Sources of bias in the Goodman–Kruskal gamma coefficient measure of association: Implications for studies of metacognitive processes.

Michael E. J. Masson · Caren M. Rotello

173 citations0/0 files storedJournal of Experimental Psychology Learning Memory and Cognition

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Psychometric Methodologies and Testing Memory Processes and Influences Reliability and Agreement in Measurement

Abstract

In many cognitive, metacognitive, and perceptual tasks, measurement of performance or prediction accuracy may be influenced by response bias. Signal detection theory provides a means of assessing discrimination accuracy independent of such bias, but its application crucially depends on distributional assumptions. The Goodman-Kruskal gamma coefficient, G, has been proposed as an alternative means of measuring accuracy that is free of distributional assumptions. This measure is widely used with tasks that assess metamemory or metacognition performance. The authors demonstrate that the empirically determined value of G systematically deviates from its actual value under realistic conditions. A distribution-specific variant of G, called G-sub(c), is introduced to show why this bias arises. The findings imply that caution is needed when using G as a measure of accuracy, and alternative measures are recommended.

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References

1962A New Asymmetric Measure of Association for Ordinal Variables 1988Decision rules in the perception and categorization of multidimensional stimuli.1986Indices of discrimination or diagnostic accuracy: Their ROCs and implied models.1986Form of empirical ROCs in discrimination and diagnostic tasks: Implications for theory and measurement of performance.1983Measures of Association 2006The intricate relationships between monitoring and control in metacognition: Lessons for the cause-and-effect relation between subjective experience and behavior.

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