ST-LE-2023-09
Curriculum: Starter TrackAbout the meaningfulness of data
Motivation
Data are not, as etymology suggests, „the given“, but they
are generated, constructed, or made
(sometimes in the bad understanding of the wording). Therefore, we need to shed
some light onto the hidden presuppositions in our scientific agenda. For a
start, let’s assume that there is no meaning in the data per se, but that
meaning happens to data, it is
attached to it. In fact, YOU attach it, and therefore you must assume liability
for establishing a referential link between the data themselves and the
phenomenon that your data are supposed to capture and whose epistemological
status (“I got it, you see”) and ontological status (“It exists, I mean, like
‘really’ there”) might not be the same. You may find that technological
sophistication and programming skills might not be enough to this end. In order
to identify how your scientific attitudes and your decision making as a
researcher along the rocky road of empirical research adds, withdraws, or
alters the meaningfulness of data, we will span a wide range from
epistemological paradigms down to specific choices of statistical models in
handling missing data, bridged by measurement theory and its map of pathways
from the (in-)tangible world to numbers. Welcome to the vast realm of
philosophy of science.
Learning contents
- Notions of meaning, data, and information
- Epistemology and ontology: how data refer to what is being measured
- The ideal research process: are data decisive ? A menu of paradigms
- Data and theory. Realism – Anti-realism – Pragmatism. Models. Truth.
- Introduction to measurement theory: do you abide by the rules?
- Fuzziness, vagueness, uncertainty, incompleteness: “bad” data?
- Missing data: how your philosophical stance indeed impacts study results
Learning outcomes
Since, as a psychologist and statistician, I cannot
claim expertise in your respective field of work, I will not, and cannot, tell
you how to “do it right”. But the patterns behind „doing it wrong“ are quite
universal: unawareness and intransparency. My aim is to make some of the
implicit explicit, and foster a critical mindset when it comes to relating data
to meaning in your specific discipline.
Prior knowledge
None. Just be nosy and open-minded.
Further reading
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Motivation
Data are not, as etymology suggests, „the given“, but they are generated, constructed, or made (sometimes in the bad understanding of the wording). Therefore, we need to shed some light onto the hidden presuppositions in our scientific agenda. For a start, let’s assume that there is no meaning in the data per se, but that meaning happens to data, it is attached to it. In fact, YOU attach it, and therefore you must assume liability for establishing a referential link between the data themselves and the phenomenon that your data are supposed to capture and whose epistemological status (“I got it, you see”) and ontological status (“It exists, I mean, like ‘really’ there”) might not be the same. You may find that technological sophistication and programming skills might not be enough to this end. In order to identify how your scientific attitudes and your decision making as a researcher along the rocky road of empirical research adds, withdraws, or alters the meaningfulness of data, we will span a wide range from epistemological paradigms down to specific choices of statistical models in handling missing data, bridged by measurement theory and its map of pathways from the (in-)tangible world to numbers. Welcome to the vast realm of philosophy of science.
Learning contents
- Notions of meaning, data, and information
- Epistemology and ontology: how data refer to what is being measured
- The ideal research process: are data decisive ? A menu of paradigms
- Data and theory. Realism – Anti-realism – Pragmatism. Models. Truth.
- Introduction to measurement theory: do you abide by the rules?
- Fuzziness, vagueness, uncertainty, incompleteness: “bad” data?
- Missing data: how your philosophical stance indeed impacts study results
Learning outcomes
Since, as a psychologist and statistician, I cannot claim expertise in your respective field of work, I will not, and cannot, tell you how to “do it right”. But the patterns behind „doing it wrong“ are quite universal: unawareness and intransparency. My aim is to make some of the implicit explicit, and foster a critical mindset when it comes to relating data to meaning in your specific discipline.
Prior knowledge
None. Just be nosy and open-minded.
Further reading
---
When?
February 23, 2023, 9:30 AM - 11:30 AM
Where?
Online via Zoom
Register until:
February 20, 2023
Waldmann, Prof. Dr. Hans-Christian
Professor of Theoretical Psychology & Psychometrics
Department of Psychology, University of Bremen
Cooperating in U Bremen Research Alliance:
