Statistics is not measurement: The inbuilt semantics of psychometric scales and language-based models obscures crucial epistemic differences
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Summary
This summary is machine-generated.Psychology overemphasizes statistical models, neglecting proper measurement. This leads to pragmatic quantification lacking true understanding of phenomena, hindering scientific progress and perpetuating a crisis in the field.
Area Of Science
- Psychology
- Philosophy of Science
- Measurement Science
Background
- Psychology heavily relies on statistical modeling, often overlooking the foundational principles of epistemologically sound measurement.
- A critical distinction exists between statistical analysis of structural relations and measurement, which establishes empirical links between phenomena and data.
- Existing measurement models, like Rosen's, provide frameworks for understanding these relations, crucial for scientific rigor.
Purpose Of The Study
- To critique psychology's overreliance on statistical modeling at the expense of epistemologically grounded measurement.
- To elaborate on the epistemic challenges psychology faces in establishing genuine measurement analogues.
- To propose a path towards epistemically justified inferences in psychological research.
Main Methods
- Critique of current statistical practices in psychology.
- Application of Rosen's general model of measurement to psychological research.
- Analysis of metrological approaches to epistemic circularity (coordination, calibration).
- Examination of challenges posed by psychological phenomena (complexity, non-ergodicity) and language-based methods.
- Exploration of artificial intelligence (AI) for natural language analysis.
Main Results
- Psychometrics fails to establish coordinated and calibrated measurement relations, yielding only pragmatic quantifications.
- Psychologists often conflate method semantics with phenomena, blurring ontological and epistemological distinctions.
- Statistics cannot establish or analyze a model's relation to the phenomena studied.
- AI-driven natural language processing (NLP) offers potential for analyzing unrestricted verbal responses.
Conclusions
- Psychology's current methods lead to an epistemic gap, hindering genuine understanding and perpetuating a 'cardinal error'.
- Epistemically justified inferences require methods that bridge the gap between formal models and empirical phenomena.
- While AI offers new possibilities, its misuse for generating standardized descriptions may exacerbate existing problems.
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