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Related Experiment Video

Updated: Jan 25, 2026

Assays for Validating Histone Acetyltransferase Inhibitors

Assays for Validating Histone Acetyltransferase Inhibitors

Published on: August 6, 2020

Rethinking psychological measurement: Validity potential versus realised validity.

Wendy C Higgins¹, David M Kaplan², Alexander J Gillett³

¹University of Melbourne, Melbourne School of Psychological Sciences, VIC, 3010, Australia; Macquarie University, School of Psychological Sciences, NSW, 2109, Australia.

Studies in History and Philosophy of Science

|January 23, 2026

Summary

This summary is machine-generated.

This study introduces a new concept of measurement validity for psychological sciences. It emphasizes causal relationships and distinguishes between a measurement procedure's potential and its realized validity, improving validation practices.

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Area of Science:

Psychological Sciences
Measurement Theory
Causal Inference

Background:

Current measurement validation guidelines are complex, linking validity to interpretations of test scores.
Existing practices may not clearly delineate requirements for valid measurement.
A need exists for a more precise and interpretable framework for validity claims.

Purpose of the Study:

To propose a novel, measurement-specific, and causal concept of validity.
To enhance measurement validation practices in the psychological sciences.
To clarify the requirements for valid measurement and the interpretation of validity claims.

Main Methods:

Conceptual development based on Borsboom and colleagues' work.
Distinction between validity potential (in abstracto) and realized validity (concrete outcomes).
Application of interventionist theory of causation and causal inference for evaluation.

Main Results:

A measurement-specific and causal concept of validity is proposed.
The concept clarifies requirements for valid measurement and interpretation of claims.
Distinction between potential and realized validity offers practical benefits for research.

Conclusions:

The proposed concept offers a clearer, causal framework for measurement validation.
It facilitates theoretical refinement, procedure selection, and reporting of validity evidence.
This approach addresses challenges in evaluating psychological measurements as causal inferences.