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- Articles and reports: 12-001-X201900100006Description:
The empirical predictor under an area level version of the generalized linear mixed model (GLMM) is extensively used in small area estimation (SAE) for counts. However, this approach does not use the sampling weights or clustering information that are essential for valid inference given the informative samples produced by modern complex survey designs. This paper describes an SAE method that incorporates this sampling information when estimating small area proportions or counts under an area level version of the GLMM. The approach is further extended under a spatial dependent version of the GLMM (SGLMM). The mean squared error (MSE) estimation for this method is also discussed. This SAE method is then applied to estimate the extent of household poverty in different districts of the rural part of the state of Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey collected by the National Sample Survey Office (NSSO) of India, and the 2011 Indian Population Census. Results from this application indicate a substantial gain in precision for the new methods compared to the direct survey estimates.
Release date: 2019-05-07 - Articles and reports: 11-522-X20010016279Description:
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.
Rather than having to rely on traditional measures of survey quality, such as response rates, the Social Survey Division of the U.K. Office for National Statistics has been looking for alternative ways to report on quality. In order to achieve this, all the processes involved throughout the lifetime of a survey, from sampling and questionnaire design through to production of the finished report, have been mapped out. Having done this, we have been able to find quality indicators for many of these processes. By using this approach, we hope to be able to appraise any changes to our processes as well as to inform our customers of the quality of the work we carry out.
Release date: 2002-09-12 - Surveys and statistical programs – Documentation: 11-522-X19990015674Description:
The effect of the environment on health is of increasing concern, in particular the effects of the release of industrial pollutants into the air, the ground and into water. An assessment of the risks to public health of any particular pollution source is often made using the routine health, demographic and environmental data collected by government agencies. These datasets have important differences in sampling geography and in sampling epochs which affect the epidemiological analyses which draw them together. In the UK, health events are recorded for individuals, giving cause codes, a data of diagnosis or death, and using the unit postcode as a geographical reference. In contrast, small area demographic data are recorded only at the decennial census, and released as area level data in areas distinct from postcode geography. Environmental exposure data may be available at yet another resolution, depending on the type of exposure and the source of the measurements.
Release date: 2000-03-02 - 4. Applications of spatial smoothing to survey data ArchivedArticles and reports: 12-001-X19960022984Description:
In this paper we present two applications of spatial smoothing using data collected in a large scale economic survey of Australian farms: one a small area and the other a large area application. In the small area application, we describe how the sample weigths can be spatially smoothed in order to improve small area estimates. In the large area application, we give a method for spatially smoothing and then mapping the survey data. The standard method of weighting in the survey is a variant of linear regression weighting. For the small area application, this method is modified by introducing a constraint on the spatial variability of the weights. Results from a small scale empirical study indicate that this decreases the variance of the small area estimators as expected, but at the cost of an increase in their bias. In the large area application, we describe the nonparametric regression method used to spatially smooth the survey data as well as techniques for mapping this smoothed data using a Geographic Information System (GIS) package. We also present the results of a simulation study conducted to determine the most appropriate method and level of smoothing for use in the maps.
Release date: 1997-01-30
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- Articles and reports: 12-001-X201900100006Description:
The empirical predictor under an area level version of the generalized linear mixed model (GLMM) is extensively used in small area estimation (SAE) for counts. However, this approach does not use the sampling weights or clustering information that are essential for valid inference given the informative samples produced by modern complex survey designs. This paper describes an SAE method that incorporates this sampling information when estimating small area proportions or counts under an area level version of the GLMM. The approach is further extended under a spatial dependent version of the GLMM (SGLMM). The mean squared error (MSE) estimation for this method is also discussed. This SAE method is then applied to estimate the extent of household poverty in different districts of the rural part of the state of Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey collected by the National Sample Survey Office (NSSO) of India, and the 2011 Indian Population Census. Results from this application indicate a substantial gain in precision for the new methods compared to the direct survey estimates.
Release date: 2019-05-07 - Articles and reports: 11-522-X20010016279Description:
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.
Rather than having to rely on traditional measures of survey quality, such as response rates, the Social Survey Division of the U.K. Office for National Statistics has been looking for alternative ways to report on quality. In order to achieve this, all the processes involved throughout the lifetime of a survey, from sampling and questionnaire design through to production of the finished report, have been mapped out. Having done this, we have been able to find quality indicators for many of these processes. By using this approach, we hope to be able to appraise any changes to our processes as well as to inform our customers of the quality of the work we carry out.
Release date: 2002-09-12 - 3. Applications of spatial smoothing to survey data ArchivedArticles and reports: 12-001-X19960022984Description:
In this paper we present two applications of spatial smoothing using data collected in a large scale economic survey of Australian farms: one a small area and the other a large area application. In the small area application, we describe how the sample weigths can be spatially smoothed in order to improve small area estimates. In the large area application, we give a method for spatially smoothing and then mapping the survey data. The standard method of weighting in the survey is a variant of linear regression weighting. For the small area application, this method is modified by introducing a constraint on the spatial variability of the weights. Results from a small scale empirical study indicate that this decreases the variance of the small area estimators as expected, but at the cost of an increase in their bias. In the large area application, we describe the nonparametric regression method used to spatially smooth the survey data as well as techniques for mapping this smoothed data using a Geographic Information System (GIS) package. We also present the results of a simulation study conducted to determine the most appropriate method and level of smoothing for use in the maps.
Release date: 1997-01-30
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Reference (1) ((1 result))
- Surveys and statistical programs – Documentation: 11-522-X19990015674Description:
The effect of the environment on health is of increasing concern, in particular the effects of the release of industrial pollutants into the air, the ground and into water. An assessment of the risks to public health of any particular pollution source is often made using the routine health, demographic and environmental data collected by government agencies. These datasets have important differences in sampling geography and in sampling epochs which affect the epidemiological analyses which draw them together. In the UK, health events are recorded for individuals, giving cause codes, a data of diagnosis or death, and using the unit postcode as a geographical reference. In contrast, small area demographic data are recorded only at the decennial census, and released as area level data in areas distinct from postcode geography. Environmental exposure data may be available at yet another resolution, depending on the type of exposure and the source of the measurements.
Release date: 2000-03-02
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