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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Diagnosing a common rater halo effect using the polytomous Rasch model.

Ida Marais¹, David Andrich

¹Graduate School of Education, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia. ida.marais@uwa.edu.au

Journal of Applied Measurement

|February 24, 2012

Summary

A common halo effect among raters, often missed by standard methods, can be detected using specific Rasch analyses. This research identifies a method to diagnose this rater bias, improving assessment accuracy.

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Area of Science:

Psychometrics
Educational Measurement
Psychological Assessment

Background:

The halo effect, a cognitive bias, can influence rater judgments in assessments.
Distinguishing between rater-unique and rater-common halo effects is crucial for accurate measurement.
Standard Rasch model fit indices may not detect halo effects common to all raters.

Purpose of the Study:

To investigate methods for detecting a halo effect common to all raters.
To propose a simulation-based approach using Rasch modeling to identify rater bias.
To differentiate between rater-specific and general halo effects in assessment data.

Main Methods:

Simulating a halo effect as a violation of local independence within the Rasch model framework.
Comparing two-facet Rasch analyses: 'stack' (clustered thresholds, multimodal persons) and 'rack' (aligned thresholds, unimodal persons).
Utilizing contrasts between stack and rack analyses to diagnose common halo effects.

Main Results:

A halo effect common to all raters can be diagnosed by contrasting Rasch analyses.
The 'stack' analysis reveals clustered thresholds and a multimodal person distribution when common halo is present.
The 'rack' analysis, in contrast, shows aligned thresholds and a unimodal person distribution, indicating the absence of common halo.

Conclusions:

A halo effect common to all raters is diagnosable through specific Rasch modeling techniques.
Contrasting Rasch analyses provides a method to identify and account for pervasive rater bias.
Accurate assessment requires addressing halo effects that are common across all raters.