2,869 research outputs found
Estimation of a measure of disclosure risk for survey microdata under unequal probability sampling
No full textSkinner and Elliot (2002) proposed a simple measure of disclosure risk for survey microdata and showed how to estimate this measure under sampling with equal probabilities. In this paper we show how their results on point estimation and variance estimation may be extended to handle unequal probability sampling. Our approach assumes a Poisson sampling design. Comments are made about the possible impact of departures from this assumption
Calibration weighting and non-sampling errors
No full textThis paper explores the properties of calibration estimation in the presence of both nonresponse and measurement errors. The ideas are illustrated with a simple example concerning the estimation of the number of sight tests carried out in Great Britain
Analysis of survey data
No full textThis book is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected from a questionnaire or from measurements, such as those taken as part of a quality control process. Concerned with the statistical methods for the analysis of sample survey data, this book will update and extend the successful book edited by Skinner, Holt and Smith on 'Analysis of Complex Surveys'. The focus will be on methodological issues, which arise when applying statistical methods to sample survey data and will discuss in detail the impact of complex sampling schemes. Further issues, such as how to deal with missing data and measurement of error will also be critically discussed. There have significant improvements in statistical software which implement complex sampling schemes (eg SUDAAN, STATA, WESVAR, PC CARP ) in the last decade and there is greater need for practical advice for those analysing survey data. To ensure a broad audience, the statistical theory will be made accessible through the use of practical examples. This book will be accessible to a broad audience of statisticians but will primarily be of interest to practitioners analysing survey data. Increased awareness by social scientists of the variety of powerful statistical methods will make this book a useful reference
Random effects models for longitudinal survey data
No full textIntroduction (R. L. Chambers & C. J. Skinner).PART A: APPROACHES TO INFERENCE.Introduction to Part A (R. L.Chambers).Design-based and Model-based Methods for Estimating Model Parameters(David A. Binder and Georgia R. Roberts).The Bayesian Approach to Sample Survey Inference (Roderick J. Little).Interpreting a Sample as Evidence about a Finite Population (Richard Royall).PART B: CATEGORICAL RESPONSE DATA.Introduction to Part B (C. J.Skinner).Analysis of Categorical Response Data from Complex Surveys: an Appraisal and Update (J. N. K. Rao and D. R. Thomas).Fitting Logistic Regression Models in Case-Control Studies with Complex Sampling (Alastair Scott and Chris Wild).PART C: CONTINUOUS AND GENERAL RESPONSE DATA.Introduction to Part C (R. L.Chambers).Graphical Displays of Complex Survey Data through Kernel Smoothing (D. R. Bellhouse, C. M. Goia, and J. E. Stafford)Nonparametric Regression with Complex Survey Data (R. L. Chambers, A. H. Dorfman and M. Yu. Sverchkov).Fitting Generalized Linear Models under Informative Sampling (Danny Pfeffermann and M. Yu. Sverchkov).PART D: LONGITUDINAL DATA.Introduction to Part D (C. J.Skinner).Random Effects Models for Longitudinal Survey Data (C. J.Skinner and D. J.Holmes).Event History Analysis and Longitudinal Surveys (J. F. Lawless).Applying Heterogeneous Transition Models in Labour Economics: the Role of Youth Training in Labour Market Transitions (Fabrizia Mealli and Stephen Pudney).PART E: INCOMPLETE DATA.Introduction to Part E (R. L.Chambers).Bayesian Methods for Unit and Item Nonresponse (Roderick J. Little).Estimation for Multiple Phase Samples (Wayne A. Fuller).Analysis Combining Survey and Geographically Aggregated Data (D. G. Steel, M. Tranmer and D. Holt).References
Regression estimation and post-stratification in factor analysis
No full textRegression estimation and poststratification are methods used in survey sampling to estimate a population mean, when additional information is available for some auxiliary variables. The extension of these methods to factor analysis is considered. These methods may be used either to improve the efficiency of estimation or to adjust for the effects of nonrandom selection. The estimation procedure may be formulated in a LISREL framework
On the geometric approach to multivariate selection
No full textMultivariate selection can be represented as a linear transformation in a geometric framework. This approach has led to considerable simplification in the study of the effects of selection on factor analysis. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation
On conditioning for model-based inference in survey sampling
No full textA ‘discontinuity’ of sampling distribution inference between a prediction problem and an estimation problem in model-based survey sampling is discussed. The problem is resolved by conditioning on an appropriate ancillary within a curved exponential family framework. The ignorability of the sampling design for inference is discussed
Multivariate prediction from selected samples
No full textMinimum variance unbiased predictors of the mean vector and covariance matrix of a finite population are obtained under the assumption that the population values obey a given linear model. An example is given of stratified sampling
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