Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Winter, S. D., Sinval, J. & Merkle, E. C. (2023). Comparing priors for estimating sparse ordinal indicators in Bayesian factor analyses. 2023 NCME Annual Meeting.
Exportar Referência (IEEE) 
S. D. Winter et al.,  "Comparing priors for estimating sparse ordinal indicators in Bayesian factor analyses", in 2023 NCME Annu. Meeting, Chicago, IL, 2023
Exportar BibTeX 
@misc{winter2023_1716073445399,
	author = "Winter, S. D. and Sinval, J. and Merkle, E. C.",
	title = "Comparing priors for estimating sparse ordinal indicators in Bayesian factor analyses",
	year = "2023",
	howpublished = "Ambos (impresso e digital)",
	url = "https://www.ncme.org/"
}
Exportar RIS 
TY  - CPAPER
TI  - Comparing priors for estimating sparse ordinal indicators in Bayesian factor analyses
T2  - 2023 NCME Annual Meeting
AU  - Winter, S. D.
AU  - Sinval, J.
AU  - Merkle, E. C.
PY  - 2023
CY  - Chicago, IL
UR  - https://www.ncme.org/
AB  - A common issue in educational measurement is low item endorsement of extreme response options. Modeling such 
sparse data can result in non-convergence, overly optimistic model fit indices, and biased parameter estimates. This 
study examines the potential of the Dirichlet prior distribution to model such data using Bayesian estimation.
ER  -