LEADER 00768oam 22002413u 450 001 9910158886103321 005 20250511160237.0 010 $a1-68195-680-2 035 $a(CKB)3710000001011836 035 $a(EXLCZ)993710000001011836 100 $a20220128d2004|||| ||s | 101 0 $aeng 200 10$aA Letter to a Hindu 210 $cProject Gutenberg 606 $aNational characteristics$aEast Indian$aGovernment$aResistance to$aEvil$aNon-resistance to 615 14$aNational characteristics$aEast Indian$aGovernment$aResistance to$aEvil$aNon-resistance to 700 $aTolstoy$b Leo, graf$f1828-1910$0159663 801 0$bUtSlPG 801 1$bIL-JeEL 906 $aBOOK 912 $a9910158886103321 996 $aA Letter to a Hindu$92597819 997 $aUNINA LEADER 04907nam 22007215 450 001 9910580174103321 005 20250628110034.0 010 $a981-19-3639-0 024 7 $a10.1007/978-981-19-3639-5 035 $a(CKB)5700000000100487 035 $a(MiAaPQ)EBC7028997 035 $a(Au-PeEL)EBL7028997 035 $a(OCoLC)1334995976 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/87693 035 $a(PPN)263902943 035 $a(DE-He213)978-981-19-3639-5 035 $a(ODN)ODN0010070551 035 $a(oapen)doab87693 035 $a(EXLCZ)995700000000100487 100 $a20220702d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHow Data Quality Affects our Understanding of the Earnings Distribution /$fby Reza Che Daniels 205 $a1st ed. 2022. 210 $d2022 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (xx, 114 pages) $cillustrations (some color) 311 1 $a981-19-3638-2 327 $aIntroduction -- A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys -- Questionnaire Design and Response Propensities for Labour Income Micro Data -- Univariate Multiple Imputation for Coarse Employee Income Data -- Conclusion: How Data Quality A?ects our Understanding of the Earnings Distribution. 330 $aThis open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error. The parameters of interest concern the earnings distribution, but despite this outcome of interest, the discussion is generalizable to any question in a random sample survey of households or firms. This book then investigates questionnaire design and item nonresponse by building a response propensity modelfor the employee income question in two South African labour market surveys: the October Household Survey (OHS, 1997-1999) and the Labour Force Survey (LFS, 2000-2003). This time period isolates a period of changing questionnaire design for the income question. Finally, this book is concerned with how to employee income data with a mixture of continuous data, bounded response data and nonresponse. A variable with this mixture of data types is called coarse data. Because the income question consists of two parts -- an initial, exact income question and a bounded income follow-up question -- the resulting statistical distribution of employee income is both continuous and discrete. The book shows researchers how to appropriately deal with coarse income data using multiple imputation. The take-home message from this book is that researchers have a responsibility to treat data quality concerns in a statistically sound manner, rather than making adjustments to public-use data in arbitrary ways, often underpinned by undefensible assumptions about an implicit unobservable loss function in the data. The demonstration of how this can be done provides a replicable concept map with applicable methods that can be utilised in any sample survey. . 606 $aStatistics 606 $aSampling (Statistics) 606 $aQuantitative research 606 $aStatistical Theory and Methods 606 $aSurvey Methodology 606 $aData Analysis and Big Data 606 $aMethodology of Data Collection and Processing 606 $aAfrican Economics 606 $aAfrican History 607 $aAfrica$xEconomic conditions 607 $aAfrica$xHistory 615 0$aStatistics. 615 0$aSampling (Statistics) 615 0$aQuantitative research. 615 14$aStatistical Theory and Methods. 615 24$aSurvey Methodology. 615 24$aData Analysis and Big Data. 615 24$aMethodology of Data Collection and Processing. 615 24$aAfrican Economics. 615 24$aAfrican History. 676 $a519.5 686 $aBUS069000$aHIS001000$aMAT029000$2bisacsh 700 $aDaniels$b Reza C$01881013 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910580174103321 996 $aHow Data Quality Affects our Understanding of the Earnings Distribution$94495311 997 $aUNINA