[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120635-en":3,"doc-seo-120635-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120635,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning Applications to Survey Nonresponse - Inaugural Dissertation","This inaugural dissertation examines how machine learning can improve survey methodology by mitigating biased inferences caused by low response rates in general population surveys. It addresses nonresponse bias arising when respondents differ systematically from nonrespondents. Using predictive models, the research develops and evaluates approaches including longitudinal nonresponse prediction with time series machine learning, pre-trained nonresponse prediction in panel surveys, prediction-based adaptive designs to reduce attrition, and a machine-learning case study for electoral seat prediction.","Machine Learning Applications to Survey  \nNonresponse  \nInaugural Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Social Sciences in the Graduate School of Economic and Social Sciences at the University of Mannheim  \nBy John ’Jack’ Collins  \nNovember 2025  \nFull-time Dean of the Faculty of Social Sciences:  \nDr. Julian Dierkes  \nPrimary supervisor: Prof. Dr. Christoph Kern  \nSecondary supervisor:  \nProf. Dr. Florian Keusch  \nPrimary reviewer: Prof. Dr. Florian Keusch  \nSecondary reviewer: Prof. Dr. Frauke Kreuter  \nDay of the disputation:  \nNovember 3rd 2025  \nAcknowledgments  \nIn the making of this dissertation, much gratitude is owed to many.  \nFirstly, Jun.-Prof. Dr. Christoph Kern trusted me with his research project through which most of the papers that comprise this thesis were published. Thanks also to Christoph for his excellent mentorship. Almost any PhD-closer does so thanks to their supervisors, but I am especially lucky to have had the benefit of Christoph’s thoughtful and ever-constructive advice as well as his co-authorship through which I could learn straight from the master.  \nWarm thanks also to Prof. Florian Keusch and Prof. Frauke Kreuter for manythings. They have agreed to review this dissertation, and moreover provided a superbly stimulating research environment. Florian has always been open and available with excellent advice on the art of research to myself and all Sociologists in the Universit¨at Mannheim school. Thankyou, Florian, for always having an answer to my endless queue of ”quick questions.” I’m indebted to Frauke for making me a part of her research group through which I’ve gotten to see some of the best research in this area as well as to see how top-tier academics go to work. Thanks to Frauke also for putting faith in me to bea part of the first ’Data Science for Social Good X’ in Munich.  \nI’m very pleased with all of my papers and that is much in thanks to my teammates at GESIS. It is thanks to this team that I was able to work on the project ”Predictionbased Adaptive Designs for Panel Surveys (PrADePS),” which yielded three of this dissertation’s four papers. Also, I was able to see how experienced practitioners like Tobias Gummer and Bernd Weiß go about the business of survey research. Not least, it has been amazing to go through my PhD in tandem with teammate Saskia Bartholom¨aus, not just because of her comradeship, but also because her work taught me much about survey research.  \nAll across the University of Mannheim and the Ludwig Maximilian University of Munich, brilliant people helped me with reviews, feedback, ideas, and even just encouragement and advice. It’s been a privilege to have such august peers.  \nFinally, I would not have had this adventure of a lifetime if not for my wife, who is my best friend and chiefest of all supporters.  \nThankyou, Dr. Rajika Kuruwita.  .  \nContents  \n1 Introduction 1  \n1.1 Survey Research and Nonresponse Bias .................... 1  \n1.2 What is Machine Learning? .......................... 7  \n1.3 Machine Learning Applications to Nonresponse ............... 10  \n1.4 This Dissertation’s Contributions to Survey Methodology ......... 13  \n1.5 References .................................... 17  \n2 Longitudinal Nonresponse Prediction with Time Series Machine Learning 22  \n2.1 Introduction ................................... 23  \n2.2 Background ................................... 25  \n2.3 Methodology .................................. 31  \n2.4 Results ...................................... 38  \n2.5 Discussion .................................... 44  \n2.6 Appendices ................................... 48  \n2.7 References .................................... 81  \n3 Pre-Trained Nonresponse Prediction in Panel Surveys with Machine Learning 86  \n3.1 Introduction ................................... 86  \n3.2 Background ................................... 88  \n3.3 Methods ..................................... 91  \n","cbCaiiw7fxdVhyyt","https://ap.wps.com/l/cbCaiiw7fxdVhyyt","pdf",14045076,1,222,"English","en",105,"# Introduction\n## Survey Research and Nonresponse Bias\n## What is Machine Learning?\n## Machine Learning Applications to Nonresponse\n## This Dissertation’s Contributions to Survey Methodology\n# Longitudinal Nonresponse Prediction with Time Series Machine Learning\n## Introduction\n## Background\n## Methodology\n## Results\n## Discussion\n# Pre-Trained Nonresponse Prediction in Panel Surveys with Machine Learning\n## Introduction\n## Background\n## Methods\n## Results\n## Discussion\n# Prediction-Based Adaptive Designs for Reducing Attrition Rates and Bias in Panel Surveys\n## Introduction\n## Methods\n## Results\n## Discussion\n# Predicting Australian Federal Electoral Seats with Machine Learning\n## Introduction\n## Methods\n## Results\n## Discussion\n# Conclusion","[{\"question\":\"How does nonresponse bias affect general population surveys?\",\"answer\":\"Nonresponse bias occurs when respondents differ systematically from nonrespondents regarding the study topic, causing samples to be biased toward participant characteristics and undermining accurate population inferences.\"},{\"question\":\"Why is machine learning suited for predicting survey participation?\",\"answer\":\"Machine learning develops algorithms that predict outcomes based on historical data, enabling models to learn patterns in survey data and predict individual tendencies to participate.\"},{\"question\":\"What kinds of ML-based approaches does the dissertation investigate?\",\"answer\":\"It studies longitudinal nonresponse prediction with time series ML, pre-trained nonresponse prediction in panel surveys, prediction-based adaptive designs to reduce attrition and bias, and an electoral-seat prediction case using ML.\"}]","Machine Learning Applications to Survey Nonresponse - Inaugural Dissertation | PDF",1785731015,559,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-applications-to-survey-nonresponse-inaugural-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-applications-to-survey-nonresponse-inaugural-dissertation/120635/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does nonresponse bias affect general population surveys?","Question",{"text":75,"@type":76},"Nonresponse bias occurs when respondents differ systematically from nonrespondents regarding the study topic, causing samples to be biased toward participant characteristics and undermining accurate population inferences.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is machine learning suited for predicting survey participation?",{"text":80,"@type":76},"Machine learning develops algorithms that predict outcomes based on historical data, enabling models to learn patterns in survey data and predict individual tendencies to participate.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of ML-based approaches does the dissertation investigate?",{"text":84,"@type":76},"It studies longitudinal nonresponse prediction with time series ML, pre-trained nonresponse prediction in panel surveys, prediction-based adaptive designs to reduce attrition and bias, and an electoral-seat prediction case using ML.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]