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Welche Veränderungen fallen Menschen in der deutschen Sprache auf? Sprache in Zahlen: Folge 11
(2023)
Das Leibniz-Institut für Deutsche Sprache (IDS) führt seit den 1990er Jahren regelmäßig Repräsentativerhebungen zu sprachlichen Fragen durch. Über die letzten Umfragen, die Deutschland-Erhebung 2017 und die Erhebung Dialekt und Beruf 2019, wurde bereits in dieser Reihe berichtet. Informationen über die Deutschland-Erhebung 2017 finden sich in Folge 1 bis 6 dieser Reihe. In den Folgen 7 bis 9 wurden Ergebnisse der Erhebung Dialekt und Beruf 2019 vorgestellt. Im Winter 2022 hat das IDS eine neue Repräsentativumfrage durchgeführt: die Deutschland-Erhebung 2022. Darin wurden Einstellungen zum Deutschen und anderen Sprachen sowie die Wahrnehmung von sprachlichen Veränderungen erfasst. In dieser Folge 10 werden die Erhebung und erste Ergebnisse vorgestellt
Sprachstatistischer Rahmen
(1979)
Unterschiede bei Dialektübersetzungen in Abhängigkeit von schriftlichen und mündlichen Stimuli
(2016)
When collecting linguistic data using translation tasks, stimuli can be presented in written or in oral form. In doing so, there is a possibility that a systematic source of error can occur that can be traced back to the selected survey method and which can influence the results of the translation tasks. This contribution investigates whether and to what extent both of the aforementioned survey methods result in divergent results when using translation tasks. For this investigation, 128 informants provided linguistic data; each informant had to translate 25 Wenker sentences from Standard German into either East Swabian, Lechrain or West Central Bavarian dialect, as the case may be. The results show two tendencies. First, written stimuli lead to a slightly higher number of dialectal translation in segmental variables. Second, when oral stimuli are used, syntactic and lexical variables are translated significantly more often in such a manner that they diverge from the template. The results can be explained in terms of varying cognitive processing operations and the constraints of human working memory. When collecting data in the future, these tendencies should be taken into account.
When collecting linguistic data using translation tasks, stimuli can be presented in written or in oral form. In doing so, there is a possibility that a systematic source of error can occur that can be traced back to the selected survey method and which can influence the results of the translation tasks. This contribution investigates whether and to what extent both of the aforementioned survey methods result in divergent results when using translation tasks. For this investigation, 128 informants provided linguistic data; each informant had to translate 25 Wenker sentences from Standard German into either East Swabian, Lechrain or West Central Bavarian dialect, as the case may be. The results show two tendencies. First, written stimuli lead to a slightly higher number of dialectal translation in segmental variables. Second, when oral stimuli are used, syntactic and lexical variables are translated significantly more often in such a manner that they diverge from the template. The results can be explained in terms of varying cognitive processing operations and the constraints of human working memory. When collecting data in the future, these tendencies should be taken into account.
Content analysis provides a useful and multifaceted, methodological framework for Twitter analysis. CAQDAS tools support the structuring of textual data by enabling categorising and coding. Depending on the research objective, it may be appropriate to choose a mixed-methods approach that combines quantitative and qualitative elements of analysis and plays out their respective advantages to the greatest possible extent while minimising their shortcomings. In this chapter, we will discuss CAQDAS speech act analysis of tweets as an example of software-assisted content analysis. We start with some elementary thoughts on the challenges of the collection and evaluation of Twitter data before we give a brief description of the potentials and limitations of using the software QDA Miner (as one typical example for possible analysis programmes). Our focus will lie on analytical features that can be particularly helpful in speech act analysis of tweets.
Data and transcription
(2008)