German ASA Questionnaire Translation - Part 1: Translation and Formative Assessment

doi: 10.4121/1975af9a-9b58-4dde-ae58-58e1001ef553.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/1975af9a-9b58-4dde-ae58-58e1001ef553
Datacite citation style:
Khodakov, Boleslav; Bokel, Emma; Albers, Nele; Bönsch, Andrea; Ehret, Jonathan et. al. (2023): German ASA Questionnaire Translation - Part 1: Translation and Formative Assessment. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/1975af9a-9b58-4dde-ae58-58e1001ef553.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
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time coverage
18 May 2023 - 4 June 2023
licence
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This is the data and analysis code for the formative assessment of the German translation of the Artificial Social Agent (ASA) Questionnaire. This questionnaire can be used to characterize interactions between humans and ASAs.


The original English questionnaire can be found with this DOI: 10.4121/19650846.


Studies

The formative assessment consists of 3 rounds of assessing the correlation between original English questionnaire items and their German translations. English items with too low correlation scores in a round are retranslated and re-assessed in the subsequent round. The assessment consists of people who indicate having German as their first and primary language, being fluent in English, and being bilingual rating the English items and their German translations for the human-ASA interaction depicted in a 30-second video clip. The studies were run on the online crowdsourcing platform Prolific.


The studies have been pre-registered here: https://osf.io/adknw.


Data

For each round, we provide the ratings for the English and German questionnaire items as well as data on the characteristics of the participants (age, gender, highest completed education level from their Prolific profiles). A random identifier called "rand_id" can be used to link the data files.


In case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).

history
  • 2023-06-26 first online, published, posted
publisher
4TU.ResearchData
format
.docx, .md, .pdf, .txt, .sav, .csv, .tex, .bib, .zip, .py, .rmd
funding
  • This work is part of the multidisciplinary research project Perfect Fit, which is supported by several funders organized by the Netherlands Organization for Scientific Research (NWO), program Commit2Data - Big Data & Health (project number 628.011.211). Besides NWO, the funders include the Netherlands Organisation for Health Research and Development (ZonMw), Hartstichting, the Ministry of Health, Welfare and Sport (VWS), Health Holland, and the Netherlands eScience Center.
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Interactive Intelligence

DATA

files (1)