03838nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001700186072002300203082001400226100003300240245007000273264004600343300003400389336002600423337002600449338003600475347002400511490004600535505021700581520183800798650001602636650002202652650001602674650002102690650002102711650001602732650001602748650009002764650003602854650002602890650003002916650001602946650004002962710003403002773002003036776003603056830004603092856004803138912001403186999001903200952007703219978-0-387-70782-2DE-He21320180115171413.0cr nn 008mamaa100301s2007 xxu| s |||| 0|eng d a97803877078229978-0-387-70782-27 a10.1007/978-0-387-70782-22doi 4aQA276-280 7aJHBC2bicssc 7aSOC0270002bisacsh04a519.52231 aLavallée, Pierre.eauthor.10aIndirect Samplingh[electronic resource] /cby Pierre Lavallée. 1aNew York, NY :bSpringer New York,c2007. aXVI, 256 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aSpringer Series in Statistics,x0172-73970 aDescription and Use of the GWSM -- Literature Review -- Properties -- Other Generalisations -- Application in Longitudinal Surveys -- GWSM and Calibration -- Non-response -- GWSM and Record Linkage -- Conclusion. aFollowing the classical sampling theory, the survey statistician selects samples of people, businesses or others, in order to obtain the desired information. Drawing the samples is usually done by randomly selecting from a list representing the target population. In practice, this list is often not available. At best, the statistician only has access to a different list, indirectly related to the targeted population. The example of a survey of children where the statistician only has a list of adult persons is a typical case. In this case, the statistician first draws a sample of adults, and for each selected adult, the statistician then identifies his/her children. The survey is done from the latter. This is what is called indirect sampling. When indirect sampling is used jointly with the sampling of clusters of persons (families, for example), many complications arise for the survey statistician. One of the complications relates to the computation of the estimates from the survey. The production of estimates of simple totals or means can then become nightmares for the survey statistician. To solve this problem, the author proposes a simple solution, easy to implement, that is called the generalised weight share method. This book is the reference on indirect sampling and the generalised weight share method. It contains the different developments done by the author on these subjects. The theory surrounding them is presented, but also different possible applications that drive its interest. The reader will find in this book the answer to questions that come, inevitably, when working in a context of indirect sampling. Pierre Lavallée has been a survey statistician at Statistics Canada since 1985. He gas worked in social, business, and agricultural surveys. He has also worked for Eurostat in Luxembourg. 0aStatistics. 0aMedical research. 0aPopulation. 0aSocial sciences. 0aQuality of life. 0aDemography.14aStatistics.24aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.24aStatistical Theory and Methods.24aPopulation Economics.24aQuality of Life Research.24aDemography.24aMethodology of the Social Sciences.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387707785 0aSpringer Series in Statistics,x0172-739740uhttp://dx.doi.org/10.1007/978-0-387-70782-2 aZDB-2-SMA c369591d369591 001040708EBookaelibbelibd2018-01-15r2018-01-15w2018-01-15yEBOOK