{"product_id":"survey-data-harmonization-in-the-social-sciences-9781119712176","title":"Survey Data Harmonization in the Social Sciences","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSurvey Data Harmonization in the Social Sciences An expansive and incisive overview of the practical uses of harmonization and its implications for data quality and costs In Survey Data Harmonization in the Social Sciences, a team of distinguished social science researchers delivers a comprehensive collection of ex-ante and ex-post harmonization methodologies in the context of specific longitudinal and cross-national survey projects. The book examines how ex-ante and ex-post harmonization work individually and in relation to one another, offering practical guidance on harmonization decisions in the preparation of new data infrastructure for comparative research. Contributions from experts in sociology, political science, demography, economics, health, and medicine are included, all of which give voice to discipline-specific and interdisciplinary views on methodological challenges inherent in harmonization. The authors offer perspectives from Europe and the United States, as well as Afr\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface and Acknowledgments xv\u003c\/p\u003e \u003cp\u003eAbout the Editors xvii\u003c\/p\u003e \u003cp\u003eAbout the Contributors xviii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Objectives and Challenges of Survey Data Harmonization 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKazimierz M. Slomczynski, Irina Tomescu-Dubrow, J. Craig Jenkins, and Christof Wolf\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 What is the Harmonization of Survey Data? 2\u003c\/p\u003e \u003cp\u003e1.2.1 Ex-ante, Input and Output, Survey Harmonization 3\u003c\/p\u003e \u003cp\u003e1.3 Why Harmonize Social Survey Data? 5\u003c\/p\u003e \u003cp\u003e1.3.1 Comparison and Equivalence 6\u003c\/p\u003e \u003cp\u003e1.4 Harmonizing Survey Data Across and Within Countries 7\u003c\/p\u003e \u003cp\u003e1.4.1 Harmonizing Across Countries 7\u003c\/p\u003e \u003cp\u003e1.4.2 Harmonizing Within the Country 8\u003c\/p\u003e \u003cp\u003e1.5 Sources of Knowledge for Survey Data Harmonization 8\u003c\/p\u003e \u003cp\u003e1.6 Challenges to Survey Harmonization 9\u003c\/p\u003e \u003cp\u003e1.6.1 Population Representation (Sampling Design) 10\u003c\/p\u003e \u003cp\u003e1.6.2 Instruments and Their Adaptation (Including Translation) 10\u003c\/p\u003e \u003cp\u003e1.6.3 Preparation for Interviewing (Including Pretesting) 11\u003c\/p\u003e \u003cp\u003e1.6.4 Fieldwork (Including Modes of Interviewing) 11\u003c\/p\u003e \u003cp\u003e1.6.5 Data Preparation (Including Building Data Files) 12\u003c\/p\u003e \u003cp\u003e1.6.6 Data Processing, Quality Controls, and Adjustments 12\u003c\/p\u003e \u003cp\u003e1.6.7 Data Dissemination 13\u003c\/p\u003e \u003cp\u003e1.7 Survey Harmonization and Standardization Processes 13\u003c\/p\u003e \u003cp\u003e1.8 Quality of the Input and the End-product of Survey Harmonization 14\u003c\/p\u003e \u003cp\u003e1.9 Relevance of Harmonization Methodology to the FAIR Data Principles 15\u003c\/p\u003e \u003cp\u003e1.10 Ethical and Legal Issues 15\u003c\/p\u003e \u003cp\u003e1.11 How to Read this Volume? 