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Administrative Records for Survey Methodology
ADMINISTRATIVE RECORDS FOR SURVEY METHODOLOGY Addresses the international use of administrative records for large-scale surveys, censuses, and other statistical purposes Administrative Records for Survey Methodology is a comprehensive guide to improving the quality, cost-efficiency, and interpretability of surveys and censuses using administrative data research. Contributions from a team of internationally-recognized experts provide practical approaches for integrating administrative data in statistical surveys, and discuss the methodological issues—including concerns of privacy, confidentiality, and legality—involved in collecting and analyzing administrative records. Numerous real-worl...
Improving Surveys with Paradata
Explore the practices and cutting-edge research on the new and exciting topic of paradata Paradata are measurements related to the process of collecting survey data. Improving Surveys with Paradata: Analytic Uses of Process Information is the most accessible and comprehensive contribution to this up-and-coming area in survey methodology. Featuring contributions from leading experts in the field, Improving Surveys with Paradata: Analytic Uses of Process Information introduces and reviews issues involved in the collection and analysis of paradata. The book presents readers with an overview of the indispensable techniques and new, innovative research on improving survey quality and total survey...
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The Measurement of Low Pay in the UK Labour Force Survey
Consideration of the National Minimum Wage requires estimates of the distribution of hourly pay. The UK Labour Force Survey (LFS) is a key source of such estimates. The approach most frequently adopted by researchers has been to measure hourly earnings from several questions on pay and hours. The Office for National Statistics is now applying a new approach, based on an alternative more direct measurement introduced in March 1999. These two measures do not produce identical values and this paper investigates sources of discrepancies and concludes that the new variable is more accurate. The difficulty with using the new variable is that it is only available on a subset of respondents. An approach is developed in which missing values of the new variable are replaced by imputed values. The assumptions underlying this imputation approach and results of applying it to LFS data are presented. The relation to weighting approaches is also discussed.
