The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
PROC LCA: A SAS Procedure for Latent Class Analysis
Open accessAbstract
Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Group-Based Modeling of Development
NAGIN, D. (2005) · doi:10.4159/9780674041318 · Cited by 4,164
An entropy criterion for assessing the number of clusters in a mixture model
Celeux, G., & Soromenho, G. (1996) · Journal of Classification · doi:10.1007/bf01246098 · Cited by 2,448 · cited 1×
“The average posterior probabilities can then be used to calculate the odds of correct classification for each class (Nagin, 2005) and the overall entropy (Celeux & Soromenho, 1996).”
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Cited by
Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment
Lanza, S. T., & Rhoades, B. L. (2013) · Prevention Science · doi:10.1007/s11121-011-0201-1 · Cited by 1,255
Self-citationSensitivity and specificity of information criteria
(2019) · doi:10.1093/bib/bbz016 · Cited by 718
Dziak, J. J., Lanza, S. T., & Tan, X. (2014) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2014.919819 · Cited by 441
Self-citationPetersen, K. J., Qualter, P., & Humphrey, N. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01214 · Cited by 195
Roesch, S. C., Villodas, M., & Villodas, F. (2010) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2010.01.003 · Cited by 148
Metadata
- Authors
- Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2007-10-23
- Volume
- 14
- Issue
- 4
- Pages
- 671-694
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2088697515
- Cited by
- 1,070
- References
- 4
- FWCI
- 19.55
- Type
- journal article
- Language
- en
- Open access
- green
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- Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS Procedure for Latent Class Analysis. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705510701575602
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