LEADER 03199nam 2200445 450 001 9910427042203321 005 20210302094722.0 010 $a1-4842-6103-8 024 7 $a10.1007/978-1-4842-6103-3 035 $a(CKB)4100000011479449 035 $a(MiAaPQ)EBC6362739 035 $a(DE-He213)978-1-4842-6103-3 035 $a(CaSebORM)9781484261033 035 $a(PPN)255961537 035 $a(EXLCZ)994100000011479449 100 $a20210302d2020 uy 0 101 0 $aeng 135 $aurcn| ||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCreating good data $ea guide to dataset structure and data representation /$fHarry J. Foxwell 205 $a1st ed. 2020. 210 1$a[Place of publication not identified] :$cApress,$d[2020] 210 4$dİ2020 215 $a1 online resource (112 pages) 311 $a1-4842-6102-X 327 $aChapter 1: The Need for Good Data -- Chapter 2: Basic Data Types and When to Use Them -- Chapter 3: Representing Quantitative Data -- Chapter 4: Planning Your Data Collection and Analysis -- Chapter 5: Good Datasets -- Chapter 6: Good Data Collection -- Chapter 7: Dataset Examples and Use Cases -- Chapter 8: Cleaning your Data -- Chapter 9: Good Data Anayltics -- Appendix A: Recommended Reading. 330 $aCreate good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data. Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed. This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected. You will: Be aware of the principles of creating and collecting data Know the basic data types and representations Select data types, anticipating analysis goals Understand dataset structures and practices for analyzing and sharing Be guided by examples and use cases (good and bad) Use cleaning tools and methods to create good data. 606 $aElectronic data processing$xData preparation 615 0$aElectronic data processing$xData preparation. 676 $a005.72 700 $aFoxwell$b Harry J.$0955410 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427042203321 996 $aCreating good data$92161893 997 $aUNINA LEADER 03133oam 22007215 450 001 9910784066303321 005 20200520144314.0 010 $a1-280-65432-5 010 $a9786610654321 010 $a0-8213-6879-6 024 7 $a10.1596/978-0-8213-6878-7 035 $a(CKB)1000000000256968 035 $a(EBL)459632 035 $a(OCoLC)77798047 035 $a(SSID)ssj0000086567 035 $a(PQKBManifestationID)11367487 035 $a(PQKBTitleCode)TC0000086567 035 $a(PQKBWorkID)10054778 035 $a(PQKB)11513987 035 $a(MiAaPQ)EBC459632 035 $a(Au-PeEL)EBL459632 035 $a(CaPaEBR)ebr10146799 035 $a(CaONFJC)MIL65432 035 $a(OCoLC)935270912 035 $a(The World Bank)ocm72161847 035 $a(US-djbf)14583099 035 $a(EXLCZ)991000000000256968 100 $a20061004d2007 uf 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnergy policies and multitopic household surveys : $eguidelines for questionnaire design in living standards measurement studies /$fKyran O'Sullivan and Douglas F. Barnes 210 1$aWashington, D.C. :$cWorld Bank,$dc2007. 215 $avii, 52 pages $cillustrations ;$d26 cm 225 0 $aWorld bank working paper ;$vno. 90 300 $aDescription based upon print version of record. 311 $a0-8213-6878-8 320 $aIncludes bibliographical references. 327 $aContents; Foreword; Acknowledgments; 1. Introduction; 2. Characteristics of LSMS Surveys and Specialized Household Energy Surveys; 3. Importance of LSMS Survey Data for Energy Policy Analysis; LIST OF TABLES; LIST OF FIGURES; 4. The Way Forward; LIST OF BOX; 5. Conclusion; Appendix; References 330 $aThe Energy Sector Management Assistance Program (ESMAP) is a global technical assistance program that promotes the role of energy in poverty reduction and economic growth with redistribution. ESMAP undertakes analytical work and provides policy advice on sustainable energy development to governments and other institutions in developing countries and economies in transition. ESMAP was established in 1983 under the joint sponsorship of the World Bank and the United Nations Development Programme as a partnership in response to global energycrises. Since its creation, ESMAP has operated in some 10 410 0$aWorld Bank e-Library. 606 $aCost and standard of living 606 $aHousehold surveys 606 $aHouseholds$xEnergy consumption 615 0$aCost and standard of living. 615 0$aHousehold surveys. 615 0$aHouseholds$xEnergy consumption. 676 $a333.79/63130723 700 $aO'Sullivan$b Kyran$f1954-$01539345 701 $aBarnes$b Douglas F$01522849 801 0$bDLC 801 1$bDLC 801 2$bYDX 801 2$bBAKER 801 2$bYDXCP 801 2$bBTCTA 801 2$bDLC 906 $aBOOK 912 $a9910784066303321 996 $aEnergy policies and multitopic household surveys$93790231 997 $aUNINA