Small area estimation strategy for the 2011 Census in England and Wales

© 2018-IOS Press and the authors. All rights reserved. The use of model-based small area estimation for adjusting census results in the UK was first introduced in the 2001 Census. The aim was to obtain local level population estimates by age-sex groups, adjusted for the level of undercount that combined results from the Census and the Census Coverage Survey. A similar approach was adopted for the 2011 Census but with new features and this paper describes the work carried out to arrive at the chosen small area strategy. Simulation studies are used to investigate three proposed small area estimation methods: a local fixed effects model (the 2001 Census approach), a direct estimator and a synthetic estimator. The results indicate that both the synthetic and the local fixed effect models constitute good options to produce accurate and reliable local authority population estimates. A proposal is made to implement a small area estimation procedure that accommodates both the synthetic and local fixed models, as in some selected areas with differing local authority under-coverage rates a local fixed effects model may perform best. We examine this strategy under real census conditions based on the final results from the 2011 census

Joint Calibration Estimator for Dual Frame Surveys.

Dual frame surveys are becoming more common in survey practice due to rapid changes in the cost of survey data collection, as well as changes in population coverage patterns and sample unit accessibility. Many dual frame estimators have been proposed in the literature. Some of these estimators are theoretically optimal but hard to be applied in practice, whereas the rest are applicable but not as optimal as the first group. All the standard dual frame estimators require accurate information about the design domain membership. In this dissertation, a set of desirable properties for the dual frame estimators is specified. These properties are used as criteria to evaluate the standard dual frame estimators. At the same time, the Joint Calibration Estimator (JCE) is proposed as a new dual frame estimator that is simple to apply and meets most of the desirable properties for dual frame estimators. In Chapter 2, the JCE is introduced as an approximately unbiased dual frame estimator, with a degree of unbiasedness depending on the relationship between study variables and auxiliary variables. The JCE achieves better performance when the auxiliary variables can fully explain the variability in the study variables of interest or at least when the auxiliary variables are strong correlates of the study variables. The JCE for point estimates can be applied by standard survey software and can easily be extended to multiple frame survey estimation. In Chapter 3, the JCE properties are explored in the presence of the nonresponse error. Theoretically and empirically, the JCE proves to be robust to nonresponse error as long as a strong set of auxiliary variables is used. This strong set should predict both the response mechanism and the main study variables. Finally, the effect of domain misclassification on the dual frame estimators is discussed in Chapter 4. Since the JCE does not require domain membership information, it tends to be robust against domain misclassification even if domain totals are included in the calibration auxiliary variables.PHDSurvey MethodologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99941/1/mkasabi_1.pd

Optimising the Sampling Methodology for CSO Household Surveys

This report assesses alternative sampling designs for CSO household surveys, such as the Quarterly National Household Survey (QNHS), the annual Survey on Income and Living Conditions (SILC) and Household Budget Survey (HBS), as well as considering the design of a potential future General Household Survey. The aim of the report is to provide recommendations in relation to appropriate sampling designs, including considerations in relation to sample size, the degree of clustering, stratification and selection procedures for areas and households