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Cited by 417

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Estimating Multiple Classification Latent Class Models

Maris, E.·1999·Psychometrika
DOI 10.1007/bf02294535
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Abstract

This paper presents a new class of models for persons-by-items data. The essential new feature of this class is the representation of the persons: every person is represented by its membership to multiple latent classes, each of which belongs to one latent classification. The models can be considered as a formalization of the hypothesis that the responses come about in a process that involves the application of a number of mental operations. Two algorithms for maximum likelihood (ML) and maximum a posteriori (MAP) estimation are described. They both make use of the tractability of the complete data likelihood to maximize the observed data likelihood. Properties of the MAP estimators (i.e., uniqueness and goodness-of-recovery) and the existence of asymptotic standard errors were examined in a simulation study. Then, one of these models is applied to the responses to a set of fraction addition problems. Finally, the models are compared to some related models in the literature.

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Metadata

Authors
Maris, E.
Venue
Psychometrika
Publisher
Cambridge University Press (CUP)
Published
1999-06
Volume
64
Issue
2
Pages
187-212
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
DOI
10.1007/bf02294535
OpenAlex
W2054855387
Cited by
417
References
32
FWCI
2.35
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.