16\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Effects of Data Harmonization on the Survey Research Process 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRanjit K. Singh, Arnim Bleier, and Peter Granda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Part 1: Harmonization: Origins and Relation to Standardization 22\u003c\/p\u003e \u003cp\u003e2.2.1 Early Conceptions of Standardization and Harmonization 22\u003c\/p\u003e \u003cp\u003e2.2.2 Foundational Work of International Survey Programs 23\u003c\/p\u003e \u003cp\u003e2.2.3 The Growing Impact of Data Harmonization 23\u003c\/p\u003e \u003cp\u003e2.3 Part 2: Stakeholders and Division of Labor 25\u003c\/p\u003e \u003cp\u003e2.3.1 Stakeholders 26\u003c\/p\u003e \u003cp\u003e2.3.1.1 International Actors and Funding Agencies 26\u003c\/p\u003e \u003cp\u003e2.3.1.2 Data Producers 26\u003c\/p\u003e \u003cp\u003e2.3.1.3 Archives 27\u003c\/p\u003e \u003cp\u003e2.3.1.4 Data Users 27\u003c\/p\u003e \u003cp\u003e2.3.2 Toward an Integrative View on Harmonization 28\u003c\/p\u003e \u003cp\u003e2.3.2.1 Harmonization Cost 29\u003c\/p\u003e \u003cp\u003e2.3.2.2 Harmonization Quality 29\u003c\/p\u003e \u003cp\u003e2.3.2.3 Harmonization Fit 30\u003c\/p\u003e \u003cp\u003e2.3.2.4 Moving Forward 30\u003c\/p\u003e \u003cp\u003e2.4 Part 3: New Data Types, New Challenges 31\u003c\/p\u003e \u003cp\u003e2.4.1 Designed Data and Organic Data 31\u003c\/p\u003e \u003cp\u003e2.4.2 Stakeholders in the Collection of Organic Data 32\u003c\/p\u003e \u003cp\u003e2.4.2.1 Producers 32\u003c\/p\u003e \u003cp\u003e2.4.2.2 Archives 32\u003c\/p\u003e \u003cp\u003e2.4.2.3 Users 33\u003c\/p\u003e \u003cp\u003e2.4.2.4 Harmonization of Organic Data 33\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 33\u003c\/p\u003e \u003cp\u003eReferences 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Ex-ante harmonization of survey instruments and non-survey data 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Harmonization in the World Values Survey 41\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKseniya Kizilova, Jaime Diez-Medrano, Christian Welzel, and Christian Haerpfer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Applied Harmonization Methods 42\u003c\/p\u003e \u003cp\u003e3.3 Documentation and Quality Assurance 48\u003c\/p\u003e \u003cp\u003e3.4 Challenges to Harmonization 49\u003c\/p\u003e \u003cp\u003e3.5 Software Tools 51\u003c\/p\u003e \u003cp\u003e3.6 Recommendations 52\u003c\/p\u003e \u003cp\u003eReferences 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Harmonization in the Afrobarometer 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCarolyn Logan, Robert Mattes, and Francis Kibirige\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 57\u003c\/p\u003e \u003cp\u003e4.2 Core Principles 58\u003c\/p\u003e \u003cp\u003e4.3 Applied Harmonization Methods 60\u003c\/p\u003e \u003cp\u003e4.3.1 Sampling 60\u003c\/p\u003e \u003cp\u003e4.3.2 Training 61\u003c\/p\u003e \u003cp\u003e4.3.3 Fieldwork and Data Collection 62\u003c\/p\u003e \u003cp\u003e4.3.4 Questionnaire 62\u003c\/p\u003e \u003cp\u003e4.3.5 Translation 64\u003c\/p\u003e \u003cp\u003e4.3.6 Data Management 65\u003c\/p\u003e \u003cp\u003e4.3.7 Documentation 65\u003c\/p\u003e \u003cp\u003e4.4 Harmonization and Country Selection 66\u003c\/p\u003e \u003cp\u003e4.5 Software Tools and Harmonization 66\u003c\/p\u003e \u003cp\u003e4.6 Challenges to Harmonization 67\u003c\/p\u003e \u003cp\u003e4.6.1 Local Knowledge, Flexibility\/Adaptability, and the “Dictatorship of Harmonization” 68\u003c\/p\u003e \u003cp\u003e4.6.2 The Quality-Cost Trade-off and Implications for Harmonization 68\u003c\/p\u003e \u003cp\u003e4.6.3 Final Challenge: “Events” 69\u003c\/p\u003e \u003cp\u003e4.7 Recommendations 70\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Harmonization in the National Longitudinal Surveys of Youth (NLSY) 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eElizabeth Cooksey, Rosella