the information collection process, and we should not lose sight of this fact. However, it is regrettably the case that except in the most controlled experimental settings, it is unusual for studies of any human activity to generate complete information. The Survey of Consumer Finances (SCF) focuses intensely on the details of households' finances. Owing to the perceived sensitivity of this topic to some people, unit and item nonresponse rates in the SCF are substantial. In addition, many of the assets and liabilities treated in the survey may have values that are not always precisely defined—for example, a closely held business may have a distribution of possible values and the market value might not be knowable without actually selling the business. Such assets can have relatively high rates of missing information. Multiple imputation (MI) has provided a means of providing a public dataset that is more informative overall than anything that could be constructed with the data available to the public. At the same time, the variability in imputations allows us to give a more honest picture of the limits of our knowledge about the missing data. MI also plays a key role in the SCF in disclosure limitation. Fairly unusual observations in the population are relatively common in the SCF, and this aspect of the survey complicates the creation of public versions of the data. MI used as a data simulation tool has been a key part of a larger strategy to protect the identity of respondents while avoiding distortions in the data made available to the public. The next section provides a brief description of the SCF and some of the actions that have been taken …

The System of social statistical datasets of Statistics Netherlands: An integral approach to the production of register-based social statistics

Abstract: More and more countries are using register data to replace traditional Censuses Moreover, official statistics as well as research are increasingly based on register data or combinations of survey and register data Register-based statistics offer wonderful new opportunities At the same time, they require a new approach to how data are processed and managed In this article, we present the System of social statistical datasets (SSD), a system of interlinked and standardized registers and surveys All production processes within Statistics Netherlands that pertain to social or spatial statistics converge in the SSD, which thus constitutes a shared output-oriented system The SSD contains a wealth of information on persons, households, jobs, benefits, pensions, education, hospitalizations, crime reports, dwellings, vehicles and more In the Netherlands it is the most important source for official social statistics and, because the data are available on request by means of remote access, also very popular in the social sciences This article describes the contents of the SSD as well as the underlying process and organization, and demonstrates its possibilities

Back to the basics: Identifying and addressing underlying challenges in achieving high quality and relevant health statistics for indigenous populations in Canada.

Abstract: Canada is known internationally for excellence in both the quality and public policy relevance of its health and social statistics. There is a double standard however with respect to the relevance and quality of statistics for Indigenous populations in Canada. Indigenous specific health and social statistics gathering is informed by unique ethical, rights-based, policy and practice imperatives regarding the need for Indigenous participation and leadership in Indigenous data processes throughout the spectrum of indicator development, data collection, management, analysis and use. We demonstrate how current Indigenous data quality challenges including misclassification errors and non-response bias systematically contribute to a significant underestimate of inequities in health determinants, health status, and health care access between Indigenous and non-Indigenous people in Canada. The major quality challenge underlying these errors and biases is the lack of Indigenous specific identifiers that are consistent and relevant in major health and social data sources. The recent removal of an Indigenous identity question from the Canadian census has resulted in further deterioration of an already suboptimal system. A revision of core health data sources to include relevant, consistent, and inclusive Indigenous self-identification is urgently required. These changes need to be carried out in partnership with Indigenous peoples and their representative and governing organizations.

The methods and materials of health expectancy

Abstract: Health expectancy is an essential population health indicator and can be used to monitor changes and/or assess differences in population health. This article provides a brief overview of health expectancy and the issues to be considered in operationalizing and interpreting health expectancy. The article introduces the concept of health expectancy, discusses measures used to compute health expectancy, and methods of calculation. The discussion of measures relates health expectancy to the concept of "health" and clarifies that values of health expectancy depend on how "health" is defined. Software available to compute health expectancy including IMaCh, SPACE and ELECT are also briefly introduced.