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Winter, S. D., Sinval, J. & Merkle, E. C. (2023). Comparing priors for estimating sparse ordinal indicators in Bayesian factor analyses. 2023 NCME Annual Meeting.
S. D. Winter et al., "Comparing priors for estimating sparse ordinal indicators in Bayesian factor analyses", in 2023 NCME Annu. Meeting, Chicago, IL, 2023
@misc{winter2023_1728292820557, 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/" }
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 -