Biased or Unbiased Measures?

What is a "correct" measure in the presence of disturbing influences (biases)? The answer depends on the goal of the researcher. If the goal is to create reproducible measures, measures which are the same from one testing situation to another, the "correct" measure is the unbiased one - so long as all measures are subject to the same iterative process of removing biases and misfits. If the goal is to describe the outcome of a particular testing situation, the biased measure is more reflective of what actually happened.

This was crucial during a recent analysis of data compression methods for audio samples. In the Figure below, the "biased" measure for each data compression "Codec" is the measure produced by the complete data. The "unbiased" measure is produced by omitting idiosyncratic portions of the data. Notice that the measure for Codec 4 changes substantially relative to the others depending on whether biased data are included. Which is the "correct" measure of Codec 4?

Approximately 75% of the drop in Codec 4 is due to an interaction with one audio sample featuring percussion. It performed consistently on the other samples. This identified for removal a technical error that caused the percussion interaction. Thus, even though the observed data produce the "biased" measure, the "correct" measure for future development is the "unbiased" one!

Mark Moulton

Biased or unbiased measures.Moulton M. … Rasch Measurement Transactions, 1998, 12:3 p. 649.

Rasch Publications
Rasch Measurement Transactions (free, online) Rasch Measurement research papers (free, online) Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Applying the Rasch Model 3rd. Ed., Bond & Fox Best Test Design, Wright & Stone
Rating Scale Analysis, Wright & Masters Introduction to Rasch Measurement, E. Smith & R. Smith Introduction to Many-Facet Rasch Measurement, Thomas Eckes Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. Statistical Analyses for Language Testers, Rita Green
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Journal of Applied Measurement Rasch models for measurement, David Andrich Constructing Measures, Mark Wilson Rasch Analysis in the Human Sciences, Boone, Stave, Yale
in Spanish: Análisis de Rasch para todos, Agustín Tristán Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez

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