Gardecki, Carole Lunney, and Amanda Roose\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 73\u003c\/p\u003e \u003cp\u003e5.2 Cross-Cohort Design 75\u003c\/p\u003e \u003cp\u003e5.3 Applied Harmonization 76\u003c\/p\u003e \u003cp\u003e5.4 Challenges to Harmonization 80\u003c\/p\u003e \u003cp\u003e5.5 Documentation and Quality Assurance 82\u003c\/p\u003e \u003cp\u003e5.6 Software Tools 84\u003c\/p\u003e \u003cp\u003e5.7 Recommendations and Some Concluding Thoughts 86\u003c\/p\u003e \u003cp\u003eReferences 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Harmonization in the Comparative Study of Electoral Systems (CSES) Projects 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eStephen Quinlan, Christian Schimpf, Katharina Blinzler, and Slaven Zivkovic\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introducing the CSES 89\u003c\/p\u003e \u003cp\u003e6.2 Harmonization Principles and Technical Infrastructure 91\u003c\/p\u003e \u003cp\u003e6.3 Ex-ante Input Harmonization 91\u003c\/p\u003e \u003cp\u003e6.3.1 Module Questionnaire 92\u003c\/p\u003e \u003cp\u003e6.3.2 Macro Data 94\u003c\/p\u003e \u003cp\u003e6.4 Ex-ante Output Harmonization 97\u003c\/p\u003e \u003cp\u003e6.4.1 Demographic Variables in CSES Modules 97\u003c\/p\u003e \u003cp\u003e6.4.2 Harmonizing Party Data in Modules 98\u003c\/p\u003e \u003cp\u003e6.4.3 Derivative Variables 99\u003c\/p\u003e \u003cp\u003e6.5 Exploring Interplay Between Ex-ante and Ex-post Harmonization 101\u003c\/p\u003e \u003cp\u003e6.5.1 Demographic Variables in CSES IMD 101\u003c\/p\u003e \u003cp\u003e6.5.2 Harmonizing Party Data in CSES IMD 102\u003c\/p\u003e \u003cp\u003e6.6 Taking Stock and New Frontiers in Harmonization 104\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Harmonization in the East Asian Social Survey 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNoriko Iwai, Tetsuo Mo, Jibum Kim, Chyi-In Wu, and Weidong Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 107\u003c\/p\u003e \u003cp\u003e7.2 Characteristics of the EASS and its Harmonization Process 108\u003c\/p\u003e \u003cp\u003e7.2.1 Outline of the East Asian Social Survey 108\u003c\/p\u003e \u003cp\u003e7.2.2 Harmonization Process of the EASS 111\u003c\/p\u003e \u003cp\u003e7.2.2.1 Establishing the Module Theme 111\u003c\/p\u003e \u003cp\u003e7.2.2.2 Selecting Subtopics and Questions 112\u003c\/p\u003e \u003cp\u003e7.2.2.3 Harmonization of Standard Background Variables 113\u003c\/p\u003e \u003cp\u003e7.2.2.4 Harmonization of Answer Choices and Scales 114\u003c\/p\u003e \u003cp\u003e7.2.2.5 Translation of Questions and Answer Choices 115\u003c\/p\u003e \u003cp\u003e7.3 Documentation and Quality Assurance 115\u003c\/p\u003e \u003cp\u003e7.3.1 Five Steps to Harmonize the EASS Integrated Data 115\u003c\/p\u003e \u003cp\u003e7.3.2 Documentation of the EASS Integrated Data 117\u003c\/p\u003e \u003cp\u003e7.4 Challenges to Harmonization 118\u003c\/p\u003e \u003cp\u003e7.4.1 How to Translate “Fair” and Restriction by Copyright 118\u003c\/p\u003e \u003cp\u003e7.4.2 Difficulty in Synchronizing the Data Collection Phase 121\u003c\/p\u003e \u003cp\u003e7.5 Software Tools 122\u003c\/p\u003e \u003cp\u003e7.6 Recommendations 122\u003c\/p\u003e \u003cp\u003eAcknowledgment 123\u003c\/p\u003e \u003cp\u003eReferences 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Ex-ante Harmonization of Official Statistics in Africa (SHaSA) 125\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDossina Yeo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 125\u003c\/p\u003e \u003cp\u003e8.1 Introduction 127\u003c\/p\u003e \u003cp\u003e8.2 Applied Harmonization