Bayesian Item Response Modeling: Theory and Applications

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Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data (2016, methodology): “This approach has antecedents in psychometric methods (e.g., Baker and Kim 2004; Fox 2010; Hambleton et al. 1991; Lord 1980), and has been used to aggregate crowd ratings (e.g., Ipeirotis et al. 2014; Welinder et al. 2010; Welinder and Perona 2010; White-hill et al. 2009).”
Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “In contrast to priors on item parameters, priors on person parameters are almost always hierarchical in IRT (e.g., De Boeck et al. 2011; Fox 2010; Levy and Mislevy 2017).”
Recovery of Graded Response Model Parameters (2012, background): “Moreover, extensive information about parameters can be obtained through various summary statistics of the posterior distributions, which can be computed during parameter estimation (see, for example, Fox, 2010).”
Variational inference in nonconjugate models (2012, unknown): “…1 include Bayesian logistic regression (Jaakkola and Jordan, 1997), Bayesian generalized linear models (Wells, 2001), discrete choice models (Braun and McAuliffe, 2010), Bayesian item response models (Clinton et al., 2004; Fox, 2010), and non-conjugate topic models (Blei and Lafferty, 2006, 2007).”