Extending a Matrix Lie Group Model of Measurement Symmetries

TL;DR

This paper extends a matrix Lie group measurement model to identify additional measurement symmetries, demonstrating their importance for invariance and comparability in measurement and meta-analysis.

Contribution

It introduces an extended Lie group framework for measurement symmetries, linking continuous symmetries to invariance in score distributions and meta-analytic measures.

Findings

Symmetry breaks affect score distribution invariance.

Standardized mean difference invariance depends on specific symmetry conditions.

Lie group symmetries can improve measurement validity and comparability.

Abstract

Symmetry principles underlie and guide scientific theory and research, from Curie's invariance formulation to modern applications across physics, chemistry, and mathematics. Building on a recent matrix Lie group measurement model, this paper extends the framework to identify additional measurement symmetries implied by Lie group theory. Lie groups provide the mathematics of continuous symmetries, while Lie algebras serve as their infinitesimal generators. Within applied measurement theory, the preservation of symmetries in transformation groups acting on score frequency distributions ensure invariance in transformed distributions, with implications for validity, comparability, and conservation of information. A simulation study demonstrates how breaks in measurement symmetry affect score distribution symmetry and break effect size comparability. Practical applications are considered, particularly in meta analysis, where the standardized mean difference (SMD) is shown to remain invariant across measures only under specific symmetry conditions derived from the Lie group model. These results underscore symmetry as a unifying principle in measurement theory and its role in evidence based research.