@incollection{AssmannSteinhauerWuerbachetal., author = {Aßmann, Christian and Steinhauer, Hans Walter and W{\"u}rbach, Ariane and Zinn, Sabine and Hammon, Angelina and Kiesl, Hans and Rohwer, G{\"o}tz and R{\"a}ssler, Susanne and Blossfeld, Hans-Peter}, title = {Sampling Designs of the National Educational Panel Study: Setup and Panel Development}, series = {Education as a Lifelong Process}, booktitle = {Education as a Lifelong Process}, editor = {Blossfeld, Hans-Peter and Roßbach, Hans-G{\"u}nther}, publisher = {Springer Fachmedien Wiesbaden}, address = {Wiesbaden}, isbn = {978-3-658-23161-3}, doi = {10.1007/978-3-658-23162-0_3}, pages = {35 -- 55}, abstract = {The German National Educational Panel Study (NEPS) was set up to provide an empirical basis for longitudinal analyses of individuals' educational careers and competencies and how they unfold over the life course in relation to family, formal educational institutions, and private life. Educational developments and decisions over the life span are being tracked in six starting cohorts as a foundation for characterizing and analyzing educational processes. These six starting cohorts include newborns, Kindergarten children, secondary school children (5th and 9th grade), first-year undergraduate students, and adults. Because access to the target population in several starting cohorts was gained via educational institutions such as Kindergartens and schools, multistage sampling approaches were implemented that reflect the clustered structure of the target populations. Samples in individual contexts, such as those in the adult and newborn cohorts, were established via register-based stratified cluster approaches. This chapter briefly reviews the designs of the implemented sampling strategies for each established starting cohort and provides information on the levels of attrition in the panel development.}, language = {en} } @misc{WeberDendorferSuessetal., author = {Weber, Karsten and Dendorfer, Sebastian and S{\"u}ß, Franz and Kubowitsch, Simone and Schratzenstaller, Thomas and Haug, Sonja and Mohr, Christa and Kiesl, Hans and Drechsler, J{\"o}rg and Westner, Markus and Kobus, J{\"o}rn and Schubert, Martin J. W. and Zenger, Stefan and Pietsch, Alexander and Weiß, Josef and Hinterseer, Sebastian and Schieck, Roland and Scherzinger, Stefanie and Klettke, Meike and Ringlstetter, Andreas and St{\"o}rl, Uta and Bissyand{\´e}, Tegawend{\´e} F. and Seeburger, Achim and Schindler, Timo and Ramsauer, Ralf and Kiszka, Jan and K{\"o}lbl, Andreas and Lohmann, Daniel and Mauerer, Wolfgang and Maier, Johannes and Scorna, Ulrike and Palm, Christoph and Soska, Alexander and Mottok, J{\"u}rgen and Ellermeier, Andreas and V{\"o}gele, Daniel and Hierl, Stefan and Briem, Ulrich and Buschmann, Knut and Ehrlich, Ingo and Pongratz, Christian and Pielmeier, Benjamin and Tyroller, Quirin and Monkman, Gareth J. and Gut, Franz and Roth, Carina and Hausler, Peter and Bierl, Rudolf and Prommesberger, Christian and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Huang, Yifeng and She, Juncong and Ottl, Andreas and Rieger, Walter and Kraml, Agnes and Poxleitner, Thomas and Hofer, Simon and Heisterkamp, Benjamin and Lerch, Maximilian and Sammer, Nike and Golde, Olivia and Wellnitz, Felix and Schmid, Sandra and Muntschick, Claudia and Kusterle, Wolfgang and Paric, Ivan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Schmidt, Ottfried and Schwanzer, Peter and Rabl, Hans-Peter and Sterner, Michael and Bauer, Franz and Steinmann, Sven and Eckert, Fabian and Hofrichter, Andreas}, title = {Forschungsbericht 