Analyzing Distinctive Features

Lawrence J. Hubert and Frank B. Baker

Journal of Educational and Behavioral Statistics, 1977, vol. 2, issue 2, 79-98

Abstract: A statistical technique is proposed for comparing an empirically obtained confusion matrix against a set of distinctive features that supposedly characterize the stimuli on which the given confusion matrix is based. Each distinctive feature corresponds to a partition of the stimulus set, and the term “confusion matrix†refers to the measures of “closeness†collected on all pairs of stimuli. The suggested paradigm can be considered an analysis-of-variance generalization and is dependent on a randomization strategy for evaluating the size of a goodness-of-fit index calculated between the given confusion matrix and a single partition. An example of the inference scheme is carried out on a data set dealing with the 26 Roman capital letters; in addition, an exploratory strategy is illustrated that tries to locate “good†partitionings of a stimulus set in a post-hoc manner.

Keywords: Randomization Test; Distinctive Feature; Nonparametric Test; Confusion Matrix (search for similar items in EconPapers)
Date: 1977
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:2:y:1977:i:2:p:79-98

DOI: 10.3102/10769986002002079

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