Methods 128\u003c\/p\u003e \u003cp\u003e8.2.1 Examples of Ex-ante Harmonization Methods: The Cases of GPS Data and CRVS 131\u003c\/p\u003e \u003cp\u003e8.2.1.1 Governance, Peace and Security (GPS) Statistics Initiative 131\u003c\/p\u003e \u003cp\u003e8.2.1.2 Development of Civil Registration and Vital Statistics (CRVS) 132\u003c\/p\u003e \u003cp\u003e8.2.2 Examples of Ex-post Harmonization: The Cases of Labor Statistics, ATSY, ASY and KeyStats, and ICP-Africa Program 132\u003c\/p\u003e \u003cp\u003e8.3 Quality Assurance Framework 134\u003c\/p\u003e \u003cp\u003e8.4 Challenges to Statistical Harmonization in Africa 136\u003c\/p\u003e \u003cp\u003e8.4.1 Challenges to the Implementation of NSDS 137\u003c\/p\u003e \u003cp\u003e8.4.2 Challenges with Ex-ante Harmonization: Examples of GPS and ICP Initiatives 138\u003c\/p\u003e \u003cp\u003e8.4.3 Challenges with Ex-post Harmonization: Examples of KeyStats and ATSY 139\u003c\/p\u003e \u003cp\u003e8.5 Common Software Tools Used 139\u003c\/p\u003e \u003cp\u003e8.6 Conclusion and Recommendations 140\u003c\/p\u003e \u003cp\u003eReferences 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Ex-post harmonization of national social surveys 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Harmonization for Cross-National Secondary Analysis: Survey Data Recycling 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIrina Tomescu-Dubrow, Kazimierz M. Slomczynski, Ilona Wysmulek, Przemek Powałko, Olga Li, Yamei Tu, Marcin Slarzynski, Marcin W. Zielinski, and Denys Lavryk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 147\u003c\/p\u003e \u003cp\u003e9.2 Harmonization Methods in the SDR Project 149\u003c\/p\u003e \u003cp\u003e9.2.1 Building the Harmonized SDR2 Database 150\u003c\/p\u003e \u003cp\u003e9.3 Documentation and Quality Assurance 155\u003c\/p\u003e \u003cp\u003e9.4 Challenges to Harmonization 156\u003c\/p\u003e \u003cp\u003e9.5 Software Tools of the SDR Project 161\u003c\/p\u003e \u003cp\u003e9.5.1 The SDR Portal 161\u003c\/p\u003e \u003cp\u003e9.5.2 The SDR2 COTTON FILE 162\u003c\/p\u003e \u003cp\u003e9.6 Recommendations 162\u003c\/p\u003e \u003cp\u003e9.6.1 Recommendations for Researchers Interested in Harmonizing Survey Data Ex-Post 162\u003c\/p\u003e \u003cp\u003e9.6.2 Recommendations for SDR2 Users 163\u003c\/p\u003e \u003cp\u003eAcknowledgments 164\u003c\/p\u003e \u003cp\u003eReferences 164\u003c\/p\u003e \u003cp\u003e9.A Data Quality Indicators in SDR2 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Harmonization of Panel Surveys: The Cross-National Equivalent File 169\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDean R. Lillard\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 169\u003c\/p\u003e \u003cp\u003e10.2 Applied Harmonization Methods 170\u003c\/p\u003e \u003cp\u003e10.2.1 CNEF Country Data Sources, Current and Planned 176\u003c\/p\u003e \u003cp\u003e10.3 Current CNEF Partners 176\u003c\/p\u003e \u003cp\u003e10.3.1 The HILDA Survey  176\u003c\/p\u003e \u003cp\u003e10.3.2 The SLID  176\u003c\/p\u003e \u003cp\u003e10.3.3 The CFPS  177\u003c\/p\u003e \u003cp\u003e10.3.4 The SOEP  177\u003c\/p\u003e \u003cp\u003e10.3.4.1 The BHPS  177\u003c\/p\u003e \u003cp\u003e10.3.4.2 Understanding Society, UKHLS  178\u003c\/p\u003e \u003cp\u003e10.3.5 The ITA.LI 178\u003c\/p\u003e \u003cp\u003e10.3.6 The JHPS  178\u003c\/p\u003e \u003cp\u003e10.3.7 The RLMS-HSE  178\u003c\/p\u003e \u003cp\u003e10.3.8 The KLIPS  179\u003c\/p\u003e \u003cp\u003e10.3.9 The Swedish Pseudo-Panel 179\u003c\/p\u003e \u003cp\u003e10.3.10 The SHP  179\u003c\/p\u003e \u003cp\u003e10.3.11 The PSID  179\u003c\/p\u003e \u003cp\u003e10.4 Planned CNEF Partners 180\u003c\/p\u003e \u003cp\u003e10.4.1 The ASEP 180\u003c\/p\u003e \u003cp\u003e10.4.2 LISA  180\u003c\/p\u003e \u003cp\u003e10.4.3 The ILS 180\u003c\/p\u003e \u003cp\u003e10.4.4 The MxFLS  180\u003c\/p\u003e \u003cp\u003e10.4.5 The NIDS  181\u003c\/p\u003e \u003cp\u003e10.4.6 The PSFD  181\u003c\/p\u003e \u003cp\u003e10.5 Documentation and Quality Assurance 181\u003c\/p\u003e \u003cp\u003e10.6 Challenges to Harmonization 183\u003c\/p\u003e \u003cp\u003e10.7 Recommendations for Researchers Interested in Harmonizing Panel Survey Data 185\u003c\/p\u003e \u003cp\u003e10.8 Conclusion 186\u003c\/p\u003e \u003cp\u003eReferences 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Harmonization of Survey Data from UK Longitudinal Studies: CLOSER 189\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDara O’Neill and Rebecca Hardy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 189\u003c\/p\u003e \u003cp\u003e11.2 Applied Harmonization Methods 191\u003c\/p\u003e \u003cp\u003e11.2.1 Occupational Social Class 191\u003c\/p\u003e \u003cp\u003e11.2.2 Body Size\/Anthropometric Data 193\u003c\/p\u003e \u003cp\u003e11.2.3 Mental Health 194\u003c\/p\u003e \u003cp\u003e11.2.4 Harmonization Methods: Divergence and Convergence 195\u003c\/p\u003e \u003cp\u003e11.3 Documentation and Quality Assurance 196\u003c\/p\u003e \u003cp\u003e11.4 Challenges to Harmonization 198\u003c\/p\u003e \u003cp\u003e11.5 Software Tools 199\u003c\/p\u003e \u003cp\u003e11.6 Recommendations 200\u003c\/p\u003e \u003cp\u003eAcknowledgments 202\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Harmonization of Census Data: IPUMS – International 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSteven Ruggles, Lara Cleveland, and Matthew Sobek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 207\u003c\/p\u003e \u003cp\u003e12.2 Project History 208\u003c\/p\u003e \u003cp\u003e12.2.1 Evolution of the Web Dissemination System 210\u003c\/p\u003e \u003cp\u003e12.3 Applied Harmonization Methods 210\u003c\/p\u003e \u003cp\u003e12.4 Documentation and Quality Assurance 215\u003c\/p\u003e \u003cp\u003e12.5 Challenges to Harmonization 217\u003c\/p\u003e \u003cp\u003e12.6 Software Tools 221\u003c\/p\u003e \u003cp\u003e12.6.1 Metadata Tools 221\u003c\/p\u003e \u003cp\u003e12.6.2 Data Reformatting 221\u003c\/p\u003e \u003cp\u003e12.6.3 Data Harmonization 221\u003c\/p\u003e \u003cp\u003e12.6.4 Dissemination System 222\u003c\/p\u003e \u003cp\u003e12.7 Team Organization and Project Management 222\u003c\/p\u003e \u003cp\u003e12.8 Lessons and Recommendations 223\u003c\/p\u003e \u003cp\u003eReferences 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Domain-driven ex-post harmonization 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13 Maelstrom Research Approaches to Retrospective Harmonization of Cohort Data for Epidemiological Research 229\u003cbr\u003e \u003ci\u003eTina W. Wey and Isabel Fortier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 229\u003c\/p\u003e \u003cp\u003e13.2 Applied Harmonization Methods 230\u003c\/p\u003e \u003cp\u003e13.2.1 Implementing the Project 233\u003c\/p\u003e \u003cp\u003e13.2.1.1 Initiating Activities and Organizing the Operational Framework 233\u003c\/p\u003e \u003cp\u003e13.2.1.2 Assembling Study Information and Selecting Final Participating Studies (Guidelines Step 1) 234\u003c\/p\u003e \u003cp\u003e13.2.1.3 Defining Target Variables to be Harmonized (the DataSchema) and Evaluating Harmonization Potential across Studies (Guidelines Step 2) 235\u003c\/p\u003e \u003cp\u003e13.2.2 Producing the Harmonized Datasets 236\u003c\/p\u003e \u003cp\u003e13.2.2.1 Processing Data (Guidelines Step 3a) 236\u003c\/p\u003e \u003cp\u003e13.2.2.2 Processing Study-Specific Data to Generate Harmonized Datasets (Guidelines Step 3b) 237\u003c\/p\u003e \u003cp\u003e13.3 Documentation and Quality Assurance 238\u003c\/p\u003e \u003cp\u003e13.4 Challenges to Harmonization 240\u003c\/p\u003e \u003cp\u003e13.5 Software Tools 241\u003c\/p\u003e \u003cp\u003e13.6 Recommendations 243\u003c\/p\u003e \u003cp\u003eAcknowledgments 244\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003e14 Harmonizing and Synthesizing Partnership Histories from Different German Survey Infrastructures 249\u003cbr\u003e \u003ci\u003eBernd Weiß, Sonja Schulz, Lisa Schmid, Sebastian Sterl, and Anna-Carolina Haensch\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 249\u003c\/p\u003e \u003cp\u003e14.2 Applied Harmonization Methods 250\u003c\/p\u003e \u003cp\u003e14.2.1 Data Search Strategy and Data Access 250\u003c\/p\u003e \u003cp\u003e14.2.2 Processing and Harmonizing Data 253\u003c\/p\u003e \u003cp\u003e14.2.2.1 Harmonizing Partnership Biography Data 253\u003c\/p\u003e \u003cp\u003e14.2.2.2 Harmonizing Additional Variables on Respondents’ or Couples’ Characteristics 254\u003c\/p\u003e \u003cp\u003e14.3 Documentation and Quality Assurance 255\u003c\/p\u003e \u003cp\u003e14.3.1 Documentation 255\u003c\/p\u003e \u003cp\u003e14.3.2 Quality Assurance 256\u003c\/p\u003e \u003cp\u003e14.3.2.1 Process-Related Quality Assurance 256\u003c\/p\u003e \u003cp\u003e14.3.2.2 Benchmarking the Harmonized HaSpaD Data Set with Official Statistics 256\u003c\/p\u003e \u003cp\u003e14.4 Challenges to Harmonization 258\u003c\/p\u003e \u003cp\u003e14.4.1 Analyzing Harmonized Complex Survey Data 258\u003c\/p\u003e \u003cp\u003e14.4.2 Sporadically and Systematically Missing Data 259\u003c\/p\u003e \u003cp\u003e14.5 Software Tools 260\u003c\/p\u003e \u003cp\u003e14.6 Recommendations 262\u003c\/p\u003e \u003cp\u003e14.6.1 Harmonizing Biographical Data 262\u003c\/p\u003e \u003cp\u003e14.6.1.1 Methodological Recommendations 262\u003c\/p\u003e \u003cp\u003e14.6.1.2 Procedural Recommendations 263\u003c\/p\u003e \u003cp\u003e14.6.1.3 Technical Recommendations 263\u003c\/p\u003e \u003cp\u003e14.6.2 Getting Started with the Cumulative HaSpaD Data Set 263\u003c\/p\u003e \u003cp\u003eAcknowledgments 264\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Harmonization and Quality Assurance of Income and Wealth Data: The Case of LIS 269\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJörg Neugschwender, Teresa Munzi, and Piotr R. Paradowski\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 269\u003c\/p\u003e \u003cp\u003e15.2 Applied Harmonization Methods 271\u003c\/p\u003e \u003cp\u003e15.3 Documentation and Quality Assurance 275\u003c\/p\u003e \u003cp\u003e15.3.1 Quality Assurance 275\u003c\/p\u003e \u003cp\u003eSelection of Source Datasets 276\u003c\/p\u003e \u003cp\u003eHarmonization 276\u003c\/p\u003e \u003cp\u003eValidation – “Green Light” Check 276\u003c\/p\u003e \u003cp\u003e15.3.2 Documentation 278\u003c\/p\u003e \u003cp\u003e15.4 Challenges to Harmonization 278\u003c\/p\u003e \u003cp\u003e15.5 Software Tools 281\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 282\u003c\/p\u003e \u003cp\u003eReferences 283\u003cbr\u003e \u003cb\u003e\u003ci\u003e16 Ex-Post Harmonization of Time Use Data: Current Practices and Challenges in the Field 285\u003cbr\u003e \u003c\/i\u003e\u003c\/b\u003e\u003ci\u003eEwa Jarosz, Sarah Flood, and Margarita Vega-Rapun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 285\u003c\/p\u003e \u003cp\u003e16.2 Applied Harmonization