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Analyzing Distinctive Features

Author

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  • Lawrence J. Hubert
  • Frank B. Baker

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.

Suggested Citation

  • Lawrence J. Hubert & Frank B. Baker, 1977. "Analyzing Distinctive Features," Journal of Educational and Behavioral Statistics, , vol. 2(2), pages 79-98, June.
    • Handle: RePEc:sae:jedbes:v:2:y:1977:i:2:p:79-98
    DOI: 10.3102/10769986002002079
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    Citations

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    Cited by:

    1. Lawrence Hubert, 1979. "Matching models in the analysis of cross-classifications," Psychometrika, Springer;The Psychometric Society, vol. 44(1), pages 21-41, March.
    2. J. Sutcliffe, 1986. "Differential ordering of objects and attributes," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 209-240, June.
    3. Phipps Arabie & J. Carroll, 1980. "Mapclus: A mathematical programming approach to fitting the adclus model," Psychometrika, Springer;The Psychometric Society, vol. 45(2), pages 211-235, June.

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