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      Zur Anpassungsfähigkeit von Weiterbildungsanbietern in der Corona-Pandemie Translated title: On the adaptability of continuing education providers in the COVID‑19-pandemic

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          Abstract

          Die Corona-Pandemie hat den Handlungskontext der Weiterbildungsanbieter in Deutschland unabhängig von deren Merkmalen und Eigenschaften kurzfristig verändert und diese zu zeitnahen Anpassungen ihres Angebotes gezwungen. Für die Wissenschaft bietet ein solcher exogener Schock die Gelegenheit zu untersuchen, inwiefern es den Weiterbildungsanbietern gelingt, sich einer grundlegend veränderten Situation anzupassen und welche Einrichtungsmerkmale dabei eine Rolle spielen. In dem Beitrag untersuchen wir die theoretisch begründeten Annahmen, dass die Ausgangslage der Weiterbildungsanbieter vor der Corona-Pandemie in Bezug auf die Nutzung digitaler Formate und Medien ein entscheidender Faktor für die kurzfristige Umstellung des Angebotes in der Pandemie war und dass sich insbesondere kommerzielle Anbieter besser an die veränderte Situation anpassen konnten. Auf der Grundlage von wbmonitor-Daten aus 2019 und 2020 testen wir die Hypothesen mit Dose-Response und Difference-in-Differences (DiD) Modellen. Die Ergebnisse deuten auf exogene Faktoren hin, die den Anpassungsleistungen Grenzen setzen.

          Zusatzmaterial online

          Zusätzliche Informationen sind in der Online-Version dieses Artikels (10.1007/s40955-021-00194-3) enthalten.

          Translated abstract

          The COVID‑19-pandemic has changed the context of action of continuing education providers in Germany in the short term, irrespective of their characteristics and attributes, and forced them to make prompt adjustments to their offerings. For researchers, such an exogenous shock offers the opportunity to investigate to what extent continuing education providers succeed in adapting to a fundamentally changed situation and which institution characteristics play a role in this process. In this paper, we examine the theoretically based assumptions that the initial situation of continuing education providers prior to the COVID‑19-pandemic with regard to the use of digital formats and media was a decisive factor for the short-term conversion of offerings during the pandemic and that commercial providers in particular were better able to adapt to the changed situation. Using wbmonitor data from 2019 and 2020, we test the hypotheses with dose-response and difference-in-differences (DiD) models. The results suggest exogenous factors that set limits on adaptation performance.

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          Multiple imputation of missing blood pressure covariates in survival analysis.

          This paper studies a non-response problem in survival analysis where the occurrence of missing data in the risk factor is related to mortality. In a study to determine the influence of blood pressure on survival in the very old (85+ years), blood pressure measurements are missing in about 12.5 per cent of the sample. The available data suggest that the process that created the missing data depends jointly on survival and the unknown blood pressure, thereby distorting the relation of interest. Multiple imputation is used to impute missing blood pressure and then analyse the data under a variety of non-response models. One special modelling problem is treated in detail; the construction of a predictive model for drawing imputations if the number of variables is large. Risk estimates for these data appear robust to even large departures from the simplest non-response model, and are similar to those derived under deletion of the incomplete records.
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            Competition and Innovation: an Inverted-U Relationship

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              Tuning multiple imputation by predictive mean matching and local residual draws

              Background Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor’s residual. Both methods relax some assumptions of parametric imputation, promising greater robustness when the imputation model is misspecified. Methods We review development of PMM and LRD and outline the various forms available, and aim to clarify some choices about how and when they should be used. We compare performance to fully parametric imputation in simulation studies, first when the imputation model is correctly specified and then when it is misspecified. Results In using PMM or LRD we strongly caution against using a single donor, the default value in some implementations, and instead advocate sampling from a pool of around 10 donors. We also clarify which matching metric is best. Among the current MI software there are several poor implementations. Conclusions PMM and LRD may have a role for imputing covariates (i) which are not strongly associated with outcome, and (ii) when the imputation model is thought to be slightly but not grossly misspecified. Researchers should spend efforts on specifying the imputation model correctly, rather than expecting predictive mean matching or local residual draws to do the work.
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                Author and article information

                Contributors
                christ@die-bonn.de
                martin@die-bonn.de
                koscheck@bibb.de
                Journal
                ZfW
                Zeitschrift für Weiterbildungsforschung
                Springer Fachmedien Wiesbaden (Wiesbaden )
                2364-0014
                2364-0022
                22 November 2021
                22 November 2021
                : 1-25
                Affiliations
                [1 ]GRID grid.461675.7, ISNI 0000 0001 1091 3901, Deutsches Institut für Erwachsenenbildung – Leibniz-Zentrum für Lebenslanges Lernen, ; Bonn, Deutschland
                [2 ]GRID grid.432854.c, ISNI 0000 0001 2254 4621, Bundesinstitut für Berufsbildung, ; Bonn, Deutschland
                Author information
                http://orcid.org/0000-0002-8195-5276
                http://orcid.org/0000-0002-4282-4601
                Article
                194
                10.1007/s40955-021-00194-3
                8607067
                beec5053-7017-4be4-a5bc-235b5ef993bc
                © The Author(s) 2021

                Open Access Dieser Artikel wird unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden.

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                History
                : 10 August 2021
                : 21 October 2021
                : 27 October 2021
                Funding
                Funded by: Deutsches Institut für Erwachsenenbildung - Leibniz-Zentrum für Lebenslanges Lernen e.V. (3434)
                Categories
                Originalbeitrag

                corona,digitalisierung,weiterbildung,organisation,erwachsenenbildung,reproduktionskontexte,digitization,continuing education,organization,adult education,reproduction contexts

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