Bayesian analysis of longitudinal data using growth curve models

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A Simulation Study Comparison of Bayesian Estimation With Conventional Methods for Estimating Unknown Change Points (2008, background): “However, it has been shown that correctly specified informative priors can make the parameter estimates more accurate and effective ( Ibrahim & Chen, 2000 ; Zhang, Hamagami, Wang, Grimm, & Nesselroade, 2007 ).”
Comparison of Inverse Wishart and Separation-Strategy Priors for Bayesian Estimation of Covariance Parameter Matrix in Growth Curve Analysis (2016, methodology): “To reduce the influence of priors, researchers often prefer noninformative priors even though Bayesian methods allow the incorporation of prior information (e.g., Zhang et al., 2007).”
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research (2013, unknown): “Of speci ﬁ c concern to substantive researchers, the Bayesian paradigm offers a very different view of hypothesis testing (e.g., Kaplan & Depaoli, 2012, 2013; Walker, Gustafson, & Frimer, 2007; Zhang, Hamagami, Wang, Grimm, & Nesselroade, 2007).”
Moderated Mediation Analysis Using Bayesian Methods (2015, unknown): “regression parameter estimates even more accurate and precise (e.g., Ibrahim & Chen, 2000; Yuan & MacKinnon, 2009; Zhang et al., 2007).”
Bayesian analyses : where to start and what to report (2014, background): “Many papers have shown the benefits of Bayesian statistics in the context of small data set (e.g., Zhang et al. , 2007).”