The Greatest Lower Bound to the Reliability of a Test and the Hypothesis of Unidimensionality

Bibliographic Data

ID23359205
AuthorsJos M F Ten Berge (University of Groningen, corresponding author), Gregor Sočan (0000-0002-8481-5199, University of Groningen)
Year2004
Volume69
Issue4
Pages613-625
Publication date2004-12-01
Open AccessYes
TypeARTICLE
VenuePsychometrika (JOURNAL)
Journal identifiersISSN: 0033-3123 • E-ISSN: 1860-0980
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1007/bf02289858
OpenAlexW2139593703
LanguageEN
Citations received68
References cited30

To assess the reliability of congeneric tests, specifically designed reliability measures have been proposed. This paper emphasizes that such measures rely on a unidimensionality hypothesis, which can neither be confirmed nor rejected when there are only three test parts, and will invariably be rejected when there are more than three test parts. Jackson and Agunwamba's (1977) greatest lower bound to reliability is proposed instead. Although this bound has a reputation for overestimating the population value when the sample size is small, this is no reason to prefer the unidimensionality-based reliability. Firstly, the sampling bias problem of the glb does not play a role when the number of test parts is small, as is often the case with congeneric measures. Secondly, glb and unidimensionality based reliability are often equal when there are three test parts, and when there are more test parts, their numerical values are still very similar. To the extent that the bias problem of the greatest lower bound does play a role, unidimensionality-based reliability is equally affected. Although unidimensionality and reliability are often thought of as unrelated, this paper shows that, from at least two perspectives, they act as antagonistic concepts. A measure, based on the same framework that led to the greatest lower bound, is discussed for assessing how close is a set of variables to unidimensionality. It is the percentage of common variance that can be explained by a single factor. An empirical example is given to demonstrate the main points of the paper.

Econometrics · Population · Reliability (semiconductor) · Sample (material) · Statistics · Test (biology) · Upper and lower bounds · Variance (accounting) · Advanced Statistical Methods and Models · Demography · Mathematics · Psychology · Social Psychology · Statistical Distribution Estimation and Applications · Statistical Methods and Bayesian Inference

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