The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Exploratory latent structure analysis using both identifiable and unidentifiable models
Abstract
This paper considers a wide class of latent structure models. These models can serve as possible explanations of the observed relationships among a set of m manifest polytomous variables. The class of models considered here includes both models in which the parameters are identifiable and also models in which the parameters are not. For each of the models considered here, a relatively simple method is presented for calculating the maximum likelihood estimate of the frequencies in the m-way contingency table expected under the model, and for determining whether the parameters in the estimated model are identifiable. In addition, methods are presented for testing whether the model fits the observed data, and for replacing unidentifiable models that fit by identifiable models that fit. Some illustrative applications to data are also included.
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Cites
Mayer, T. F., Lazarsfeld, P. F., & Henry, N. W. (1969) · American Sociological Review · doi:10.2307/2092222 · Cited by 2,384
Introduction to Mathematical Sociology.
Kish, L., & Coleman, J. S. (1965) · Social Forces · doi:10.2307/2575636 · Cited by 1,258
Goodman, L. A. (1974) · American Journal of Sociology · doi:10.1086/225676 · Cited by 502
Self-citationGoodman, L. A. (1968) · Journal of the American Statistical Association · doi:10.1080/01621459.1968.10480916 · Cited by 465
Self-citationIntroduction to Mathematical Sociology
Coleman, J. S. (1968) · CERN Document Server (European Organization for Nuclear Research) · Cited by 377
Goodman, L. A. (1973) · Biometrika · doi:10.1093/biomet/60.1.179 · Cited by 241
Self-citationStouffer, S. A., & Toby, J. (1951) · American Journal of Sociology · doi:10.1086/220785 · Cited by 137
Log-Linear Models for Frequency Tables Derived by Indirect Observation: Maximum Likelihood Equations
Haberman, S. J. (1974) · The Annals of Statistics · doi:10.1214/aos/1176342813 · Cited by 83
Cited by
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Representing and Testing Organizational Theories: A Holistic Construal
Bagozzi, R. P., & Phillips, L. W. (1982) · Administrative Science Quarterly · doi:10.2307/2392322 · Cited by 2,064
poLCA: An R Package for Polytomous Variable Latent Class Analysis
Linzer, D. A., & Lewis, J. B. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i10 · Cited by 1,636
Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm
Dawid, A. P., & Skene, A. M. (1979) · Applied Statistics · doi:10.2307/2346806 · Cited by 1,623
The theoretical status of latent variables.
Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003) · Psychological Review · doi:10.1037/0033-295x.110.2.203 · Cited by 1,566
Metadata
- Authors
- GOODMAN, L. A.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1974
- Volume
- 61
- Issue
- 2
- Pages
- 215-231
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2115881827
- Cited by
- 1,637
- References
- 8
- FWCI
- 4.96
- 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).
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