Applied Measurement Overview

Principles

Definition

Applied measurement is a research field and methodological approach concerned with the practical application, validation, and evaluation of measurement principles and techniques within specific disciplinary or real-world contexts. It focuses on ensuring the reliability, validity, and utility of measurement instruments and procedures for purposes such as assessment, evaluation, and decision-making.

Ontological type

Validity and Reliability

Instrument Development

Core Methods

Key developments

Key Figures

Instrumental Standardization and Automation era

M. G. Mellon [1] was active at Purdue University West Lafayette [3] during the Instrumental Standardization and Automation era. His work on Analytical absorption spectroscopy [6] exemplifies key contributions of the era by translating spectroscopic outputs into standardized, instrument-driven measurements, thereby reducing operator variance and enabling lab-to-lab comparability. James J. Lingane [2] was affiliated with Harvard University [4] and the University of California, Berkeley [5] during this era. His contributions include the Cell and dropping Electrode for polarographic analysis [7] and the Multipurpose Electroanalytical Servo Instrument [8], which advanced automation and sensitivity and helped translate electrochemical measurements into reproducible process metrics.

Reliability and Validity Foundations era

Elazar J. Pedhazur [1] was associated with Columbia University [3] and New York University [4] during the Reliability and Validity Foundations era. Pedhazur [1] contributed to measurement design and analysis, as exemplified in Measurement, Design, and Analysis: An Integrated Approach [7], which reframed score interpretation around mastery and decision consequences, aligning measurement practice with the era's emphasis on reliability and validity as decision-focused concepts. Samuel Messick [2] was affiliated with University of Pennsylvania [5] and Princeton University [6] in this period. Messick [2] advanced a unified view of validity and evidence in The Interplay of Evidence and Consequences in the Validation of Performance Assessments [8], underscoring how the interplay between evidence and consequences supports reproducibility and score interpretation across laboratories and assessment settings in this era.

Cross-Context Invariance Paradigm era

Marko Sarstedt [1] is associated with the University of California, Berkeley [3] and Ludwig-Maximilians-Universität München [4] during this era. In this era, his key contribution lies in establishing practical guidelines for when to use PLS-SEM and how to report its results, as detailed in the 2018 paper 'When to use and how to report the results of PLS-SEM' [7], a standard-setting move that improved methodological clarity and cross-context comparability. Christian M. Ringle [2] is affiliated with Radboud University Nijmegen [5] and James Cook University [6] in this period. Similarly, his work on PLS-SEM reporting guidelines in the same 2018 paper 'When to use and how to report the results of PLS-SEM' [7] contributed to promoting consistent measurement modeling and cross-context invariance, supporting the era's standards.