Methods 289\u003c\/p\u003e \u003cp\u003e16.2.1 Harmonizing the Matrix of the Diary 289\u003c\/p\u003e \u003cp\u003e16.2.2 Variable Harmonization 291\u003c\/p\u003e \u003cp\u003e16.2.3 Other Variables 293\u003c\/p\u003e \u003cp\u003e16.2.4 Other Types of Time Use Data 294\u003c\/p\u003e \u003cp\u003e16.3 Documentation and Quality Assurance 294\u003c\/p\u003e \u003cp\u003e16.3.1 Documentation 294\u003c\/p\u003e \u003cp\u003e16.3.2 Quality Checks 296\u003c\/p\u003e \u003cp\u003e16.4 Challenges to Harmonization 297\u003c\/p\u003e \u003cp\u003e16.5 Software Tools 300\u003c\/p\u003e \u003cp\u003e16.6 Recommendations 301\u003c\/p\u003e \u003cp\u003eReferences 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Further Issues: Dealing with Methodological Issues in Harmonized Survey Data 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Assessing and Improving the Comparability of Latent Construct Measurements in Ex-Post Harmonization 307\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eRanjit K. Singh and Markus Quandt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 307\u003c\/p\u003e \u003cp\u003e17.2 Measurement and Reality 307\u003c\/p\u003e \u003cp\u003e17.3 Construct Match 308\u003c\/p\u003e \u003cp\u003e17.3.1 Consequences of a Mismatch 309\u003c\/p\u003e \u003cp\u003e17.3.2 Assessment 309\u003c\/p\u003e \u003cp\u003e17.3.2.1 Qualitative Research Methods 309\u003c\/p\u003e \u003cp\u003e17.3.2.2 Construct and Criterion Validity 309\u003c\/p\u003e \u003cp\u003e17.3.2.3 Techniques for Multi-Item Instruments 310\u003c\/p\u003e \u003cp\u003e17.3.2.4 Improving Construct Comparability 311\u003c\/p\u003e \u003cp\u003e17.4 Reliability Differences 311\u003c\/p\u003e \u003cp\u003e17.4.1 Consequences of Reliability Differences 311\u003c\/p\u003e \u003cp\u003e17.4.2 Assessment 312\u003c\/p\u003e \u003cp\u003e17.4.3 Improving Reliability Comparability 312\u003c\/p\u003e \u003cp\u003e17.5 Units of Measurement 312\u003c\/p\u003e \u003cp\u003e17.5.1 Consequences of Unit Differences 313\u003c\/p\u003e \u003cp\u003e17.5.2 Improving Unit Comparability 313\u003c\/p\u003e \u003cp\u003e17.5.3 Controlling for Instrument Characteristics 314\u003c\/p\u003e \u003cp\u003e17.5.4 Harmonizing Units Based on Repeated Measurements 315\u003c\/p\u003e \u003cp\u003e17.5.5 Harmonizing Units Based on Measurements Obtained from the Same Population 315\u003c\/p\u003e \u003cp\u003e17.6 Cross-Cultural Comparability 316\u003c\/p\u003e \u003cp\u003e17.6.1 Construct Match 316\u003c\/p\u003e \u003cp\u003e17.6.1.1 Translation and Cognitive Probing 317\u003c\/p\u003e \u003cp\u003e17.6.2 Reliability 317\u003c\/p\u003e \u003cp\u003e17.6.3 Units of Measurement 318\u003c\/p\u003e \u003cp\u003e17.6.3.1 Harmonizing Units of Localized Versions of the Same Instrument 318\u003c\/p\u003e \u003cp\u003e17.6.3.2 Harmonizing Units Across Cultures and Instruments 318\u003c\/p\u003e \u003cp\u003e17.6.4 Cross-Cultural Comparability of Multi-Item Instruments 318\u003c\/p\u003e \u003cp\u003e17.7 Discussion and Outlook 319\u003c\/p\u003e \u003cp\u003eReferences 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Comparability and Measurement Invariance 323\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArtur Pokropek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Latent Variable Framework for Testing and Accounting for Measurement Non-Invariance 324\u003c\/p\u003e \u003cp\u003e18.2 Approaches to Empirical Assessment of Measurement Equivalence 325\u003c\/p\u003e \u003cp\u003e18.2.1 Classical Invariance Analysis (MG-CFA) 326\u003c\/p\u003e \u003cp\u003e18.2.2 Partial Invariance (MG-CFA) 327\u003c\/p\u003e \u003cp\u003e18.2.3 Approximate Invariance 327\u003c\/p\u003e \u003cp\u003e18.2.4 Approximate Partial Invariance (Alignment, BSEM Alignment, Partial BSEM) 328\u003c\/p\u003e \u003cp\u003e18.3 Beyond Multiple Indicators 329\u003c\/p\u003e \u003cp\u003e18.4 Conclusions 329\u003c\/p\u003e \u003cp\u003eReferences 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 On the Creation, Documentation, and Sensible Use of Weights in the Context of Comparative Surveys 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDominique Joye, Marlène Sapin, and Christof Wolf\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 333\u003c\/p\u003e \u003cp\u003e19.2 Design Weights 335\u003c\/p\u003e \u003cp\u003e19.2.1 What to do? 336\u003c\/p\u003e \u003cp\u003e19.3 Post-stratification Weights 337\u003c\/p\u003e \u003cp\u003e19.3.1 What Should be Done? 340\u003c\/p\u003e \u003cp\u003e19.4 Population Weights 341\u003c\/p\u003e \u003cp\u003e19.4.1 What Should be Done? 342\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 342\u003c\/p\u003e \u003cp\u003eReferences 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 On Using Harmonized Data in Statistical Analysis: Notes of Caution 347\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eClaire Durand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 347\u003c\/p\u003e \u003cp\u003e20.2 Challenges in the Combination of Data Sets 347\u003c\/p\u003e \u003cp\u003e20.2.1 A First Principle: A No Censorship Inclusive Approach 348\u003c\/p\u003e \u003cp\u003e20.2.2 A Second Principle: Using Multilevel Analysis and Introducing a Measurement Level 349\u003c\/p\u003e \u003cp\u003e20.2.3 A Third Principle: Assessing the Equivalence of Survey Projects 351\u003c\/p\u003e \u003cp\u003e20.3 Challenges in the Analysis of Combined Data Sets 353\u003c\/p\u003e \u003cp\u003e20.3.1 Dealing with Time 354\u003c\/p\u003e \u003cp\u003e20.3.2 Dealing with Missing Values 358\u003c\/p\u003e \u003cp\u003e20.3.2.1 Missing Values at the Respondent and Measurement Level 358\u003c\/p\u003e \u003cp\u003e20.3.2.2 Missing Values at the Survey Level 359\u003c\/p\u003e \u003cp\u003e20.3.3 Dealing with Weights 361\u003c\/p\u003e \u003cp\u003e20.4 Recommendations 362\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 On the Future of Survey Data Harmonization 367\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKazimierz M. Slomczynski, Christof Wolf, Irina Tomescu-Dubrow, and J. Craig Jenkins\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 What We Have Learned from Contributions on Survey Data Harmonization in this Volume 368\u003c\/p\u003e \u003cp\u003e21.2 New Opportunities and Challenges 370\u003c\/p\u003e \u003cp\u003e21.2.1 Reorientation of Survey Research in the Era of New Technology 370\u003c\/p\u003e \u003cp\u003e21.2.2 Advances in Technical Aspects of Data Management 370\u003c\/p\u003e \u003cp\u003e21.2.3 Harmonizing Survey Data with Other Types of Data 371\u003c\/p\u003e \u003cp\u003e21.3 Developing a New Methodology of Harmonizing Non-Survey Data 372\u003c\/p\u003e \u003cp\u003e21.3.1 Emerging Legal and Ethical Issues 372\u003c\/p\u003e \u003cp\u003e21.4 Globalization of Science and Harmonizing Scientific Practice 373\u003c\/p\u003e \u003cp\u003eReferences 373\u003c\/p\u003e \u003cp\u003eIndex 377\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":51039267782999,"sku":"9781119712176","price":88.65,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119712176.jpg?v=1750943107","url":"https:\/\/bookcurl.com\/products\/survey-data-harmonization-in-the-social-sciences-9781119712176","provider":"Book Curl","version":"1.0","type":"link"}