A Test Can Have Multiple Reliabilities

Jules L. Ellis ()
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Jules L. Ellis: Radboud University Nijmegen

Psychometrika, 2021, vol. 86, issue 4, No 3, 869-876

Abstract: Abstract It is argued that the generalizability theory interpretation of coefficient alpha is important. In this interpretation, alpha is a slightly biased but consistent estimate for the coefficient of generalizability in a subjects x items design where both subjects and items are randomly sampled. This interpretation is based on the “domain sampling” true scores. It is argued that these true scores have a more solid empirical basis than the true scores of Lord and Novick (1968), which are based on “stochastic subjects” (Holland, 1990), while only a single observation is available for each within-subject distribution. Therefore, the generalizability interpretation of coefficient alpha is to be preferred, unless the true scores can be defined by a latent variable model that has undisputed empirical validity for the test and that is sufficiently restrictive to entail a consistent estimate of the reliability—as, for example, McDonald’s omega. If this model implies that the items are essentially tau-equivalent, both the generalizability and the reliability interpretation of alpha can be defensible.

Keywords: true score; stochastic subject; domain sampling; latent variable; generalizability; reliability; indeterminacy (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11336-021-09800-2

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