Data generation methods across the empirical sciences: differences in the study phenomena’s accessibility and the processes of data encoding

Jana Uher ()
Additional contact information
Jana Uher: University of Greenwich

Quality & Quantity: International Journal of Methodology, 2019, vol. 53, issue 1, No 11, 246 pages

Abstract: Abstract Data generation methods differ across the empirical sciences. Today’s physicists and engineers primarily generate data with automated technologies. Behavioural, psychological and social scientists explore phenomena that are not technically accessible (e.g., attitudes, social beliefs) or only in limited ways (e.g., behaviours) and therefore generate data primarily with persons. But human abilities are involved in any data generation, even when technologies are used and developed. This article explores concepts and methods of data generation of different sciences from transdisciplinary and philosophy-of-science perspectives. It highlights that empirical data can reveal information about the phenomena under study only if relevant properties of these phenomena have been encoded systematically in the data. Metatheoretical concepts and methodological principles are elaborated that open up new perspectives on methods of data generation across the empirical sciences, highlighting commonalities and differences in two pivotal points: (1) in the accessibility that various kinds of phenomena have for the persons generating the data and for the researchers, and (2), as a consequence thereof, in the processes involved in the encoding of information from these phenomena in the signs (symbols) used as data. These concepts and principles cut across establish method categorisations (e.g., human-generated versus instrument-generated data; quantitative versus qualitative methods), highlighting fundamental issues equally important in all sciences as well as essential differences. They also provide novel lines of argumentation that substantiate psychologists’ and social scientists’ increasing criticism of their own disciplines’ focus on standardised assessment methods and establish connections to concepts of data generation developed in metrology.

Keywords: Assessment method; Data generation; Experience sampling; Human-based measurement; Introspection; Measurement with persons; Metrology; Observation; Qualitative–quantitative methods (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11135-018-0744-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:53:y:2019:i:1:d:10.1007_s11135-018-0744-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-018-0744-3

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().