2017}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-3-5}, doi = {10.35096/othr/pub-1383}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13835}, subject = {Forschung}, language = {de} } @incollection{Kiesl, author = {Kiesl, Hans}, title = {Gewichtung}, series = {Handbuch Methoden der empirischen Sozialforschung}, booktitle = {Handbuch Methoden der empirischen Sozialforschung}, editor = {Baur, Nina and Blasius, J{\"o}rg}, publisher = {Springer Nature}, doi = {10.1007/978-3-658-37985-8_30}, pages = {473 -- 481}, abstract = {Ziel einer Analyse quantitativer Daten ist die Verallgemeinerung der Stichprobenergebnisse auf die interessierende Grundgesamtheit (H{\"a}der/H{\"a}der, Kapitel 27 in diesem Band). Tats{\"a}chlich unterscheiden sich Stichproben in bestimmter Hinsicht aber fast immer von der Grundgesamtheit; sei es durch ein geplantes „Oversampling" einer bestimmten Teilpopulation (die Genauigkeit einer Sch{\"a}tzung wird im Wesentlichen von der Fallzahl in der Stichprobe bestimmt, weshalb seltene Teilpopulationen, f{\"u}r die valide Sch{\"a}tzungen m{\"o}glich sein sollen, mit einem gr{\"o}ßeren Auswahlsatz in die Erhebung aufgenommen werden) oder durch selektiven Nonresponse (Engel/Schmidt, Kapitel 29 in diesem Band). Viele Befragungen weisen etwa einen so genannten „Mittelschichtsbias" auf; Personen mit mittlerem bis gehobenem Bildungsniveau (gemessen durch den h{\"o}chsten Schulabschluss) zeigen sich am {\"o}ftesten bereit, an Umfragen teilzunehmen, sie sind in den Erhebungsdaten daher {\"u}berrepr{\"a}sentiert.}, language = {de} } @article{BleningerFuernrohrKiesletal., author = {Bleninger, Sara and F{\"u}rnrohr, Michael and Kiesl, Hans and Kr{\"a}mer, Walter and K{\"u}chenhoff, Helmut and Burgard, Jan Pablo and M{\"u}nnich, Ralf and Rupp, Martin}, title = {Kommentare und Erwiderung zu: Qualit{\"a}tszielfunktionen f{\"u}r stark variierende Gemeindegr{\"o}ßen im Zensus 2021}, series = {AStA Wirtschafts- und Sozialstatistisches Archiv}, volume = {14}, journal = {AStA Wirtschafts- und Sozialstatistisches Archiv}, publisher = {Springer Nature}, doi = {10.1007/s11943-019-00264-6}, pages = {67 -- 98}, abstract = {Burgard et al. (2020) stellen in ihrem Artikel zu Qualit{\"a}tszielfunktionen f{\"u}r stark variierende Gemeindegr{\"o}ßen im Zensus 2021 Erweiterungen der Stichproben- und Sch{\"a}tzmethoden des Zensus 2011 vor, die kleine Gemeinden unter 10.000 Einwohnern in den Entscheidungsprozess integrieren. Die Dringlichkeit zur L{\"o}sung dieses Problems wurde ebenso im Urteil des Bundesverfassungsgerichts zur Volksz{\"a}hlung 2011 festgestellt. Ziel dieser Erwiderung ist eine eingehende Diskussion der Ergebnisse des vorangegangenen Beitrags mit namhaften Experten auf diesem Gebiet. Insbesondere geht es um eine Einordnung des Artikels in den Wissenschaftskontext (Kr{\"a}mer), die Bedeutung von Nichtstichprobenfehlern f{\"u}r den Zensus (K{\"u}chenhoff), den Zensus aus Sicht der Amtsstatistik (Bleninger und F{\"u}rnrohr) sowie aus statistisch-methodischer Sicht (Kiesl). Dar{\"u}ber hinaus werden aktuelle Entwicklungen vorgestellt.}, language = {de} } @incollection{Kiesl, author = {Kiesl, Hans}, title = {Gewichtung}, series = {Handbuch Methoden der empirischen Sozialforschung}, volume = {29}, booktitle = {Handbuch Methoden der empirischen Sozialforschung}, editor = {Baur, Nina and Blasius, J{\"o}rg}, publisher = {Springer Fachmedien}, address = {Wiesbaden}, isbn = {978-3-658-21307-7}, doi = {10.1007/978-3-658-21308-4_28}, pages = {405 -- 412}, abstract = {Ziel einer Analyse quantitativer Daten ist die Verallgemeinerung der Stichprobenergebnisse auf die interessierende Grundgesamtheit (H{\"a}der/H{\"a}der, Kapitel 22 in diesem Band). Tats{\"a}chlich unterscheiden sich Stichproben in bestimmter Hinsicht aber fast immer von der Grundgesamtheit; sei es durch ein geplantes „Oversampling" einer bestimmten Teilpopulation (die Genauigkeit einer Sch{\"a}tzung wird im Wesentlichen von der Fallzahl in der Stichprobe bestimmt, weshalb seltene Teilpopulationen, f{\"u}r die valide Sch{\"a}tzungen m{\"o}glich sein sollen, mit einem gr{\"o}ßeren Auswahlsatz in die Erhebung aufgenommen werden) oder durch selektiven Nonresponse (Engel/Schmidt, Kapitel 27 in diesem Band). Viele Befragungen weisen etwa einen so genannten „Mittelschichtsbias" auf; Personen mit mittlerem bis gehobenem Bildungsniveau (gemessen durch den h{\"o}chsten Schulabschluss) zeigen sich am {\"o}ftesten bereit, an Umfragen teilzunehmen, sie sind in den Erhebungsdaten daher {\"u}berrepr{\"a}sentiert.}, language = {de} } @article{DrechslerKieslMeinfelderetal., author = {Drechsler, J{\"o}rg and Kiesl, Hans and Meinfelder, Florian and Raghunathan, Trivellore E. and Rubin, Donald B. and Schenker, Nathaniel and Zell, Elizabeth R.}, title = {In Memory of Professor Susanne R{\"a}ssler}, series = {Journal of Official Statistics}, volume = {35}, journal = {Journal of Official Statistics}, number = {1}, publisher = {de Gruyter}, doi = {10.2478/jos-2019-0013}, pages = {285 -- 286}, language = {en} } @article{Kiesl, author = {Kiesl, Hans}, title = {Indirect Sampling: A Review of Theory and Recent Applications}, series = {AStA Wirtschafts- und Sozialstatistisches Archiv}, volume = {10}, journal = {AStA Wirtschafts- und Sozialstatistisches Archiv}, number = {4}, publisher = {Springer}, doi = {10.1007/s11943-016-0183-3}, pages = {289 -- 303}, abstract = {Survey practitioners regularly face the task to draw a sample from a (sub-) population for which no sampling frame exists. Indirect sampling might be a way out in such situations, given that connections exist between the target population and another population for which probability sampling is feasible. While the theory of indirect sampling originated in the context of household panel studies, a wider area of applications emerged during the last decade. We first give a short review of the theory of indirect sampling, show that estimators from indirect samples might have smaller variance than the corresponding direct estimators (contrary to some claims in the literature), summarize recent applications and discuss some issues that are relevant for applying indirect sampling in practice. We also present some theory for unbiased estimation after an additional subsampling stage that was necessary for sampling kindergarten children in the German National Educational Panel Study (NEPS).}, language = {en} } @article{RoemmeleMendelBarrettetal., author = {R{\"o}mmele, Christoph and Mendel, Robert and Barrett, Caroline and Kiesl, Hans and Rauber, David and R{\"u}ckert, Tobias and Kraus, Lisa and Heinkele, Jakob and Dhillon, Christine and Grosser, Bianca and Prinz, Friederike and Wanzl, Julia and Fleischmann, Carola and Nagl, Sandra and Schnoy, Elisabeth and Schlottmann, Jakob and Dellon, Evan S. and Messmann, Helmut and Palm, Christoph and Ebigbo, Alanna}, title = {An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, publisher = {Nature Portfolio}, address = {London}, doi = {10.1038/s41598-022-14605-z}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-46928}, pages = {10}, abstract = {The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.}, language = {en} }